International Seminar on Mathematics in Industry

International Seminar on Mathematics in Industry

CONTENTS

IDABSTRACT TITLE

PAGE

Plenary: Essential Mathematical Contributions To The Digital Economy. 1Keynote 1: STEM Harnesses 4th Industrial Revolution (4IR) Challenges &Opportunities: Academic & Industry Perspectives. 2Keynote 2: In Bayesian: All About Hierarchical Modelling. 3

005 Parameter Estimation for a Model of Ionizing Radiation Effects on Cells inDifferent Cell Cycle Phase. 4

006 Two–Dimensional Signal Transduction during the Formation of Invadopodia. 5007 Identifying Risk Factors for Female Cardiovascular Disease Patients in Malaysia:

A Bayesian Approach. 6008 Skyrme–Hartree–Fock Approach for Descriptions of Static Nuclear Properties of

Well–deformed Nuclei. 8009 Uncertainties in Static Nuclear Properties Due to Pairing Fit Procedures within

Skyrme–Hartree–Fock Approach. 9010 Stochastic Mortality Model in a State-Space Framework. 10011 Construction of Dependence Model for Rainfall Stations by Joining Time Series

Models with Copula Method. 11012 Modelling the Asthma Disease Behaviour by Count Analysis Approach: Poisson

INGARCH and Negative Binomial INGARCH. 12013 Understanding the Pattern of Wind Direction in Malaysia. 13014 Fast and Robust Parameter Estimation in the Application of Fuzzy Logistic

Equations in Population Growth. 15015 Multiscale Boundary Element Method for Acoustic Wave Model. 16016 An Outlier Detection Method for Circular Data Using Covratio Statistics. 17018 Comrade Matrix Methods in Computing the GCD of Two Polynomials. 18019 Mixed Convection Boundary Layer Flow of Viscoelastic Nanofluid past a Sphere

with Constant Wall Temperature. 19020 Thermal Radiation Effects on Magnetohydrodynamics Dusty Non–Newtonian

Flow Past an Exponentially Stretching Sheet. 20021 Modelling Monthly Rainfall in Peninsular Malaysia using Tweedie Distribution. 21022 On Estimates of Malaysian Mortality Rates: A Numerical Approach. 22023 Hartmann and Reynolds Number Effects into the Newtonian Blood Flow

of a Bifurcated Artery with an Overlapping Stenosis. 23025 Tumour–Immune Interaction Model with Cell Cycle Effects including G0 Phase. 24029 A Hybrid Wavelet-ARIMA Model for SPI Drought Forecasting. 25

STEM Harness 4IR Challenges and Opportunities | iii

International Conference on Theoretical and Applied Statistics

030 Scenario Based Two-Stage Stochastic Programming Approach for theMidterm Production Planning of Oil Refinery. 26

031 Single-machine-based Integrated Production Preventive Maintenance Scheduling:A Simheuristic Approach. 27

032 The Evaluation Hybrid of ARIMA, Combined Forecast and GARCH Model inForecasting Tax Revenue in Nigeria. 28

034 Spatial Bayesian Model Averaging to Calibrate Short–Range Weather Forecastin Jakarta, Indonesia. 29

035 Calibrating Weather Forecasting in Indonesia: The Geostatistical OutputPerturbation Method. 30

036 Logistic Regression Ensemble (Lorens) Applied to Drug Discovery. 32037 Ensemble Support Vector Machine by Random Undersampling For DNA

Microarray Classification to Overcome Multiclass Imbalanced Data. 33038 Particle Swarm Optimization for Obtaining the Weights of Neural Network. 34039 Scholarship Selection Using a Hybrid Fuzzy TOPSIS. 35042 Parameter Estimation in Replicated Linear Functional Relationship Model in

the Presence Of Outliers. 36043 The Role of Transportation Infrastructure in Central Java Indonesia Regional

Economic Growth: A Spatial Durbin Model Approach. 37044 Analysis of HIV-1 Infection of CD4+ T Cells with Combined RTIs and PIs Therapy. 38046 Optimal Control Strategy through RTI and PI therapy for HIV-1 Infection in CD4+

T Cells. 39047 Rainfall Forecast with Best and Full Members of North American Multi Model

Ensemble (NMME). 40048 VARX and GSTARX Models for Forecasting Currency Inflow and Outflow with

Multiple Calendar Variations Effect. 41049 Bidimensional Discrete–Time Risk Models based on Bivariate Negative Binomial

Moving Average Models. 42050 Performance Analysis of M/C3/1 Queues: An LPC Approach. 43052 Bayesian Approach on Mixture Poisson Model for Analysing Spatial Point Pattern

of Primary Health Centres in Surabaya. 44053 Multivariate T 2 Control Chart based on James-Stain and Successive Difference

Covariance Matrix Estimators for Intrusion Detection. 45056 Boosting-Ensemble Support Vector Machine: Alternative Classification Algorithm

for Handling High Dimensional and Imbalanced in Multiclass Microarray Data. 46057 Multivariate CUSUM Control Chart Based on The Residual of Multioutput Least

Square SVR for Monitoring Water Quality. 47059 Multiscale Finite Difference on Real Space Ballistic Carbon Nanotubes Field–Effect

Transistor Water Quality. 48061 Gaussian Mixture Model and K–means for Clustering Heavy Precipitation in

Surabaya. 49062 Parameter Estimation on Bivariate Poisson Conditional Regression Models. 50064 Geographically Weighted Trivariate Generalized Poisson Regression Model. 51

iv | ISMI – ICTAS 2018 Kuala Lumpur


065 On the Classification Boosting in Imbalanced Data (Case Study: The Acceptanceof Bidikmisi Scholarship 2017 in East Java). 52

066 On the Comparison of Density Peaks Clustering Mixed (DPC–M) and K–prototypesAlgorithm for Clustering Mixed Large Data. 53

068 Micro and Macro Determinants of Delisting and Liquidity in Indonesian StockMarket: A Time Dependent Covariate of Survival Cox Model Approach. 54

069 Forecasting Exchange Rate Across Countries with Gold Price as ExogenousVariable Using Transfer Function and Vari-X Model. 55

070 On the Comparison of Deep Learning Neural Network and Binary LogisticRegression for Classifying the Acceptance Status of Bidikmisi ScholarshipApplicants In East Java. 56

071 Outlier Detection using Widening Window Based on Out of Window Time Data. 57072 Modifying Product and Marketing Strategy to Keep Sustainability of Batik

Surabaya. 58073 MRI-Based Brain Tumor Segmentation Using Modified Stable Student–t Burr

(MSTBurr) Mixture Model With Bayesian Approach. 59074 Fernandez–Steel Skew Normal (FSSN) Mixture Model Using Bayesian Approach

for MRI–Based Brain Tumor Segmentation. 61075 Algae Concentration Dynamical Model for Performance Analysis and Evaluation

of Facultative Wastewater Treatment Ponds. 63076 MRI–Based Brain Tumor Segmentation Using Gaussian Mixture Model (GMM) and

Hybrid Gaussian Mixture Model – Spatially Variant Finite Mixture Model(GMM–SVFMM) with Expectation–Maximization (EM) Algorithm. 64

077 Process Improving in a Domestic Waste Water Treatment Stabilization Ponds byUsing Mathematical Optimization Approach. 66

078 Gaussian Mixture Model with Expectation Maximization for MRI–BasedSegmentation to Build 3 Dimension Image on Brain Tumor Area. 67

079 On the Optimum MRI–Based Brain Image Segmentation with SpatiallyConstrained Gaussian Mixture Model Using Reversible Jump Markov ChainMonte Carlo. 69

080 Numerical Solution of Fractional Electrical Circuits by Haar Wavelet. 70081 Wind Speed Forecasting Using Generalized Autoregressive Conditional

Heteroscedasticity Model Optimized by Genetic Algorithm. 71084 Linking Twitter Sentiment Knowledge with Infrastructure Development by Text

Mining. 72085 Surabaya Government Performance Evaluation Using Tweet Analysis. 73086 The Performance of Kernel Logistic Regression and Regularized Logistic

Regression using Truncated Newton on Imbalanced Classification Problem. 74087 Frequency Model of Car Credit Payment using Bayesian Geometric Regression

and Bayesian Mixture Geometric Regression. 76088 Multivariate Time Series Forecasting using Hybrid Vector Autoregressive – Neural

Network for Coupled Roll–Sway–Yaw Motions Prediction. 77089 Stock Daily Price Regime Model Detection Using Markov Switching Model. 79

STEM Harness 4IR Challenges and Opportunities | v


090 Better Bootstrap Confidence Intervals for Parameter Estimators of Kriging ModelBased on Semiparametric Bootstrapping. 80

091 An Approximate Solution of Fractional Kolmogrov–Petrovskii–Piskunov Equations. 81092 Text Mining for Identifying and Visualizing Topics of Citizen Opinion in Media

Centre Surabaya. 82097 Construction of Interval Prediction in GSTAR Model for Forecasting Consumer

Price Index. 83098 Comparison of Nonparametric Classifications Approaches for Epileptic Seizure

Detection Based on Elecroencephalogram Signals. 84099 Microarray Classification of Prostate Cancer Using Hybrid Support Vector

Machine – Genetic Algorithm (SVM–GA). 86100 Genetic Algorithm for Feature Selection and Parameter Optimization in Fuzzy

Support Vector Machine: Case of Colon Cancer Microarray Classification. 87101 Existence Conditions of the Two and Four Periodic Solutions of Second–order

Neutral Delay Differential Equations with Piecewise Constant Arguments. 88104 Algorithm of Bayesian VAR on Spatio Temporal Disaggregation Method. 89105 Generalized Interval–Valued Intuitionistic Hesitant Fuzzy Soft Set. 90106 Comparing Structural Equation Modelling with Robust Covariances, Asymtotic

Distribution Free Estimator, and Generalized Least Square Methods. 92108 Comparing Singular Spectrum Analysis and Seasonal Autoregressive Integrated

Moving Average for Forecasting Seasonal Data. 93109 Genetic Algorithm for Feature Selection in Text Clustering. 94111 New Three Term Conjugate Gradient Method with Exact Line Search. 95112 Comparison of Semivariogram Models in Rain Gauge Network Design. 96114 Academic Preferences Based on Students’ Personality Analysis. 98115 Multiple Linear Regression Model of Rice Production using Conjugate Gradient

Methods. 99117 Reduction of the Drag Coefficient on the Circular Cylinder with Three Passive

Controls with Re = 10,000. 100118 Giffler and Thompson Algorithm and Disjunctive Programming as Initial Solution

for TABU search in Solving Job Shop Scheduling Problem. 101119 Dynamic Analysis of the Mathematical Model for Microalgae Production. 102120 An Analytic Valuation of A Deposit Insurance. 103121 Time Series Regression and ARIMA Modeling for Forecasting Stock Price Index

in ASEAN Countries. 104122 Deep Learning for Sentiment Analysis. 106123 Numerical Simulation of Fluid Flow Around Circular Cylinder and Three Passive

Control Modifications to Reduce Drag Coefficient at Re= 500. 107124 Clustering of Rainfall Distribution Patterns using Time Series Clustering Method. 108125 Application of Genetic Algorithm for Large Scale Quadratic Optimization of

Probabilistic Supplier Selection Problem with Inventory Management. 110126 Probabilistic Approach for Failure Mechanism of Offshore Structures Subjected

to Ship Collision. 111

vi | ISMI – ICTAS 2018 Kuala Lumpur


127 A Mathematical Proof of Explicit Formulas for the Coefficients of Finite DifferenceApproximations of Second Derivatives. 112

128 Modeling of Clean Water Consumption using Tobit Double Hurdle Model. 113129 Variational Approximation for Intersite Dark Solitons in a Discrete Nonlinear

Schrödinger Equation. 114130 Grouping Districts/Cities Based on Environment Sanitation Indicators as Basic

Evaluation of SDGs Goals achievement in East Java Province. 115131 A New Test of Discordancy in Cylindrical Data. 116132 A Modified Model-Selection Criteria in Generalized Estimating Equation for Latent

Class Regression Model. 117133 Assessing Dynamic-Time-Warping Dissimilarity Measures in Regionalization of

River Discharges. 118134 Bayesian Models for Small Area Estimation of Binary Responses based on

Unequal Probability Samples. 120135 Estimation and Model Reduction of Water Level in Bengawan Solo River. 121136 Modeling Study of Food Necessity Forecasting in Indonesia. 122137 A Comparative Assessment of Classification Methods for RNA–Seq Data. 123138 Temperature and Humidity Forecast via Univariate Partial Least Square and

Principal Component Analysis. 124139 Named Entity Recognition Application for Short Message Service Text in Business

Corporation. 125140 Optimal Control of Lipid Extraction Model on Microalgae Using LQR (Linear

Quadratic Regulator) and PMP (Pontryagin Maximum Principle) Methods. 126142 Positive Normalization of Discrete Descriptor System under Disturbance. 127143 SEM BMARS: An Alternative Methods of Nonlinear SEM. 128144 Indonesian Sentiment Analysis using Combination of Algorithms to Improve the

Performance in Imbalanced Dataset. 129146 Mechanistic Model of Radiation-Induced Bystander Effects Using Structured Cells

Population Approach. 130147 Solving Surface Decontamination Model Using Laplace Transform. 131149 Estimation of Rainfall Curve by using Functional Data Analysis and Ordinary

Kriging Approach. 132150 g–Jitter Induced Mixed Convection Flow of Casson Fluid with the Presence of

Metallic Nanoparticles 133151 The use of RWikiStat for Fuzzy Learning System using Android Software. 135152 Forecasting East Kalimantan Hotspots Using Kalman Filter. 136153 A Hybrid Multivariate Time Series Model in Forecasting Meteorological Data. 137154 Genetic Algorithm for Inventory Routing Problem with Carbon Emission

Consideration. 138155 On the Performance of Robust Augmented Approach to Desirability Function for

Optimizing Multiple Responses. 139156 Transcritical Flow Over a Bump Using Forced Korteweg–de Vries (fKdV) Equation. 140

STEM Harness 4IR Challenges and Opportunities | vii


157 Heat and Mass Transfer of Magnetohydrodynamics (MHD) Boundary Layer Flowusing Homotopy Analysis Method. 142

viii | ISMI – ICTAS 2018 Kuala Lumpur


Plenary

Essential Mathematical Contributions To The Digital Economy

PETER GRINDROD

University of Oxford

ABSTRACT

We will consider a range of issues and problems arising within the growing digital economy andindicate how mathematical sciences can make a large and critical contribution. In particularcompanies have the strategic opportunity to Spivot", and make a digital transformation, usingtheir customer and citizen data resources to build next generation products and services. Thealternative is that the companies carry on, squeezing out incremental improvements within theirpresent paradigm, until some game-changing or "black swan" event arrives that threatens theirbusiness. Hence radical, game-changing, mathematical ideas and methods are now essentialfor their futures.

In this lecture we will look at some examples of how mathematical ideas mayunderpin analytics that is highly differentiating for those companies that adopt them. Thisincludes exploitation of open "transparentT, mathematical ideas (dynamics, stability, networks,inference, algebra), the challenge in employing black box methods in highly regulatedenvironments, as well at the professionalisation of data science itself (modes of working andcodes of conduct). We will also look at some problems within the public sector arising withinsecurity and defence. Again the data deluge and social media offer opportunities that in turndrive reproach within the mathematical sciences.

STEM Harness 4IR Challenges and Opportunities | 1


Keynote Session 1

Theme:

STEM Harnesses 4th Industrial Revolution (4IR) Challenges &Opportunities: Academic & Industry Perspectives

Panels:

PROF. DR. MOHD SALMI MD. NOORANI FASc,DR. DZAHARUDIN MANSOR,IR. DR. CHAN TUCK LEONG.

2 | ISMI – ICTAS 2018 Kuala Lumpur


Keynote Session 2

In Bayesian: All About Hierarchical Modelling

NUR IRIAWAN

Department of StatisticsFaculty of Mathematics, Computation, and Data Sciences

Institut Teknologi Sepuluh Nopember60111 Surabaya, [email protected]

ABSTRACT

There are two types of hierarchical modeling in Bayesian; i.e. purely a hierarchical model anda structurally hierarchical cases model. The first model is due to the Bayesian formula whichincludes the prior distribution set as the one level hierarchy model to characterize the patternof data to be modeled. The second model is due to the data structure to be modeled already inthe hierarchical form, and the model, therefore, is compulsory to be hierarchical. Data structuralphenomena in the real cases are almost always happening in the hierarchical form. Bayesianhierarchical modeling provides a theoretical framework for formalizing an inference for a set ofparameters of the model that are shared across a level of these structural data. This modelingwill be able to provide a way to easily understand the work of the hierarchical algorithm whichis operated as a leveling structurally data procedure, as well as an opportunity to make useof computational strategies for efficient statistical inference in each hierarchy. This approachwill be demonstrated in some examples of modeling, i.e. in such of distributional estimationand inference and in the generalized linear modeling, including the mixture modeling and thehierarchical mixture modeling.

Keywords: Bayesian analysis; hierarchical structure; hierarchical model, mixture model, generalized

linear model.



ID 005

Parameter Estimation for a Model of Ionizing Radiation Effects on

Cells in Different Cell Cycle Phase

HAMIZAH RASHIDa, FUAADA MOHD SIAMb & NORMAH MAANc

a,b,cDepartment of Mathematical Sciences

Faculty of Science

Universiti Teknologi Malaysia

81310 Johor Bahru, Malaysia

[email protected], [email protected], [email protected]

ABSTRACT

A mathematical model has been formulated to explain the effect of ionizing radiation on cells within the cell

cycle phase. The parameters in the model are estimated using global and local optimization algorithms.

The aimed of this study is to compare the efficiency between global and local search method. There are,

genetic algorithm and pattern search respectively. Exponential data from the cell survival of irradiated

Chinese hamster ovary (CHO) is used to find the minimum value of the sum of squared error (SSE) between

experimental data and simulation data from the model. The performance of both methods are compared

based on the computational time and the value of the objective function, SSE. The optimization process is

carried out using the MATLAB programming built-in function. The parameter estimation results showed

that genetic algorithm is more superior than pattern search for this problem.

Keywords: parameter estimation; genetic algorithm; pattern search; two ionizing radiation effects.



ID 006

Two–Dimensional Signal Transduction during the Formation of

Invadopodia

NUR AZURA NOOR AZHUANa, CLAIR POIGNARDb, TAKASHI SUZUKIc, SHARIDAN SHAFIEd & MOHD

ARIFF ADMONe

a,d,eDepartment of Mathematical Sciences

Faculty of Science




bINRIA de Bordeaux–Sud Ouest

Team MONC, F–33405 Talence Cedex, France

[email protected]

cCenter for Mathematical Modeling and Data Science

Osaka University

1–3 Machikaneyama-cho, Toyonaka City

Osaka 560-8531, Japan

[email protected]

ABSTRACT

Metastasis is the major cause of death among cancer patients. At the subcellular level, actin-rich

protrusions known as invadopodia are the key points for the cancer cell invasion during the early stage of

the metastasis. Signal transduction is one of the important parts associated with invadopodia formation

which is stimulated through binding between epidermal growth factor receptors and ligands on the plasma

membrane. The signal enhances the actin assembly and the up-regulation of matrix metalloproteinases

which consequently leads to cancer cell invasion. In this study, we investigated a two-dimensional free

boundary problem in a static-case of signal transduction during the formation of invadopodia. The signal

equation is represented by a Laplace equation with Dirichlet boundary condition. The plasma membrane

is takenas zero level set function. The membrane is moved by the velocity of the cancer cell which is equal

to the gradient of the signal. The first order Cartesian finite difference scheme of the level set method is

used to solve the complete model numerically. Our results showed that protrusions are developed on the

membrane surface due to the presence of signal density inside the cell as time increases.

Keywords: invadopodia formation; signal transduction; free boundary problem; level set method.



ID 007

Identifying Risk Factors for Female Cardiovascular Disease Patients in

Malaysia: A Bayesian Approach

NURLIYANA JUHANa, YONG ZULINA ZUBAIRIb, ZARINA MOHD KHALIDc & AHMAD SYADI MAHMOOD

ZUHDId

a,cDepartment of Mathematical Sciences

Faculty of Science



[email protected], [email protected]

aPreparatory Centre of Science and Technology,

Universiti Malaysia Sabah

88400 Sabah, Malaysia

bCentre for Foundation Studies in Science

University of Malaya

50603 Kuala Lumpur, Malaysia

[email protected]

dCardiology Unit

University Malaya Medical Centre


[email protected]

ABSTRACT

Most adults at increased risk of cardiovascular disease (CVD) have no symptoms or obvious signs

especially in females, but they may be identified by the assessment of their risk factors. The Bayesian

approach is a specific way in dealing with this kind of problem by formalizing a priori beliefs and of

combining them with the available observations. This study aimed to identify associated risk factors in

CVD among female patients presenting with ST Elevation Myocardial Infarction (STEMI) using Bayesian

approach and obtain a feasible model to describe the data. A total of 1248 STEMI female patients in the

National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006–

2013 were analysed. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the

univariate and multivariate analysis. Model performance was assessed through convergence diagnostics,

model calibration and discrimination. The final multivariate model of STEMI female patients consisted of



six significant variables namely smoking, dyslipidaemia, myocardial infarction (MI), renal disease, Killip

class and age group. Females aged 65 years and above have higher incidence of CVD and mortality is high

among female patients with Killip class IV. Also, renal disease was a strong predictor of CVD mortality.

Besides, performance measures for the model was considered good. Bayesian approach provided a better

understanding on the associated risk factors of CVD for female patients which may help tailor prevention

or treatment plans more effectively

Keywords: STEMI; cardiovascular disease; female; risk factor; Bayesian.



ID 008

Skyrme–Hartree–Fock Approach for Descriptions of Static Nuclear

Properties of Well-deformed Nuclei

MENG-HOCK KOHa, NURHAFIZA M.NORb, NOR ANITA REZLEc, KAI-WEN KELVIN-LEEd, P.QUENTINe &

L.BONNEAUf

aDepartment of Physics

Faculty of Science



[email protected]

ABSTRACT

One of the major aim of theoreticians working in nuclear physics is to arrive at a robust model that can

provide accurate descriptions of nuclear properties. There are, at present, three main approaches namely

those based on macroscopic, microscopic or macroscopic-microscopic approaches. While the macroscopic

approach has a long-standing history in nuclear physics, the focus was shifted to microscopic approach in

the beginning of 1970’s when the Hartree-Fock (HF) approach is first applied to nuclear physics. Within

the HF framework, pairing correlations between nucleons are treated using the Bardeen-Cooper-Schrieffer

(BCS) method. In the past decade, the investigation of nuclear properties of odd-mass nuclei using the

HF+BCS approach has been on the rise. In our work, we employed the so-called self-consistent blocking

procedure within the HF+BCS approach to odd-mass nuclei. The nucleon-nucleon interaction entering the

HF part has been approximated by the Skyrme interaction, while the seniority force is used to approximate

pairing interaction between nucleons of similar charge state. In this research, we showcase applications

of the HF+BCS approach to the study of some nuclear properties of nuclei in the actinide and rare earth

regions. Focus is given to the investigations of rotational band-heads and fission-barrier heights of actinide

nuclei. The latter is very much related to energy generation, a central theme in the Fourth Industrial

Revolution, by means of nuclear fission process. The way our results can be utilized within the scope of

nuclear energy production is briefly discussed. Finally, calculated results for other static nuclear properties

for example nuclear charge radii, magnetic dipole moment and electric charge quadrupole moments for a

wide range of rare earth nuclei are presented.

Keywords: Hartree–Fock; Skyrme; BCS; nuclear properties; odd-mass.



ID 009

Uncertainties in Static Nuclear Properties Due to Pairing Fit Procedures

within Skyrme–Hartree–Fock Approach

KAI-WEN KELVIN-LEEa, NURHAFIZA M.NORb, MENG-HOCK KOHc & NOR ANITA REZLEd

cDepartment of Physics

Faculty of Science



[email protected]

ABSTRACT

Pairing correlations between nucleons are important for proper descriptions of static and dynamical nuclear

properties. In the study of the former aspect, the Hartree-Fock (HF) method is one of the widely used

mean-field approach with pairing correlations being taken care off using the Bardeen-Cooper-Schrieffer

(BCS) method. Within this so-called HF+BCS approach, decisions must be made with regards to the

types of nuclear and pairing interactions. In our work, the nuclear interaction is approximated by an

equation proposed by Skyrme. Parameters entering the Skyrme’s interaction are determined through

some fit procedures depending on the purpose of the investigation. The SIII parameters set which gave

good descriptions of nuclear ground-state properties has been selected herein. For BCS pairing treatment,

we chose the more simplistic seniority force as the pairing interaction for a starting point to our study of

rare earth region. For any choice of pairing interaction, one needs to determine the strength of pairing

interaction. Most studies consider a fit based on either the odd-even staggering or moment of inertia

of even-even nuclei. In the odd-even staggering fit, one needs to perform HF+BCS calculations for the

neighbouring even-even nuclei. One can however choose to treat the odd-mass nucleus using either the

quasi-particle (QP) approach or the self-consistent blocking (SCB) approach. The QP approach is easier

since it does not involve extra calculation as is the case for SCB approach. However, we show that QP

approach yields a slightly different set of pairing strengths from the SCB fit. The consequences due

to varying pairing strengths on some static nuclear properties of rare earth nuclei is highlighted in this

research. These findings are of interest to other similar works as well as studies on nuclear fission which

are closely related to energy production relevant to the Fourth Industrial Revolution.

Keywords: Hartree–Fock; pairing; seniority force; rare earth; odd-even staggering; moment of inertia.



ID 010

Stochastic Mortality Model in a State-Space Framework

SITI ROHANI MOHD NORa, FADHILAH YUSOFb & ARIFAH BAHARc


Faculty of Science




ABSTRACT

The incorporation of the time-varying parameter in the mortality model has become one the main

contributions in the actuarial field since it allows for the stochastic nature of the mortality rates. However,

it has also become a growing concern among the researchers since the residuals of the proposed model are

evaluated independently. In this study, we extended the existing leading independent stochastic mortality

model which is the O’Hare mortality model into the state-space representation of the O’Hare mortality

model. The parameters of the extended model are estimated using the Expectation-Maximization algorithm

of the maximum likelihood estimation method. Using the Malaysia mortality data, we have found that our

proposed model has significantly improves the accuracy of the historical fit and the 5-year mortality rates

forecasts as compared to the existing model considered.

Keywords: state-space; stochastic mortality model; O’Hare and Li model; forecast.



ID 011

Construction of Dependence Model for Rainfall Stations by Joining

Time Series Models with Copula Method

RAHMAH BINTI MOHD LOKOMANa, FADHILAH YUSOFb& NOR ELIZA ALIASc

a,bDepartment of Mathematical Sciences

Faculty of Science




cDepartment of Hydraulics and Hydrology

Faculty of Civil Engineering


81310 UTM Johor Bahru, Johor, Malaysia

[email protected]

ABSTRACT

Many hydrologic studies have applied the copula method. However, the analyses only capture the

statistical distribution of the marginal variable without considering the nonstationary condition that may

exist in the hydrological time series. To investigate the dependence structure between two rainfall stations

at Johor Bahru, two application have been applied. First application is by considering the nonstationary

condition that exist in the rainfall data, time series forecasting models: ARIMA and GARCH models

were applied and the residuals data of the time series models were taken as the marginal variables.

Second application is that the rainfall data is assume as stationary and it is taken as the marginal

variables. In Application 1, the best fitted copula is Clayton Copula with AIC = – 300.362. While,

in Application 2, the best fitted copula in Frank Copula with AIC = – 291.400. Through goodness-off-

fit (GOF) and simulation tests, it’s found that Application 1 yields better performance than Application

2 in describing the nonstationarities in the marginal distributions. The results obtained in this study

highlight the importance of considering nonstationarity during estimating the dependence structure of the

hydrological data because under changing environments, either the individual hydrological series or the

dependence structure between the different hydrological series might be nonstationary.

Keywords: bivariate copula; time series models; dependence modelling; maximization by parts; rainfall.



ID 012

Modelling the Asthma Disease Behaviour by Count Analysis Approach:

Poisson INGARCH and Negative Binomial INGARCH

’AAISHAH RADZIAH BINTI JAMALUDINa, FADHILAH YUSOFb & SUHARTONOc


Faculty of Science




cDepartment of Statistics

Faculty of Mathematics and Natural Sciences

Institut Teknologi Sepuluh Nopember,

Surabaya, Jawa Timur 60111, Indonesia


ABSTRACT

Johor Bahru with the rapid development make the pollution episode becomes an issue to be considered

and these incidences have contributed to the number of asthma cases in this area. Therefore, the goal of

this study is to investigate the behaviour of asthma disease in Johor Bahru by count analysis approach

namely; Poisson Integer Generalized Autoregressive Conditional Heteroscedasticity (Poisson-INGARCH)

and Negative Binomial INGARCH (NB-INGARCH) with identity and log link function. Intervention analysis

has been done since the outbreak exist in the asthma data for the period of July 2012 and July 2013. This

happen may be because of the extremely bad haze in Johor Bahru due to Indonesian fires. The estimation

of the parameter will be done by quasi maximum likelihood estimation. Model assessment for this model

was evaluated from the Pearson residuals, cumulative periodogram, the probability integral transform (PIT)

histogram, log likelihood value, Akaike’s Information criterion (AIC) and Bayesian information criterion

(BIC). Our result show that NB-INGARCH with identity and log link function is adequate in representing

the asthma data with uncorrelated Pearson residuals, higher in log likelihood, the PIT exhibits normality

yet the lowest AIC and BIC. However in term of forecasting accuracy, NB-INGARCH with identity link

function is perform better with the smaller RMSE (8.54) for out sample data. Therefore, NB-INGARCH with

identity link function can be applied as the prediction model for asthma disease in Johor Bahru. This

finding hopefully can help the authorities to assist the prevention and reduction of asthma diseases in the

district of Johor Bahru. The involved parties, such as hospitals can do early preparation in facing the risk

of diseases that may occur.

Keywords: asthma; pollution; Poisson INGARCH; NB-INGARCH.



ID 013

Understanding the Pattern of Wind Direction in Malaysia

NOR HAFIZAH MUSLIMa, YONG ZULINA ZUBAIRIb, ABDUL GHAPOR HUSSINc, SITI FATIMAH HASSANd &

NURKHAIRANY AMYRA MOKHTARe

aInstitute of Graduate Studies



[email protected]

aFaculty of Industrial Sciences & Technology

Universiti Malaysia Pahang

Lebuhraya Tun Razak

26300 Gambang, Pahang, Malaysia

b,dCentre for Foundation Studies in Science




c,eFaculty of Defence Sciences and Technology

National Defence University of Malaysia

Kem Sungai Besi



ABSTRACT

Understanding the behavior of wind direction is important in meteorological studies. It is always being

used in making weather prediction. In exploratory data analysis, summary statistics provide basic

information such as the measures of central tendency and the spread of the maximum wind speed and

wind direction. In this study, we analyse the pattern of wind direction data of two monsoons from four

stations in Malaysia from the year of 2013 to 2017. The wind direction data is modeled using von

Mises distribution where confidence interval for concentration parameter is estimated using bootstrap-

t method. Based on the performance measure of expected length, the confidence interval provides an

efficient measure of the concentration parameter. The implication of this study varies with respect to

monsoons and locations are useful in understanding the pattern of wind direction in Malaysia.



Keywords: bootstrap-t; circular variables; concentration parameter; von Mises distribution; wind direction.



ID 014

Fast and Robust Parameter Estimation in the Application of Fuzzy

Logistic Equations in Population Growth

NOR ATIRAH IZZAH ZULKEPLIa, YEAK SU HOEb & NORMAH MAANc


Faculty of Science




ABSTRACT

In this paper, extended Runge-Kutta fourth order method for directly solving the fuzzy logistic problem is

constructed. The extended Runge-Kutta method has a lower number of function evaluations compared

with the classical Runge-Kutta method. The enhanced numerical robustness of the method in parameter

estimation is applied via error minimization in predicting the growth rate and carrying capacity. Then,

the results of fuzzy logistic model with the estimated parameters is compared with population growth

data in Malaysia. The results show that this method is accurate comparing with the data population

and substantially faster comparing to numerical gradient approach in parameter estimation. Numerical

example is given to illustrate the efficiency of the proposed model. It is conclude that robust parameter

estimation technique is efficient in population growth model.

Keywords: fuzzy logistic equations; population growth; parameter estimation; robust gradient

minimization.



ID 015

Multiscale Boundary Element Method for Acoustic Wave Model

NOR AFIFAH HANIM BINTI ZULKEFLIa, YEAK SU HOEb & MUNIRA BINTI ISMAILc


Faculty of Science




ABSTRACT

In numerical methods, boundary element method has been widely used to solve acoustic problems.

However, it suffers from well-known drawbacks with regard to the computational efficiency. This prevents

the boundary element method from being applied to large-scale problems. This paper proposes a new

multiscale technique coupling with boundary element method to speed up the acoustic problems. Numerical

example is given to illustrate the efficiency of the proposed method. The solution of the proposed method

will be validated with conventional boundary element method and the former method shows less iteration

in computation.

Keywords: acoustic wave model; boundary element method; multiscale technique.



ID 016

An Outlier Detection Method For Circular Data Using Covratio

Statistics

NURKHAIRANY AMYRA MOKHTARa, YONG ZULINA ZUBAIRI b, ABDUL GHAPOR HUSSINc & NOR

HAFIZAH MOSLIMd

a,cFaculty of Defence Sciences and Technology

National Defence University of Malaysia

Kem Sungai Besi



bCentre for Foundation Studies in Sciences


50603 Kuala lumpur, Malaysia

[email protected]

dInstitute of Graduate Studies


50603 Kuala lumpur, Malaysia

[email protected]

ABSTRACT

The existence of outlier may affect data aberrantly. However, outlier detection problem has been frequently

discussed for linear data but limited on circular data. Thus, this paper discusses about an outlier detection

method on circular data. We focus on circular data with equal error concentration parameters where the

data is studied using linear functional relationship model. We modify the covratio statistics in which the

correction factor is applied to the estimation of concentration parameter. We develop the cut-off point based

on the 5% upper percentile of the covratio statistics and the power of performance of outlier detection is

examined by a Monte Carlo simulation study. The simulation result shows that the power of performance

increases when the concentration and the level of contamination increase.The applicability of the proposed

method is illustrated by using the wind direction data collected from the Holderness Coastline at the

Humberside Coast in North Sea, United Kingdom.

Keywords: circular data; linear functional relationship model; outlier detection; covratio statistics.



ID 018

Comrade Matrix Methods in Computing the GCD of Two Polynomials

SITI NOR ASIAH ISAa, NORAINI ARISb, YEAK SU HOEc & SHAZIRAWATI MOHD PUZId

a,b,c,dDepartment of Mathematical Sciences

Faculty of Science



[email protected], [email protected], [email protected], [email protected]

ABSTRACT

This paper revisits the comrade matrix approach in finding the greatest common divisor (GCD) of two

orthogonal polynomials. The present work investigates on the applications of the QR decomposition with

iterative refinement (QRIR) to solve certain systems of linear equations which is generated from the comrade

matrix. Besides iterative refinement, an alternative approach of improving the conditioning behavior of the

coefficient matrix by normalizing its columns is also considered. As expected the results reveal that QRIR

is able to improve the solutions given by QR decomposition while the normalization of the matrix entries do

improves the conditioning behavior of the coefficient matrix leading to a good approximate solutions of the

GCD

Keywords: comrade matrix; QR decompositions; iterative refinement; normalization.



ID 019

Mixed Convection Boundary Layer Flow of Viscoelastic Nanofluid past

a Sphere with Constant Wall Temperature

WAN RUKAIDA WAN ABDULLAHa & SHARIDAN SHAFIEb


Faculty of Science




ABSTRACT

The mixed convection boundary layer flow of a viscoelastic nanofluid past a sphere with constant wall

temperature is discussed. Carboxymethyl cellulose solution-water (CMC-water) is chosen as the base and

cooper as a nanoparticle with the Prandtl number Pr = 6.2. The governing non-similar partial differential

equations are first transformed into dimensionless forms and then solved numerically using the Keller-box

method by augmenting an extra boundary condition at infinity. The numerical results obtained for limiting

case are comparing with related outcomes in order to validate the present results. Results on the effects of

the viscoelastic parameter in the presence of porosity and mixed convection on the skin friction and heat

transfer as well as velocity and temperature profiles are obtained and discussed.

Keywords: viscoelastic; nanofluid; mixed convection; sphere.



ID 020

Thermal Radiation Effects on Magnetohydrodynamics Dusty

Non–Newtonian Flow Past an Exponentially Stretching Sheet

SITI NUR HASEELA IZANIa & ANATI ALIb


Faculty of Science




ABSTRACT

Numerical solutions are carried out to study a boundary layer analysis of steady-state two-phase dusty

Jeffrey fluid flow in the presence of magnetic field. The influence of thermal radiation and viscous

dissipation are also considered. The fluid flow is deliberated on non-linear stretching surface. The main

purpose of this study is to conduct a detailed analysis of the fluid flow behavior with suspended dust

particles in the non-Newtonian fluid. Keeping in mind, the spherical particles are uniformly distributed

throughout the carrier fluid and acts as non-interacting dilute suspension. The governing partial differential

equations of dusty Jeffrey fluid with boundary conditions are transformed into an ordinary differential

equation by using suitable similarity transformation. The transformed equations corresponding to the

momentum, energy and concentration are then solved numerically by Keller-Box method. The influence

of several physical parameters on the velocity, temperature and concentration profiles, skin friction, the

rate of heat and mass transfer are presented in graphical form and discussed in details. Particularly,

the study of heat and mass transfer of Jeffrey fluid embedded with dust particles is useful under various

physical circumstance nowadays. The main results obtained from this study is that the presence of a

large number of dust particles caused the velocity of carrier fluid reduces significantly. It is also shown

that radiation parameter, Eckert number and Prandtl number increase the temperature profile for both

phases. However, these three parameters do not change the values of skin friction coefficient as well as

velocity and concentration profiles. The momentum, thermal and species concentration boundary layer

thickness drastically attains it asymptotic value for the dust particles suspended in the non-Newtonian

fluid case as compared to the Newtonian fluid case. The finding results for carrier phase are compared

with the previously published studies and was found to be in a good agreement.

Keywords: dusty Jeffrey fluid; two-phase flow; heat and mass transfer; thermal radiation; non-linear

stretching sheet.



ID 021

Modelling Monthly Rainfall in Peninsular Malaysia using Tweedie

Distribution

MUHAMAD HANIF BIN AZMIa & SHARIFFAH SUHAILA b


Faculty of Sciences




ABSTRACT

The finding of an accurate and reliable rainfall model has been the point of much discussion in past

statistical research. Many researchers used two separate models to fit the rainfall data, as the rainfall

process involves both the discrete and continuous periods. However, Tweedie distribution is able to

combine both aspects to provide one complete rainfall process. These distribution belong to the class

of exponential dispersion models where the variance is proportional to some power of the mean. A special

case of power-variance family of distributions, called the Poisson-gamma distributions is used to handle

the continuous real data with a discrete mass at zero. Hence, this study proposed the Tweedie family of

distribution to fit the monthly rainfall data from 10 selected rain gauge stations in Peninsular Malaysia

which covers the period from January, 1980 to December, 2015. The aim of this study is to determine

whether the distributions within the Tweedie family fit well the monthly Malaysian rainfall data. The

possibility that different distributions are needed for each station was explored by estimating the index

parameter, p. To do so, the profile likelihood plot is used to estimate the p index for which the log-likelihood

is maximized and hence the appropriate distribution within the Tweedie family was identified. Then, the

Tweedie Generalised Linear Model (GLM) with sine and cosine terms as predictors was used to model

the rainfall data. Within the Tweedie family, the Poisson-gamma distribution is more useful to describe

two important features of rainfall pattern, which are the occurrences and the amount of rainfall. These

model was identified as fairly well at fitting monthly rainfall data with exact zeroes. The model are also

potentially important for prediction and simulation purposes in various areas, including agriculture and

irrigation.

Keywords: Tweedie distribution; exponential dispersion model; Poisson-gamma distribution; generalised

linear model.



ID 022

On Estimates of Malaysian Mortality Rates: A Numerical Approach

NUR IDAYU BINTI AH KHALILUDINa, ZARINA MOHD KHALIDb & HALIZA BINTI ABD RAHMANc


Faculty of Science




ABSTRACT

Life table is a table that shows mortality experience of a nation. However, in Malaysia, the information in

this table is provided in the five-years age groups (abridged) instead of every one-year age. Hence, this

study aims to estimate the one-year age mortality rates from the abridged mortality rates using several

numerical methods. We applied Kostaki method and the Akima spline method to four sets of Malaysian

group mortality rates ranging from period of 2013 to 2016. The result were then compared with the one-

year mortality rates. We found that the method by Akima is the best method for the Malaysian mortality

experience as it gives the least minimum of sum of square errors. The method is not only provide a good fit

but also, shows a smooth mortality curve.

Keywords: mortality table; life table; cubic spline (Akima); mortality rates; expansion of life table.



ID 023

Hartmann and Reynolds Number Effects into the Newtonian Blood Flow

of a Bifurcated Artery with an Overlapping Stenosis

NOLIZA MOHD ZAINa & ZUHAILA ISMAILb


Faculty of Science




ABSTRACT

Blood flow through a bifurcated artery with the presence of an overlapping stenosis located at parent’s

arterial lumen under the action of a uniform external magnetic field is studied in this paper. Blood is

treated as an electrically conducting fluid which exhibits the Magnetohydrodynamics (MHD) principle and

is characterized by a Newtonian fluid model. The blood flow is assumed as an incompressible, steady

and laminar. Whereas, the wall of arterial bifurcation is considered rigid with no-slip condition modelled

as a two-dimensional channel. The governing equations that describes the Newtonian blood flow under

the presence of a magnetic field is discretized using a stabilization technique of finite element known as

Galerkin least-squares that allow the Babuska-Brezzi condition on the combination of pressure and velocity

subspaces to be neglected. The effects of dimensionless parameters of Hartmann and Reynolds number

into the fluid’s velocity, streamlines pattern and pressure were examined in details with further scientific

discussions. Results obtained reveals that Hartmann and Reynolds number has considerably affects the

blood flow behaviors not only in the stenotic region but also in various locations of the vessel. Reynolds

number takes vital control on fluid’s motion relative to how viscous the fluid that contributes to eddies

formation which in turn gives rise to turbulence phenomena. In addition, findings from this study may

benefits medical practitioners to analyse the impacts of static magnetic fields in magnetic therapy in order

to avoid any excessive exposure that can lead to irreversible changes to calcium dynamic of individual

patient.

Keywords: magnetohydrodynamics; Newtonian; Galerkin least-squares; overlapping stenosis; bifurcated

artery.



ID 025

Tumour-Immune Interaction Model with Cell Cycle Effects including G0

Phase

NOR AZIRAN AWANGa & NORMAH MAANb


Faculty of Science




ABSTRACT

Tumour cells behave differently than normal cell in the body. The cells grow and divide in uncontrolled

manner (actively proliferating) and failed to respond to the signal. However, there are cells become inactive

and reside in quiescent phase (G0). These cells are known as quiescence cells that are less sensitive to

drug treatments (radiotherapy and chemotherapy) than the actively proliferation cells. This paper proposes

a new mathematical model that describes the interaction of tumour growth and immune response by

considering the tumour population is divided into three different phases, namely interphase, mitosis and

G0. The model consists of a system of delay differential equations where the delay, represents the time for

tumour cell resides interphase before entering mitosis phase. Stability analysis of the equilibrium points of

the system is performed to determine the dynamics behaviour of solution which include stability of steady

states, periodic and oscillatory solutions, bifurcations, and stability switches. The result showed that the

tumour populations depend on the number of tumour cell that enter active (interphase and mitosis) and G0

phase. This study is important for the treatment planning since tumour cell can resist treatment when cells

refuge in a quiescent state.

Keywords: tumour growth; immune system; cell cycle; quiescent phase.



ID 029

A Hybrid Wavelet-ARIMA Model for SPI Drought Forecasting

MOHAMMED SALISU, ALFAa & ANI BIN SHABRIb


Faculty of Science




ABSTRACT

This paper proposes A Hybrid Wavelet-Auto-Regressive Integrated Moving Average (W-ARIMA) model to

explore the ability of the hybrid model over an ARIMA model. The W-ARIMA model was developed

by the combination of two methods, a Discrete Wavelet Transform (DWT) and ARIMA model using the

Standardized Precipitation Index (SPI) drought data for forecasting to assess the effectiveness of this model.

The models were used on four SPI data sets involving SPI3, SPI6, SPI9 and SPI12 data series. To assess

the potential of this model, the rainfall precipitation data from Arau Station in Malaysia was used for

drought forecasting. For the drought modelling development, a 624 month of SPI data from January 1954

to December 2008 was used and were divided into two parts (80% for training and 20% for testing). The

result of the Hybrid model for forecasting are compared with that of the conventional ARIMA model with

Mean Square Error (MSE) and Mean Average Error (MAE) as the performance statistical error measures

used. The results of the proposed Hybrid model (W–ARIMA) and that of the ARIMA model showed very

clearly that the propose method achieved the best forecasting performance in terms of accuracy for each

of the SPI data series. This indicates that W–ARIMA model outperform the traditional ARIMA model in SPI

drought forecasting.

Keywords: ARIMA; wavelet; SPI; drought; forecasting.



ID 030

Scenario Based Two-Stage Stochastic Programming Approach for the

Midterm Production Planning of Oil Refinery

NORSHELA MOHD NOHa, ARIFAH BAHARb & ZAITUL MARLIZAWATI ZAINUDDINc


Faculty of Science



a,b,cUTM Centre for Industrial and Applied Mathematics

Univeristi Teknologi Malaysia

81310 Johor Bharu Malaysia


ABSTRACT

In the past, deterministic linear programming approach is widely used in oil refinery optimization problems.

However, due to volatility and unpredictable of oil price in the past ten years, deterministic model might

not be able to predict the real situation as it does not take into account the uncertainties that lead to non-

optimal solution. Thus, this study will develop two-stage stochastic linear programming for the midterm

production planning of oil refinery to handle oil price volatility. Geometric Brownian motion (GBM) is used

to describe uncertainties in crude oil price, petroleum product prices, and demand for petroleum products.

This model generates the future realization of the price and demands with scenario tree based on the

statistical specification of GBM using method of moment as input to the stochastic programming. The

model developed in this paper was tested for Malaysia oil refinery data. The result of stochastic approach

indicates that the model gives better prediction of profit margin.

Keywords: Two-stage stochastic programming; geometric Brownian motion; scenario based approach; oil

refinery optimization; production planning.



ID 031

Single-machine-based Integrated Production Preventive Maintenance

Scheduling: A Simheuristic Approach

NURUL NADIAH ABDUL HALIMa, S. SARIFAH RADIAH SHARIFFb & SITI MERIAM ZAHARIc

a,b,cCentre for Statistics and Decision Science Studies,

Faculty of Computer and Mathematical Sciences,

Universiti Teknologi MARA

40450, Shah Alam, Selangor, Malaysia


bInstitute of Transport Malaysia (MITRANS),



[email protected]

ABSTRACT

Preventive maintenance planning becomes a crucial issue in the real world of the manufacturing process.

It is important in manufacturing industry in order to maintain the optimum level of production and

minimize its investments. Thus, this paper will focus on multiple jobs with single production line by

considering stochastic machine breakdown time. Three different cases of production scheduling; the

production scheduling without maintenance planning, production scheduling with machine breaks down

and integrated production with preventive maintenance scheduling will be considered in this paper. The

aim of this paper is to propose a good integration production and preventive maintenance scheduling

that will minimize total makespan time. A hybrid algorithm that combines simulation techniques inside

a metaheuristics technique is proposed in order to deal with the stochastic behavior problem. The best

outcome of the production schedule without maintenance planning is chosen based on the smallest value

of average makespan time and used as a guide to schedule the production scheduling with machine

breakdowns and integrated production with preventive maintenance scheduling. A deterministic model

will be adapted and tested under different levels of stochasticity. In this paper, we will use a different

level of machine breakdown time. Their performance will be evaluated based on the effect of preventive

maintenance on the average of minimum total makespan time. Then, the average total makespan time for

production scheduling with machine breaks down and the proposed integrated production and preventive

maintenance scheduling will be compared.

Keywords: machine’s failure; multiple-production; preventive maintenance; single line; simheuristic.



ID 032

The Evaluation Hybrid of ARIMA, Combined Forecast and GARCH Model

in Forecasting Tax Revenue in Nigeria

BABA GIMBA ALHASSANa & FADHILAH YUSOFb


Faculty of Science




ABSTRACT

Forecasting tax revenue and its predictability is very vital for government budgeting and tax administration

purposes. This research used monthly tax revenue data for the period of 120 months from January 2006

to December 2015. This study applied ARIMA, combined Forecast models and GARCH models to forecast

tax revenue and its volatility respectively. Tax revenue is found to be on increase steadily over the period,

although with a persistent volatility which increases over time. The observed volatility was found to be

associated with taxes from bases (income) which have high volatility. Based on the various forecast

accuracy evaluation Criteria. The study recommended the combined forecast and GARCH (1,1) model for

forecasting monthly revenue and its volatility respectively. The study also recommends enhance diversity

of taxes through widening consumption tax base within the existing tax portfolio so as to enhance its

contribution to revenue collection and its volatility.

Keywords: ARIMA; GARCH; combined forecast; forecasting; tax revenue.



ID 034

Spatial Bayesian Model Averaging to Calibrate Short-Range Weather

Forecast in Jakarta, Indonesia

NISWATUL QONAAHa, SUTIKNOb & PURHADIc

a,b,cDepartment of Statistics

Faculty of Mathematics Computing and Data Science

Institut Teknologi Sepuluh Nopember

60111 Surabaya, Indonesia


ABSTRACT

The output of weather forecast using ensemble is often underdispersive and uncalibrated. Bayesian

Model Averaging (BMA) is a statistical method to calibrate the ensemble forecasting and create more

reliable predictive interval. BMA produces the calibrated forecasts but it does not consider spatial

correlation. Unlike BMA, Geostatistical Output Perturbation (GOP) considers spatial correlation among

several locations altogether. GOP has spatial parameters that modify the forecasting output to capture

the spatial information. However, GOP only applies to a single deterministic forecasting. Spatial BMA is

a method which combines BMA and GOP. It extends BMA to produce calibrated probabilistic forecasting

for several locations simultaneously. The members of the spatial BMA ensemble are obtained by dressing

the weather forecasting by simulating spatially the correlated errors, in proportions that correspond to

the BMA weights. Temperature becomes the focus of this study because it tends to have a relatively

strong correlation with the other elements. Spatial BMA is applied and implemented to calibrate the

temperature forecasting in eight meteorological stations in Jakarta, Indonesia. Initially, Partial Least

Square (PLS), Principal Component Regression (PCR) and Regression Ridge methods become ensemble

members of BMA. Temperature forecasting of BMA is then used to obtain simulating spatially correlated

errors that modify temperature forecasting. For training period over 30 days, Spatial BMA produces the

calibrated temperature forecasts at 8 sites. It is able to calibrate the temperature forecasts better than

raw ensemble which coverage comes closer to standard, i.e. 50%. However, based on some indicators

of weather forecasting, such as Root Mean Square Error (RMSE) and Continuous Rank Probability Score

(CRPS), the original BMA is better than Spatial BMA in terms of accuracy and precision.

Keywords: BMA; ensemble; GOP; spatial BMA.



ID 035

Calibrating Weather Forecasting in Indonesia: The Geostatistical

Output Perturbation Method

SUTIKNOa, PURHADIb, IMAM MUKHLASHc, KARTIKA NUR ′ANISAd, URIP HARYOKOe & HASTUADI

HARSAf

a,b,dDepartment of Statistics


Institut teknologi Sepuluh Nopember

Kampus Sukolilo, Surabaya 60111, Indonesia


cDepartment of Mathematics


Institut teknologi Sepuluh Nopember

Kampus Sukolilo, Surabaya 60111, Indonesia

[email protected]

e,fCentre for Research and Development

Indonesian Agency for Meteorology, Climatology, and Geophysics

Jakarta 10720, Indonesia


ABSTRACT

Accuracy and precision of forecasting result are very important for the weather forecaster. The weather

condition affects the human’s activities such as determining harvesting period (agriculture), period/ time

of fishing (fishery), and determining the feasibility of flight or voyage (transportation). The accurate and

up to date weather forecasting can minimize the disaster risk that caused of hydrometeorology or weather.

In last few years BMKG (Meteorological, Climatological and Geophysical Agency) Indonesia has already

developed the numerical weather forecasting process using Numerical Weather Prediction (NWP) to support

weather forecasting. Using NWP forecasting has the high bias, including for the short-range one. This study

aims to forecast weather by considering the spatial correlation of observed station and to get the optimum

result of weather forecasting from the analysis. Geostatistical Output Perturbation (GOP) was proposed

and implemented to analyze data from Meteorology, Climatology and Geophysics Agency (BMKG), i.e. the

data of CCAM (conformal-cubic atmospheric model) NWP. The data are from eight meteorological stations,

i.e. Kemayoran, Priok, Cengkareng, Pondok Betung, Curug, Dermaga, Tangerang, and Citeko. Results

show that exponential is the best distribution model for analyzing maximum and minimum temperature

in Indonesia using GOP. Investigation of the underlying analysis in eight stations identifies the isolated



location (i.e. the location that is far away from the other locations) can have considerable impact

significantly to the accuracy and precision of weather forecasting using GOP model. Citeko has the quite

different characteristic from the other stations because the location is in the higher elevation area than

the other locations. If the modeling for maximum and minimum temperature in Indonesia do not involve

Citeko station, the accuracy and precision of forecasting are able to be increased twice and the results

show better than involving all stations.

Keywords: GOP; NWP; spatial weather forecasting.



ID 036

Logistic Regression Ensemble (Lorens) Applied to Drug Discovery

T.DWI ARY WIDHIANINGSIHa, HERI KUSWANTOb & DEDY DWI PRASTYOc


Faculty of Mathematics, Computing, and Data Science




ABSTRACT

Logistic regression is one of commonly used classification methods. As the parametric method, logistic

regression has some advantages related to hypothesis testing and its objective function. However, it

also has disadvantages in case of high dimensional data, such as multicolinearity, overfitting, and high

computational problem. To overcome these problems, ensemble based classification method has been

developed. Lorens, as the development of logistic regression, is expected to improve the classification

performance. Due to the algorithm of the ensemble method, Lorens is being a classification technique with

free of assumptions. This paper applies Lorens to high dimensional data in case of drug discovery. The

objective is to get the best model to classify radioprotection of compounds, as instances, to positive and

negative category. Furthermore, the characteristics of cells that have been treated by compound isused as

the features. The experimental results show that Lorens has a good performance. The accuracy is between

59.9% and 62.9%. On the other hand, AUC (Area Under Curve) lays between 0.655 and 0,706.

Keywords: drug discovery; ensemble; logistic regression; radio protection.



ID 037

Ensemble Support Vector Machine by Random Undersampling For DNA

Microarray Classification to Overcome Multiclass Imbalanced Data

NUR SILVIYAH RAHMIa, SANTI WULAN PURNAMIb & IRHAMAHc


Faculty of Mathematics, Computing and Data Science




ABSTRACT

Microarray technology measure on a large and parallel scale to express tens thousands of genes. It

has become one of the most successful molecular biological technologies of modern era and its widely

applied to predict gene function, new subtypes of specific tumors and cancer classification. However,

microarray data are known has feature characteristics such as high dimension, small sample, high noise

and imbalanced class distribution. So that, to overcome high dimensional data, the authors use Ensemble

Feature Subspace (FSS) method. This method will cluster all features and each cluster will be classified.

Meanwhile, imbalanced class distribution becomes a problem in classification because the classifier will

tend to predict majority class than minority class. Moreover, minority classes being underestimated and

influence the performance evaluation criteria. Therefore, this research applied Random Undersampling

that minimize negative impact of loss information as well as computational time. Data used in this research

are DNA Microarray data with various conditions of imbalance ratio that is low imbalanced (IR <= 9),

medium imbalanced (9.0 <IR <= 20) and high imbalanced (IR> 20). This research uses threefold cross-

validation and the feature selection method is Fast Correlation Based Filter (FCBF). The multiclass method

used in this study is SVM One Against One (OAO). Then, evaluation criteria of classification performance

based on Accuracy, Fscore and Gmean value and computational time. The results in this study show that

EnSVM-OAO (RUS) method with minimum clusters has a higher performance than SVM-OAO and EnSVM-

OAO methods.

Keywords: imbalanced data; multiclass SVM; random undersampling (RUS); ensemble feature subspace.



ID 038

Particle Swarm Optimization for Obtaining the Weights of Neural

Network

BUDI WARSITOa, HASBI YASINb & ALAN PRAHUTAMAc


Faculty of Science and Mathematics

Diponegoro University

Semarang, Indonesia


ABSTRACT

This research was discussed about the using of a class of heuristic optimizatian for obtaining the weights

of neural network model for time series prediction. In this case, Feed Forward Neural Network (FFNN) was

the chosen class of the network architecture. The heuristic algorithm determined for obtaining the weights

of the network was Particle Swarm Optimization (PSO). It is a non-gradient optimization technique. This

method is used for optimizing all of the weights from input to hidden layer and from hidden layer to output.

The lags used as the input were selected based on the strong relationship with the current. In each data

analyzed, a simple architecture was developed first. The incremental of neuron is added one by one until a

desired number. In each architecture, we repeated the running up to several times for getting the statistics

of mean and variance. Stability of the results was the based of choosing the best architecture, i.e the

optimal number of nodes in hidden layer. The analysis of comparison also been done in some activation

functions used at the hidden layer. The proposed procedure was applied in both the generating data from

autoregressive model and the real problem of the air pollution data.

Keywords: PSO; neural network; time series; air pollution.



ID 039

Scholarship Selection Using a Hybrid Fuzzy TOPSIS

KHAIRIL BARIYYAH HASSANa & ROSMA MOHD DOMb

aCentre for Foundation and General Studies

Universiti Selangor

45600 Selangor, Malaysia

[email protected]

bFaculty of Computer and Mathematical Sciences

Universiti Teknologi Mara

40000 Selangor, Malaysia

[email protected]

ABSTRACT

This research demonstrates the selection of scholarship recipients with cases in Centre for Foundation and

General Studies, Universiti Selangor, Malaysia by using Hybrid Fuzzy Technique for Order Preference by

Similarity to Ideal Solution (TOPSIS). The proposed Hybrid Fuzzy TOPSIS replaces the closeness coefficient

calculation in existing Fuzzy TOPSIS with new ranking index method proposed by Kuo (2017) in ranking

and choosing of alternatives. The ranking results are validated by comparing the ranking of alternatives

(scholarship applicants) with the ranking given by the original Fuzzy TOPSIS where ranking is based on

the closeness coefficient measurement. The findings show that the proposed Hybrid Fuzzy TOPSIS is

feasible to be used in ranking of alternatives since the ranking results produced are highly consistent.

Selection of recommended students who have the highest level of eligibility for the scholarship is based on

the highest value of the ranking index. The results of this study provide the ranking of candidate selection

for scholarship awards at the Centre for Foundation and General Studies, Universiti Selangor using Hybrid

Fuzzy TOPSIS and is found beneficial by the university management team.

Keywords: hybrid fuzzy TOPSIS; scholarship; selection; fuzzy TOPSIS.



ID 042

Parameter Estimation in Replicated Linear Functional Relationship

Model in the Presence Of Outliers

AZURAINI MOHD ARIFa, YONG ZULINA ZUBAIRIb & ABDUL GHAPOR HUSSINc

aInstitute of Graduate Studies

Universiti Malaya


[email protected]

bCentre for Foundation Studies in Science

Universiti Malaya


[email protected]

cFaculty of Defence Science and Technology

Universiti Pertahanan Nasional Malaysia


[email protected]

ABSTRACT

The relationship between two linear variables where both variables are observed with errors can be

modelled using linear functional relationship model. However, when there is no knowledge about the ratio

of error variance, we proposed that one can use the replicated linear functional relationship model. The aim

of this study is to compare the parameter estimates between unreplicated and replicated linear functional

relationship model. The study also extends to examine the behaviour of the estimators of replicated linear

functional relationship model in the presence of outliers. Simulation study is performed to investigate

the performance of the model. In the absence of outlier, it is found that the value of the parameter

estimates are almost similar for both models. Whereas in the presence of outliers, the parameter estimates

of the replicated linear functional relationship model has smaller mean square error as the number of

observations increased. This suggests the superiority of the replicated model.

Keywords: linear functional relationship model; outliers; replicated.



ID 043

The Role of Transportation Infrastructure in Central Java Indonesia

Regional Economic Growth: A Spatial Durbin Model Approach

ABDUL KARIMa, AKHMAD FATUROHMANb, SUHARTONOc & DEDY DWI PRASTYOd

a,bDepartment of Statistics

Faculty of Mathematics and Science

Muhammadiyah University of Semarang

Central Java, Indonesia


c,dDepartment of Statistics



Surabaya, Indonesia


ABSTRACT

The purpose of this study is to determine whether there is a relationship between spatial variables and

data for regional economic growth data in Central Java Province. Spatial durbin model becomes one

regression model consisting of spatial data structure, is the development of the spatial autoregressive

model, where there is a spatial effect on variable components not present in the SAR model or commonly

referred to indirectly on independent variables. The data used from Central Bureau of Statistics (BPS) of

Central Java Province consisting of the gross regional domestic product (GRDP) as dependent variable and

data of labor, human resources, and infrastructure as the independent variable. Based on the results of

the analysis, the average AIC is implemented in SDM is smaller than the ordinary least square model (OLS)

and SAR model. The results of human resources show that human capital jointly has a direct and indirect

influence on the GRDP, in addition, the labor variable does not have GRDP but is marked negative.

Keywords: spatial durbin model; spatial autoregressive; spatial modelling; growth economic;

transportation infrastructure.



ID 044

Analysis of HIV-1 Infection of CD4+ T Cells with Combined RTIs and PIs

Therapy

SUTIMINa, R. HERU TJAHJANAb & DEDY SUNARSIHd

a,b,cDepartment of Mathematics

Faculty of Sciences and Mathematics


Semarang 50275, Indonesia


ABSTRACT

In the study, we modify a mathematical model of HIV-1 infection of CD4+ T cells to study the implication

of RTIs and PIs therapies. We introduce the spread of HIV-1 infection by taking into account the contact

between the infected and healthy CD4+ T cells and incorporating RTIs and PIs treatments. The dynamical

behaviour of model is analysed to study the stability of equilibria. The local and global stability of equilibria

of the model are analysed based on the basic reproduction number that is derived from the next generation

matrix. The local stability of disease free equilibrium is analysed by linearization and Hurwitz criterion,

while the global stability of endemic equilibrium is analysed by constructing Lyapunov function and using

LaSalleŠs invariance principle. The scenarios of RTIs and PIs treatments are numerically studied to explore

the effectiveness of the treatments in reducing the progression of HIV-1 infection in CD4+ T cells. These

results show, when the basic reproduction number less than 1 then disease free equilibrium is locally

asymptotic stable, while the basic reproduction number exceed unity then endemic equilibrium is globally

asymptotic stable. The numerical results show that RTI therapy may be more effective compared to PI

therapy in reducing the progression of HIV-1 infection.

Keywords: HIV-1 infection; CD4+ T cells; RTIs; PIs.



ID 046

Optimal Control Strategy Through RTI and PI Therapy for HIV-1

Infection in CD4+ T Cells

R. HERU TJAHJANAa & SUTIMINb

a,bDepartment of Mathematic



Semarang 50275, Indonesia


ABSTRACT

HIV (Human Immunodeficiency Virus) is a retrovirus attracting the host cells that express receptor CD4

molecule. HIV-1 infects CD4+ T cells that express receptor CD4 and co-receptor CCR5 in its surface. The

therapy is used to block both the infection and replication of virus in the infected cells. The treatment

is conducted continuously by infected individual for his life. Many studies declare that the antiretroviral

treatment can be interrupted for fixed time, it is called the interrupted treatment strategy. The aim of the

strategy is to prevent drug resistance. Some studies have been conducted to determine the optimal time

that is needed to prevent effectively the infection progress. The mathematical model is an important tool

to analyze the spread of HIV-1 infection the immune system in long terms. The research is conducted

to analyze the effect of combined RTI and PI treatment regarding the interrupted treatment strategies.

This paper develops a mathematical model of HIV-1 infection in CD4+ T cells. The model is proposed

by considering the effect of cell-to-cell contact and of clearance of virus by CD4 T cells. Optimal control

strategy is used in this paper. The most important things in optimal control is the cost functional. The cost

functional used in this paper are maximize the healthy person and minimize the treatment cost.

Keywords: HIV-1; CD4+ T cells; RTI; PI.



ID 047

Rainfall Forecast with Best and Full Members of North American Multi

Model Ensemble (NMME)

DEFI YUSTI FAIDAHa, HERI KUSWANTOb & SUHARTONOc

aDepartment of Statistics

Faculty of Mathematics, Computation, and Data Science


Surabaya, Indonesia



Universitas Padjadjaran Bandung

Indonesia

[email protected]

b,cDepartment of Statistics



Surabaya, Indonesia


ABSTRACT

The North-American Multi Model Ensemble (NMME) is a multi-model seasonal forecasting system consisting

of models from combined US modeling centers. NMME is expected to generate better rainfall prediction than

single model. However, the NMME forecastsare underdispersive or overdispersive. Therefore, calibrationis

needed to produce more accurate forecasting. This research examined monthly rainfall data in Surabaya

generated by nine NMME models and further calibrated with Bayesian Model Averaging (BMA). The

purpose of this research is to assess the performance of calibration results using four best and full

ensemeble. The four models are CanCM3, CanCM4, CCSM3, and CCSM4 that were selected based on

their skills. Both calibration results are evaluated by CRPS and percentage of captured observations . The

calibration with four models produces average CRPS of 6.27 with 88.16% coverage, while with nine models

produces averagre CRPS 5.23 with 92.11% coverage. This result suggest of using full ensemble members

to generate more accurate probabilistic forecast.

Keywords: Bayesian model averaging; calibration; NMME.



ID 048

VARX and GSTARX Models for Forecasting Currency Inflow and Outflow

with Multiple Calendar Variations Effect

MUHAMMAD MUNAWIR GAZALIa, SUHARTONOb & DEDY DWI PRASTYOc


Faculty of Mathematics, Computation,and Data Science


Surabaya, Indonesia


ABSTRACT

VARX and GSTARX models are extension of Vector Autoregressive (VAR) and Generalized Space Time

Autoregressive (GSTAR) models by involving exogenous variable for increasing the forecast accuracy. The

objective of this research is to develop and compare the forecast accuracy of VARX and GSTARX models

in predicting currency inflow and outflow in Bali, West Nusa Tenggara, and East Nusa Tenggara,that

contain multiple calendar variations effect. The exogenous variables that be used in this research are some

holidays in those three locations, i.e. Eid ul-Fitr, Galungan, and Nyepi. Firstly, the proposed VARX and

GSTARX models are evaluated through simulation studies on the data that contains trend, seasonal, and

multiple calendar variations representing the occurrence of Eidul-Fitr, Galungan, and Nyepi. The criteria

for selecting the best forecasting model is Root Mean Square Error (RMSE). The results of simulation study

show that VARX and GSTARX models provide similar forecast accuracy. Furthermore, the comparison

results at currency inflow and outflow data in Bali, West Nusa Tenggara, and East Nusa Tenggara show

the best model for forecasting inflow and outflow inthese three locations are VARX and GSTARX (with

uniform weight) model, respectively. Both models show that currency inflow and outflow in Bali, West Nusa

Tenggara, and East Nusa Tenggara have relationship in space and time, and contain trends, seasonal and

multiple calendar variations.

Keywords: VARX; GSTARX; inflow; outflow; multiple calendar variations.



ID 049

Bidimensional Discrete- Time Risk Models based on Bivariate Negative

Binomial Moving Average Models

KODCHAPOWN LAPHUDOMSAKDAa & JIRAPHAN SUNTORNCHOSTb

a,bThe department of Mathematics and Computer Science

Faculty of Science

Chulalongkorn University, Bangkok

10330, Thailand


ABSTRACT

Risk model is important in the measurements of the aggregate net loss of an insurance company’s portfolio.

It also indicates the probability of losing of the company’s portfolio. The sufficiently precise is highly

necessary to the firms to decide their reserve amounts. There have been many studies attempting to

develop new models which are more suitable for real data. One of the significant components in the

risk model is the claim count distribution which is usually assumed to have the Poisson distribution.

However, the Poisson distribution has a drawback that its mean and variance are the same. This

assumption of Poisson distribution is rarely found in practice since observations are usually over-

dispersed. Consequently, extensions of Poisson model to other distributions also brought interests from

researchers worldwide. Moreover, extensions to deal with multiple businesses are also of interest.

Therefore, in this paper, we introduce a new class of bidimensional risk models based on a bivariate

negative binomial MA models. The new models can deal with over-dispersion and also allow correlated

variables. In our study, we derive some probabilistic properties such as generating function, moments,

expression of the adjustment coefficient and approximations to ruin probabilities. Moreover, numerical

examples are also discussed.

Keywords: bidimensional risk model; negative binomial MA(1); adjustment coefficient; bivariate negative

binomial.



ID 050

Performance Analysis of M/C3/1 Queues: An LPC Approach

ISNANDAR SLAMETa, NUR MEGAWATI AYUNINGTYASWAN SAPUTRIb, VIKA YUGI KURNIAWANc & ETIK

ZUKHRONAHd

a,b,c,dDepartment of Statistics


Universitas Sebelas Maret

57126 Surakarta, Indonesia

[email protected], [email protected], [email protected],


ABSTRACT

We consider M/C3/1 queueing system which is a queueing system when the arrival rate follows a Poisson

distribution, service rate is approximated by Coxian 3-phase distribution, and the service facility available

is one. This paper aims at deriving the queueing system properties of M/C3/1 in which busy period is not

complete using lattice path combinatorics approach. Analysis of this system for the transient condition is

important in assisting managerial decisions for optimality the use of resources. Through this approach, the

queue system is represented in the form of lattice path and the number of lattice paths is determined. As

the result, the probability function in the queue system M/C3/1 in this scenario is derived.

Keywords: M/C3/1 queues; LPC approach; Coxian.



ID 052

Bayesian Approach on Mixture Poisson Model for Analysing Spatial

Point Pattern of Primary Health Centres in Surabaya

TRI MURNIATIa, NUR IRIAWANb & DEDY DWIPRASTYOc






ABSTRACT

Primary Healthcare Centres (PHC) in Indonesia includes community health centres and primary clinics

are health facilities that became the first referral for Indonesian society who want to seek treatment.

Therefore, PHC location becomes very important for easy reach of society from all layers. But the reality

in the city of Surabaya as the second largest metropolitan city in Indonesia is still lacking in terms of PHC

equity. The PHC location tends to be in central Surabaya causing fewer outskirts to reach PHC faster.

The distribution of PHC location points can be viewed as a spatial point pattern that can be analyzed as

a Poisson point process. The varying distribution of PHC makes the process follow the Nonhomogeneus

Poisson Point Process (NHPP). It is a Poisson process that has spatially varying intensities. The NHPP

intensity estimation was developed through the mixture Poisson regression modelling with the number

of PHC per tessellation object as the response variable. Poisson Mixture model has been widely used

to overcome the overdispersion data count. In this paper, we use Bayesian analysis couple with Markov

Chain Monte Carlo (MCMC) to model the finite mixture Poisson regression to model the PHC data. The result

shows that there are two mixture components with three variables significantly involved in the model; i.e.

number of households behaving clean, linkage rate, and road length in good condition; that produce the

best model with the smallest BIC. Out of three variables only the variable number of linkages that has a

significant negative effect on the intensity which means the high linkage rate or low accessibility causes

the number of PHC tends to be small. The other variables have a significant positive effect which means a

high level of treatment requirement and adequate road facilities increase PHC availability.

Keywords: Bayesian analysis; mixture Poisson regression; overdispersion; PHC intensity; spatial point

process.



ID 053

Multivariate T 2 Control Chart based on James-Stain and Successive

Difference Covariance Matrix Estimators for Intrusion Detection

MUHAMMAD AHSANa, MUHAMMAD MASHURI b, HERI KUSWANTOc, DEDY DWI PRASTYOd &

HIDAYATUL KHUSNAe

a,b,c,d,eDepartment of Statistics

Institut Teknologi Sepuluh Nopember,

Surabaya, Indonesia



ABSTRACT

The swift development of Internet technology and its implementation in a computer network have caused

the rapid flow of information. This will allow the potential for a security hole that can increase crime in

cyberspace. Therefore, the security system from attacks by irresponsible parties is needed. One of the

mechanisms that can be used is the intrusion detection system. Intrusion detection is a process to monitor

the events taking place in a computer system or network and analyze the monitoring results to find signs

of intrusion. Statistical Process Control (SPC) has been widely used in many fields, both in industry and

services. SPC not only can be applied to monitor manufacture processes but also can be applied to the

Intrusion Detection System (IDS). The multivariate control chart that is often used in intrusion detection

system is Hotelling’s T 2. In this research, the Hotelling’s T 2 chart performance for intrusion detection is

improved using the Successive Difference Covariance Matrix to estimate the covariance matrix and James-

Stain estimator to estimate the mean vector. The control limits of the proposed chart are calculated using

Kernel Density Estimation. The performance of the proposed method using T 2 based on Kernel Density

Estimation control limit outperforms the other approaches both in training and testing dataset.

Keywords: T 2 control chart; James-Stain; successive difference covariance matrix; kernel density

estimation; intrusion detection.



ID 056

Boosting-Ensemble Support Vector Machine: Alternative Classification

Algorithm for Handling High Dimensional and Imbalanced in

Multiclass Microarray Data

SANTI WULAN PURNAMIa, RISKY FRASETIO WAHYU PRATAMAb, NUR SILVIYAH RAHMIc, IRHAMAHd, &

SANTI PUTERI RAHAYUe







ABSTRACT

The problem of high-dimensional and imbalanced data are two very interesting issues and recently get

attention many researchers to study. These problems are found in biology and medicine, especially on

microarray data. High-dimensional data is a condition data that have number of features more than the

number of data. This condition cause curse dimensionality which makes less classification accuracy.

While the imbalanced data is a condition data where the classes are not represented equally. This is

a problem in classification because the classifier tends to predict majority classes and ignore minority

class. Research on the above two problems usually are studied separately. In this paper we study

simultaneously to solve both problems. Ensemble Support Vector Machine is proposed to solve problem

of high-dimensional data. While boosting approach to overcome imbalanced data. Microarray data

whose various imbalanced ratio is assed. The proposed algorithm is compared with typical approach, i.e.

feature selection based to solve high dimensional data and sampling techniques based for imbalanced

data. For evaluation of performance methods, we use G–mean model and F measure. The results

show that Boosting-Ensemble Support Vector Machine obtained better performance compared than typical

approaches.

Keywords: boosting; ensemble support vector machine; high dimensional, imbalanced data.



ID 057

Multivariate CUSUM Control Chart Based on The Residual of

Multioutput Least Square SVR for Monitoring Water Quality

HIDAYATUL KHUSNAa, MUHAMMAD MASHURIb, SUHARTONOc, DEDY DWI PRASTYOd, & MUHAMMAD

AHSANe






ABSTRACT

The independent observation is one of the assumptions for most conventional control charts. Monitoring

serially dependent processes using conventional control chart yields high false alarm rate. To overcome

this problem, several statistical approaches have been developed to monitor autocorrelated processes.

Multioutput Least Square Support Vector Regression (MLS-SVR) has the ability to remove the effect of

autocorrelation by mapping appropriate multivariate input space to multivariate output space. The objective

of this paper is to develop Multivariate Cumulative Sum (MCUSUM) control chart based on the residual

of MLS-SVR model. The proper input selection in MLS-SVR model produces the residuals that satisfy

white noise assumption and follow multivariate normal distribution. Once the assumptions are fulfilled,

the control limit of the proposed chart is equivalent to the control limit of conventional MCUSUM chart.

Furthermore, the proposed control chart is successfully applied to monitor water quality. The proposed

control chart can detect the assignable causes in both water turbidity and chlorine residual data caused

by a broken pipeline.

Keywords: autocorrelation; control chart; multioutput least square SVR; multivariate CUSUM; water

quality.



ID 059

Multiscale Finite Difference on Real Space Ballistic Carbon Nanotubes

Field-Effect Transistor

CHEUN YUEN HAa, SU HOE YEAKb & MICHAEL LOONG PENG TANc


Faculty of Science




cDepartment of Electronics and Computer Engineering

Faculty of Electrical Engineering



[email protected]

ABSTRACT

This paper is focus on carbon nanotube field-effect transistor (CNTFET) which applied multiscale method

with finite difference (FD) and its numerical simulation capability. This research applied multigrid method

in fixed size nanotube length, 40nm, and the transistor channel (13, 0) intrinsic carbon nanotubes (CNTs).

We explored and compared the performance of CNTFET with multiscale FD and FD method with different

size of grid points. The comparison results show that the multiscale technique requires less complexity,

less computational time, higher current and get better valence band which is chosen fine grid of FD results.

Keywords: multiscale finite difference; carbon nanotubes; multigrid method.



ID 061

Gaussian Mixture Model and K-means for Clustering Heavy

Precipitation in Surabaya

KIKI FERAWATIa, HERI KUSWANTOb & TOMOHIKO TOMITAc



Sukolilo, Surabaya 60111, Indonesia


cFaculty of Advanced Science and Technology

Kumamoto University

Kumamoto, Japan

t–[email protected]

ABSTRACT

Precipitation in Indonesia is affected by many kinds of weather variability. Knowing the characteristics of

precipitation in the area will help on predicting heavy precipitation event. Characteristics of precipitation,

such as the shape or pattern is an important factor to predict extreme rainfall events, which can be

found on radar image. This paper applies gaussian mixture model for high dimensional data clustering

(hereafter denoted as HDDC) to cluster the shapes appearing in the radar images associated with heavy

precipitation events in Surabaya. Another method used for this analysis is k-means clustering method with

principal component analysis (PCA). Using ten minutes precipitation rate data, the Mean Residual Life Plot

(MRLP) suggests that the extreme event is characterized with the precipitation above 1.5 mm. There are

41 components chosen for k-means clustering, explaining 80,15% of variance in the data. Number of

cluster identified are too large compared to the number of observations. Meanwhile, by using the Bayesian

Information Criterion (BIC), the HDDC suggests 10 clusters to characterize the heavy precipitation patterns.

The analysis for both k-means and HDDC shows some inconsistency in terms of the cluster members, due

to the small sample size. However, the result of HDDC is more reasonable than k-means, and further

improvements on HDDC need to be developed to have more robust results.

Keywords: radar image; heavy precipitation; cluster; gmm; k–means.



ID 062

Parameter Estimation on Bivariate Poisson Conditional Regression

Models

SELLA AJI OKTARINa, MUHAMMAD MASHURIb& VITA RATNASARIc


Faculty of Mathematics, Computation and Data Science




ABSTRACT

Poisson distribution is a discrete probability distribution to calculate the number of events occurred

randomly within a given time interval or space. Based on properties of the Poisson distribution, this

distribution has been applied to count data modeling. The bivariate count models are used in cases

where two count variables are correlated and need to be jointly estimated. The bivariate Poisson is the

most widely used model for bivariate count models. Bivariate Poisson regression is a statistical method

for modeling a pair of the data counts that have a correlation with some predictor variables. If one of

the response variables is conditional of other response variable then the best model is Bivariate Poisson

Conditional Regression (BPCR) model. Conditional probability is used to obtain joint probability mass

function of BPCR. Joint density can be expressed as the product of marginal and a conditional distribution.

This study aims to estimate parameters of bivariate Poisson conditional regression models and apply

the model to infant mortality caused by low birth weight. This study used data from health profile of

Surabaya with observation unit are 31 sub-districts. Parameter estimation of BPCR model obtained by

Maximum Likelihood Estimation (MLE) method and the result of the likelihood equations have no closed-

form solution, thus it can be solved numerically by Newton-Raphson iteration algorithm. The result of

modeling is infant mortality due to low birth weight was affected by variable of percentage of the birth

attended by skilled health worker, percentage of pregnant women consuming FE3 tablets, percentage of

PHBS households and ratio of public health center.

Keywords: bivariate Poisson conditional; bivariate Poisson conditional regression; low birth weight; infant

mortality.



ID 064

Geographically Weighted Trivariate Generalized Poisson Regression

Model

PURHADIa, SUTIKNOb, BAMBANG WIDJANARKO OTOKc & SARNI MANIAR BERLIANAd



Surabaya, Indonesia

[email protected], [email protected], [email protected], [email protected]

ABSTRACT

In this study, Geographically Weighted Trivariate Generalized Poisson Regression (GWTGPR) model and

parameter estimation procedure are proposed. GWTGPR is trivariate generalized poisson regression model

which all of the regression parameters depend on the geographical location, and parameter estimation

is done locally at each location in the study area. The location isexpressed as a point coordinate in

two-dimensional geographic space (latitude and longitude). The Trivariate generalized poisson regression

model (TGPR) is the joint probability density function model of trivariate generalized poisson distribution.

Trivariate generalized poisson regression is a correlated trivariate version of the univariate generalized

poisson regression. Parameter estimation of TGPR model produces a global model for each observation

location. Interpretation of this global model assumes that each location has the same characteristics but

in some cases each location has different characteristics. The goals of this study are to estimate the

parameters of GWTGPR model and to determine the test statistics on hypothesis testing for regression

parameters. The analytical method for parameter estimation is maximum likelihood estimation (MLE) with

the weighting of geographical location. The weighting of geographical location factor can be constructed by

the weighting function which depends on the bandwidth parameter. The selection of optimum bandwidth

can use the criteria of Cross Validation (CV). The result showed that the maximum likelihood estimator

(MLEs) can not be determined in closed form, and it can be obtained by Newton-Raphson iterative method.

The test statistics for test of goodness of fit and the simultaneous test are WilkŠs likelihood ratio statistic,

and the test statistic for partial test is Wald statistic.

Keywords: GWTGPR model; MLE; the weighting of geographical location; Wilk’s likelihood ratio statistic;

Wald statistic.



ID 065

On the Classification Boosting in Imbalanced Data (Case Study:

The Acceptance of Bidikmisi Scholarship 2017 in East Java)

SINTA SEPTI PANGASTUTIa, KARTIKA FITHRIASARIb& NUR IRIAWANc


Faculty of Mathematics, Computation, and Data Sciences



[email protected], kartika [email protected], [email protected]

ABSTRACT

Most existing classification approaches assume the underlying training data set is evenly distributed.

In the imbalanced classification, the training data set as one majority class could be far surpassed the

training data set as the minority class. This became a problem because classification will tend to predict

the data come from the majority class compared to the minority class. As a result, the classification

of minority classes being underestimated and influencing performance evaluation criteria of classification.

One popular method in improving the performance classification of the imbalanced class that used recently

is SMOTE-Boosting that combine algorithms at data level i.e. SMOTE with ensemble method boosting.

This paper present a review on ensemble techniques with focus on two-class problem. Based on the

performance criteria of g-mean, unlike standard boosting the ensemble algorithm SMOTE-Boosting showed

better performance. It can be said that SMOTE-Boosting methods are quite successful to take advantage

of boosting algorithms with SMOTE. When boosting affect the performance classification of base classifier

by focusing on all data classes, the SMOTE algorithm alters the performance values of base classifier only

in minority classes.

Keywords: base classifier; boosting; g-mean; imbalanced classification; SMOTE.



ID 066

On the Comparison of Density Peaks Clustering Mixed (DPC-M) and

K–prototypes Algorithm for Clustering Mixed Large Data

LAILA QADRINIa, KARTIKA FITHRIASARIb & NUR IRIAWANc






ABSTRACT

The Clustering method in data mining differs from the conventional method commonly used for clustering.

The difference is that data mining has a high data dimension that can consist of tens of thousands or

millions of records with tens or hundreds of attributes. In addition to data mining data can consist of mixed

data types such as numerical and categorical data. The problems that are often encountered in clustering

analysis are numerical and categorical mixed data. This study aims to compare the results of clustering

methods from K-Prototypes and Density Peaks Clustering Mixed (DPC-M). These algorithms are applied

to cluster Bidikmisi scholarship applicants in East Java in 2017. In general, clustering validation can be

categorized into three classes, which are internal clustering validation, external clustering validation, and

relative validation. In this study we focus on internal and external cluster validity indexes, based on the

results of the research indicating that, overall, The DPC-M has better clustering results than K-Prototypes

Algorithm.

Keywords: density peaks clustering mixed; K–prototypes; mixed large data.



ID 068

Micro and Macro Determinants of Delisting and Liquidity in Indonesian

Stock Market: A Time Dependent Covariate of Survival Cox Model

Approach

DEDY DWI PRASTYOa, YURIKE NURMALA RUCYb, ADVENDOS D.C.SIGALINGGINGc, SUHARTONOd &

SOO-FEN FAMe



Institut Teknologi Sepuluh Nopember, Kampus ITS U Sukolilo,

Surabaya 60111, Indonesia


eDepartment of Technopreneurship

Universiti Teknikal Malaysia Melaka

76100 Durian Tunggal, Melaka, Malaysia

[email protected]

ABSTRACT

Cox model is a popular method in survival analysis. It has been adjusted for the cases of covariate vary

with time. Moreover, the time period of changing can be different among covariates, for example some

subject-specific attribute change more frequent than other global covariates. In this study, there are two

cases used as object of analysis, i.e. delisting time of manufacture companies in Jakarta Stock Exchange

(JCX) and delisting time of companies from 45 most liquid stocks in JCX (LQ45 index). The firm-specific

determinants are financial ratio calculated quarterly. In addition, there are two macroeconomics variable

considered as global covariate: Jakarta Composite Index (JCI) and Bank Indonesia interest rate (BI rate).

These two kinds of covariates change over time with different period. The empirical results show that JCI

is significant determinant for both delisting and liquidity whereas BI rate is significant for liquidity only.

The significant firm-specific financial ratios vary for delisting and liquidity.

Keywords: Cox model; delisting; liquidity; survival; time dependent covariate.



ID 069

Forecasting Exchange Rate Across Countries with Gold Price as

Exogenous Variable Using Transfer Function and Vari-X Model

ALHASSAN SESAYa, SUHARTONOb & DEDY DWI PRASTYOc


Faculty of Mathematics, Compution, and Data Science




ABSTRACT

Investors and collectors hold gold as protection for their savings and wealth at large. Gold does not

pay interest like treasure bonds or savings account but current gold prices often reflect increases and

decreases. This research aims to provide a model for understanding the relationship between exchange

rate and gold price across countries as key members in the export of gold in the world. Also, it finds

the best method to exchange rate and exogenous gold price data across countries by comparing the

forecast accuracy between transfer function and VARI-X. Three countries exchange rates are used as a

case study against the gold price i.e. Australia, Brazil, and South Africa. In this research, ARIMA model

is used for forecasting gold price data as an input for Transfer Function and VARI-X models. Transfer

function model only considers the relationship between gold price as input with the exchange rate in each

country, whereas VARI-X model also considers the interrelationship between exchange rates in these three

countries. Daily data is used for the period 1st June 2010 to the 28th February 2018. The Root Mean

Square Error (RMSE) is used as criteria for selecting the best model. The results show that VARI-X is

the best model for forecasting Australian exchange rate, whereas Transfer Function is the best model for

forecasting South African and Brazilian exchange rates.

Keywords: transfer function; VARI-X; exchange rate; gold price; RMSE.



ID 070

On the Comparison of Deep Learning Neural Network and Binary

Logistic Regression for Classifying the Acceptance Status of Bidikmisi

Scholarship Applicants In East Java

NITA CAHYANIa, KARTIKA FITHRIASARIb, IRHAMAHc & NUR IRIAWANd

a,b,c,dDepartement of Statistics





[email protected]

ABSTRACT

Neural Network and Binary Logistic Regression is a modern and classical data mining analysis tool that

can be used to classify data on Bidikmisi scholarship acceptance in East Java Province, Indonesia. One

form of Neural Network model available for various applications is the Backpropagation Neural Network

(BPNN). This study aims to compare the performance of the BPNN method as a Deep Learning Neural

Network and Binary Logistic Regression method in determining the classification of Bidikmisi scholarship

acceptance in East Java Province. After preprocessing data and dividing them into two parts, i.e. set

of testing data and training data, with 10-foldcross-validation procedure, the BPNN and Binary Logistic

Regression are implemented. The result shows that BPNN with two hidden layers is the best platform

network model. The classification accuracy resulted by these both methods is that BPNN with two hidden

layers is more representative with better performance than Binary Logistic regression. The BPNN is

recommended to be used to predict acceptance of Bidikmisi applicants yearly.

Keywords: Backpropagation Neural Network (BPNN); Bidikmisi; deep learning neural network; binary

logistic regression.



ID 071

Outlier Detection using Widening Window Based on Out of Window

Time Data

R MOHAMAD ATOKa, DEDY DWI PRASTYOb, IMAM SAFAWI AHMADc, AGUS SUHARSONOd, RYA SOFI

AULIYAe& NURSYA’BANI HENDRO PRABOWOf

a,b,c,d,e,fDepartment of Statistics






ABSTRACT

Usually reducing outlier affect to obtain better model. Generally, outlier detection using window time has

been developed based on data in window time range. Whereas modeling based on data which contain

outlier obtain potential bias model. Furthermore, outlier detection based on bias model result to bias

detection. Aim of this paper is to propose utilizing data out of window time range for reaching better result

of outlier detection. ARIMA data simulation contain outlier are modeled and outlier detection procedure is

implemented. Using window time, the outlier in-window-time-range and out-window-time-range is tried.

Comparison of both methods shows that out-window-time-range method is slightly better than in-window-

time-range.

Keywords: outlier detection; widening window; window time; out of window time; ARIMA.



ID 072

Modifying Product and Marketing Strategy to Keep Sustainability of

Batik Surabaya

R MOHAMAD ATOKa, SUHARTONOb, DEDY DWI PRASTYOc, MUHAMMAD SJAHID AKBARd & IMAM

SAFAWI AHMADe







ABSTRACT

UNESCO assigned batik as cultural heritage of world. The assignment is an international recognition,

unfortunately batik’s crafters is being difficult to market their product, with the result that modeling of

batik consumption is necessary. From the point of view volume of batik consumption, production’s large

factory data is used. On the other hand, argument which affect to costumer to choose batik is critical

issue. Result of this study are two important points. Firstly, using time series method necessity of batik

is constant with little fluctuation. Secondly, from the biplot can be concluded that price and ethnic pattern

(specific culture) are the main argument of people to choose the batik.

Keywords: modifying product; marketing strategy; batik; biplot; time series.



ID 073

MRI-Based Brain Tumor Segmentation Using Modified Stable Student–t

Burr (MSTBurr) Mixture Model With Bayesian Approach

ANINDYA APRILIYANTI PRAVITASARIa, MIFTAKHUL ARDI IKHWANUS SAFAb, NUR IRIAWANc,

IRHAMAHd, KARTIKA FITHRIASARIe, SANTI WULAN PURNAMIf & WIDIANA FERRIASTUTIg

aDoctoral Student of Department of Statistics




and Lecturer of Statistics Department,

Universitas Padjajaran

45363 Bandung, Indonesia

[email protected]

bUndergraduate Student of Department of Statistics




[email protected]

c,d,e,fDepartment of Statistics





[email protected]

gDepartment of Radiology, Faculty of Medicine

Universitas Airlangga


and Radiodiagnostic RSUD Dr. Soetomo


[email protected]

ABSTRACT

Finite Mixture Models have been developed for brain tumor image segmentation based on the Magnetic



Resonance Imaging (MRI) as a media. The goal is to get the best fit model with the appropriate

segmentation results to describe the region of interest (ROI). Image segmentation techniques with mixture

model are used to clustering pixels based on the same color intensity (grayscale). Many studies of

mixture models using asymmetric distributions, such as skew normal and skew-t distribution, have been

expanded. This is due to the fact that the data pattern in the MRI is not always symmetrical. This research

use some approaches to naturally and adaptively capture the data forms that will be able to accommodate

the skew and even the thicker tails than normal distributions. It is called the nearly-normal or neo-normal

distributions. MRI-based segmentation using MSTBurr distribution was proposed with the aim of creating

an adaptive segmentation method which able to follow the changing of the MRI data pattern distribution.

The optimization of the segmentation model are done by employing the Bayesian method couple with the

Markov Chain Monte Carlo (MCMC) approach, because the analytical solutions are considered complicated.

The results of the analysis demonstrated that the MSTBurr Mixture Model could capture the pattern image

of MRI brain tumor better than the original segmentation GMM approach.

Keywords: Bayesian; image segmentation; mixture model; modified stable student-t Burr; neo normal.



ID 074

Fernandez-Steel Skew Normal (FSSN) Mixture Model Using Bayesian

Approach for MRI-Based Brain Tumor Segmentation

ANINDYA APRILIYANTI PRAVITASARIa, MIFTAKHUL ILMI DINUL ISLAMIYAHb, NUR IRIAWANc,

IRHAMAHd, KARTIKA FITHRIASARIe, SANTI WULAN PURNAMIf& WIDIANA FERRIASTUTIg





And Department of Statistics



[email protected]





[email protected]


Faculty of Mathematics, Computation and Data Sciences




[email protected]

gDepartment of Radiology,

Faculty of Medicine, Universitas Airlangga




[email protected]

ABSTRACT

Detection of a brain tumor in Magnetic Resonance Imaging (MRI) is always challenging due to the gray



level comparison of tumor and normal tissue. The model-based clustering with Finite Mixture Model is

widely used to segment the tumor as the Region of Interest (ROI). Gaussian Mixture Model (GMM) has

started to be abandoned because, in reality, the symmetric distribution approach is less able to explain

the MRI data pattern. In addition, the use of symmetric distribution cannot compete for the model

parsimony of an asymmetric distribution to model the long and heavy tail pattern of data. It would

be more Gaussian mixture components are needed in the GMM. This study develops a mixture model

with asymmetric distribution, called Fernandez-Steel Skew Normal (FSSN). The FSSN is one of the Neo-

Normal distributions that can be skewed adaptively but still stable in its mode of distribution. Bayesian

couples with Markov Chain Monte Carlo (MCMC) approach are employed for estimating FSSN distribution

parameters numerically. The results indicate that FSSN mixture model has a better performance to

represent the data pattern of brain tumor MRI, more parsimony, and able to detect the brain tumor more

precisely than the original GMM approach.

Keywords: Bayesian; brain tumor; Fernandez-Steel skew normal; image segmentation; mixture model.



ID 075

Algae Concentration Dynamical Model for Performance Analysis and

Evaluation of Facultative Wastewater Treatment Ponds

SUTRISNOa, SUNARSIHb & DWI PURWANTORO SASONGKOc

a,bDepartment of Mathematics


Universitas Diponegoro, Jalan Prof. Soedarto, SH.

Semarang, Indonesia


cDepartment of Physics



Semarang, Indonesia

[email protected]

ABSTRACT

In this paper, we analyse and evaluate the performance of a facultative wastewater treatment ponds by

using a dynamical model of the algae concentration transport. The research was conducted in facultative

ponds at Sewon waste water treatment in Yogyakarta, Indonesia. The governed dynamical model was

consist of the dynamical equation of the algae concentration that involves the inorganic carbon, nitrogen,

phosphor, light intensity, temperature and PH. The obtained research result was shown that the process

of the pollutant degeneration in the observed facultative ponds can still be improved.

Keywords: facultative ponds; wastewater treatment; algae.



ID 076

MRI-Based Brain Tumor Segmentation Using Gaussian Mixture Model

(GMM) and Hybrid Gaussian Mixture Model - Spatially Variant Finite

Mixture Model (GMM-SVFMM) With Expectation-Maximization (EM)

Algorithm

ANINDYA APRILIANTI PRAVITASARIa, SANDRA FIRDA QONITAb, NUR IRIAWANc, IRHAMAHd, KARTIKA

FITHRIASARIe, SANTI WULAN PURNAMIf & WIDIANA FERRIASTUTIg





And Department of Statistics, Universitas Padjajaran


[email protected]





[email protected]






[email protected]

gDepartment of Radiology

Faculty of Medicine



and Radiodiagnostic RSUD Dr. Soetomo, 60286 Surabaya, Indonesia

[email protected]

ABSTRACT



A brain tumor is one part of the tumor in the nervous system. Various studies have been conducted to assist

medical personnel in dealing with brain tumors, one of them is by performing brain tumor detection through

image-based medical segmentation of MRI. In the case of MRI, segmentation is performed to separate the

Region of Interest (ROI) or segments that are considered important in the medical point of view, with

other segments (Non-ROI) including noise. The commonly used image segmentation method is the model-

based clustering with Gaussian Mixture Model (GMM). However, the weakness of GMM is that between

the pixels in the image are considered independent, so that the segmentation results do not have the

noise robustness in image segmentation. To minimize the negative effects of the noise, in this research we

will use the Markov Random Field (MRF) model which fully takes into account the spatial dependencies

between pixels. The proportion of label of pixels probabilities will be explicitly modeled as probability

vectors. At the same time, pixel component functions are also relatively related to neighboring pixels. This

scenario could be implemented as the GMM that is spatially limited by MRF, called the Spatially Variant

Finite Mixture Model (SVFMM), in which the initial parameter generated from the GMM, so the proposed

model is hybrid GMM-SVFMM. In the inference process, the maximum likelihood estimation method is used

to estimate the proposed model parameters using the Expectation-Maximization (EM) algorithm. The results

from the correct classification ratio (CCR) showed that MRI-based brain image segmentation couple with

hybrid GMM-SVFMM was able to provide more accurate results to separate the ROI with noise compared

to GMM, with CCR of 0.9876 for hybrid GMM-SVFMM and 0.8735 for GMM.

Keywords: Expectation-Maximization; GMM; Markov random fields; MR image segmentation, SVFMM.



ID 077

Process Improving in a Domestic Waste Water Treatment Stabilization

Ponds by Using Mathematical Optimization Approach

SUNARSIHa, DWI PURWANTORO SASONGKOb & SUTRISNOc

a,cDepartment of Mathematics



Semarang, Indonesia


bDepartment of Physics



Semarang, Indonesia

[email protected]

ABSTRACT

In this paper, a mathematical optimization approach is used to improve the pollutant degeneration process

in a domestic waste water treatment stabilization ponds. The optimized process in this research is the

pumping process that was modelled as a mathematical optimization problem. By using data collected in in

Sewon wastewater treatment in Yogyakarta, Indonesia, the pumping process is optimized and the optimal

decision was determined that can be applied to operate this waste water treatment pond.

Keywords: domestic wastewater treatment; optimization; pollutant transport.



ID 078

Gaussian Mixture Model with Expectation Maximization for MRI-Based

Segmentation to Build 3 Dimension Image on Brain Tumor Area

ANINDYA APRILIYANTI PRAVITASARIa, SITI AZIZAH NURUL SOLICHAHb, NUR IRIAWANc, IRHAMAHd,

KARTIKA FITHRIASARIe, SANTI WULAN PURNAMIf & WIDIANA FERRIASTUTIg





and Department of Statistics




[email protected]


Faculty of Mathematics, Computing, and Data Sciences



[email protected]

c,d,e,fDepartment of Statistic





[email protected]

gLecturer of Department of Radiology

Faculty of Medicine





[email protected]



ABSTRACT

A brain tumor is such an unnecessary mass that growing inside the central spinal canal. It gives different

clinical problem compared with other tumor cases because of its effects and limited therapy that is applied.

Primary brain tumor generally diagnosed in children and older adults. RSUD Dr. Soetomo Surabaya,

Indonesia, recorded the increasing brain tumor cases and it occurs most frequently in women. One of the

imaging methods for diagnosis is Magnetic Resonance Imaging (MRI) that based on computer simulation

of a human body with an approach of tomography. The imaging technique of MRI is nearly complex

because of the image result based on the amount and optimal selecting of parameters. Therefore, image

segmentation is needed in order to enhance the quality of MRI. Object segmentation in an image will

separate object region and background region for easier further analytic. Model-based clustering is one of

the methods that have been developed for image segmentation based on the probability model of the data.

Gaussian mixture model (GMM) is frequently used for this purpose, especially for image segmentation.

GMM is commonly used to segment an image resulted by positron emission tomography (PET), computed

tomography (CT), and any others clinical scan. The later research on MRI is only show the classification

of a brain with tumor area and show the region of interest (ROI). This research will build a dashboard

based on the MATLAB GUI and forms 3 dimensions’ model. After being clustered by GMM, slices of MRI

image result will be constructed as the 3D model visualized as a dashboard. With 3D model, the volume

of brain tumor hopefully can be known easily based on the clinical perspective. This dashboard will help

paramedic to know exactly where the brain tumor is located so diagnostic process can be efficient and

effectively detected.

Keywords: brain tumor; expectation maximization; Gausssian mixture model; image segmentation; MRI.



ID 079

On the Optimum MRI-Based Brain Image Segmentation with Spatially

Constrained Gaussian Mixture Model Using Reversible Jump Markov

Chain Monte Carlo

NURDIANTO ZAENURDINa, ARRY AKHMAD ARMANb & NUR IRIAWANc

a,bSchool of Electrical Engineering and Informatics

Institut Teknologi Bandung

Bandung, Indonesia






[email protected]

ABSTRACT

To improve the quality of MRI images with segmentation techniques, the Gaussian Mixture Model (GMM)

is very commonly used in model-based clustering. But in the conventional GMM, the relationship between

neighborhood pixels in an image is considered independent. This poses a risk that noise could be included

in a cluster with the Region of Interest (ROI). To solve this problem of pixel independence and noise

reduction, this paper proposes the use of Spatially Constrained Gaussian Mixture Model (SCGMM) as a

model-based clustering technique coupled with the Reversible Jump Markov Chain Monte Carlo (RJMCMC)

algorithm as the basis for choosing the optimum cluster on this clustering model. The RJMCMC method

is very flexible in determining the number of mixture components, since the vector dimensions of the

mixture model parameters, including the number of mixture components itself, is variable or unknown.

Implementation of clustering with SCGMM coupled with RJMCMC algorithm was performed on MRI-based

brain image of scanning using MRI machine with 1.5 Tesla power.

Keywords: image segmentation; mixture model; Gaussian mixture model; spatially constrained Gaussian

mixture; reversible jump Markov Chain Monte Carlo.



ID 080

Numerical Solution of Fractional Electrical Circuits by Haar Wavelet

SAHAR ALTAFa & SUMAIRA KHAN b

aCollege of Humanities and Sciences PAF-KIET Karachi

Institute of Economics and Technolgy

75190, Pakistan

[email protected]

bCollege of Computer Science and Information Systems

75190, Pakistan

Institute of Business Management (IoBM)

[email protected]

ABSTRACT

In this research, fractional electrical circuit are investigated. Electrical circuits are used in a number

of fields such as communication systems and signal processing. A wavelet technique named "Haar

wavelet" is used to solve fractional electrical circuit models. The fractional order operational matrix

based on Haar wavelet is established and applied to convert differential equations into a system of

algebraic equations involving unknown coefficients. To demonstrate the efficiency of the proposed method,

benchmark problems like LC, RL, RLC circuit equations are solved to obtain the numerical solution for

different values of fractional order α. The comparative analysis of numerical outcomes with the classical

ones reveals that the proposed method is computationally efficient and can be easily applied to solve other

fractional partial differential equations.

Keywords: fractional electrical circuits; Haar wavelet; operational matrix; differential equation.



ID 081

Wind Speed Forecasting Using Generalized Autoregressive Conditional

Heteroscedasticity Model Optimized by Genetic Algorithm

INDAH FAHMIYAHa, IRHAMAHb & HERI KUSWANTOc






ABSTRACT

Accurate wind speed forecasting is important to minimize the disaster impacts, such as air transport

accidents, collapsed buildings, fallen trees, etc. The most commonly used forecasting method is ARIMA.

This research revealed that the error term of the ARIMA models exhibit some degree of heteroscedasticity

which thus requires further steps to accurately predict the wind speed. In this case, Generalized

Autoregressive Conditional Heteroscedasticity (GARCH) model applied to model the variance of the residual

term. Genetic Algorithm (GA) is chosen as the method to optimize the estimation process of GARCH

model for wind speed recorded from Juanda meteorological station Surabaya. The GA yielded on a global

optimum solution. Furthermore, the RMSE of GA generated parameters is significantly lower than GARCH

parameters estimated by standard approach. It means that the GA increased the forecasting performance

of the GARCH model for wind speed.

Keywords: GARCH; genetic algorithm; wind speed forecasting.



ID 084

Linking Twitter Sentiment Knowledge with Infrastructure Development

by Text Mining

ZAKYA REYHANAa, KARTIKA FITHRIASARIb, MOH. ATOKc & NUR IRIAWANd

a,b,c,dDepartment of Statistic





[email protected]

ABSTRACT

This research aims to find out the opinion of infrastructure that sustains urban development in Surabaya,

Indonesia’s second-largest city. The data is obtained from Twitter by using a specific keyword from official

government’s twitter accounts namely @e100ss and @sapawargaSby. The procedures of text mining

analysis were the data undergoes some preprocessing first, such as removing the link, retweet (RT),

username, punctuation, digits, stopwords, case folding, and tokenizing. Then, the opinion was classified

into positive and negative comments. Classification methods used in this research were Support Vector

Machine (SVM) and Neural Network (NN). To obtain the best classification method, those methods were

compared by using three kinds of measuring instruments i.e. correct classified, precision, and recall. Out

of the two existing classes, word cloud was created to show the most common words of each class. The

result of this research showed that SVM classification method was better than NN. Word cloudŠs positive

class showed that the most mentioned words were highway, bridge, and roads. The negative ones, on the

other hand, were electricity, power outage, and clean water distribution. The conclusion of this research

suggests that Surabaya government should improve the quality of infrastructure by giving more attention

to electricity and clean water distribution.

Keywords: classification; support vector machine; neural network; text mining; twitter.



ID 085

Surabaya Government Performance Evaluation Using Tweet Analysis

RAKHMAH WAHYU MAYASARIa, KARTIKA FITHRIASARIb, NUR IRIAWANc & WIWIEK SETYA WINAHJUd

aUndergraduate Student of Department of Statistics




[email protected]

b,c,dDepartment of Statistics





ABSTRACT

Surabaya government has provided optimal services to the community. However, there are still some

members of the community who are not satisfied with the services. The purpose of this research is

to know the positive things that are appreciated by the public of Surabaya and the negative things

that need to be improved by the Surabaya government. The goal will be achieved by employing the

sentiment analysis to the commented data of the public of Surabaya obtained through the facility of

Twitter Application Programming Interface (API). The data collection is done on the official twitter account

of Surabaya government (@SapawargaSby) and Suara Surabaya Radio (@e100ss). Before running the

sentiment analysis, preprocessing data is done to the raw data by eliminating the noise. This elimination

could be as a removal process; i.e. deleting links, usernames, punctuation, case folding, stemming,

and stop word. Data cleaning by removing some special words that are not counted in the analysis of

sentiment; i.e. "SapawargaSby" and "e100ss", are also done as a second step. The sentiment analysis

methods, including the Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), and Binary Logistic

Regression (BLR), are employed to classify the pro-cons of Surabaya city services through the positive and

negative word in each tweet. The sentiment analysis will show the largest frequency which represents

the most highlighted in each classification. The comparison of three methods demonstrated that SVM

gives the best classification accuracy than the others. The highlighted word in the positive classification is

"comfort", while in the negative category is "traffic jam". Finally, there is an evaluation for the government

of Surabaya to keep Surabaya as comfortable city to live and improve performance in traffic.

Keywords: binary logistic regression; Naïve Bayes classifier; support vector machine; sentiment analysis.



ID 086

The Performance of Kernel Logistic Regression and Regularized

Logistic Regression using Truncated Newton on Imbalanced

Classification Problem

SANTI PUTERI RAHAYUaa, JASNI MOHAMAD ZAINb, & ABDULLAH EMBONGc






ABSTRACT

The main objective of this research is to evaluate the classification performance of Newton Truncated

Regularized-Kernel Logistic Regression (NTR-KLR) and Newton Truncated Regularized-Logistic Regression

(NTR-LR) on the imbalanced problem, including the proper use of evaluation metric between total accuracy

and g-means. The class imbalanced problem corresponds to domains for which one class is represented by

a large number of examples (majority class) while the other is represented by only a few (minority class).

NTR-KLR and NTR-LR implement the Truncated Newton (TN) method for solving the numerical problems of

the linear system on the Newton Raphson (NR) method. In addition, this paper studies the effectiveness of

Truncated Newton method in NTR-KLR and NTR-LR, the numerical convergence, the optimal classification

performance and its stability in both classifier algorithms. Hence, this research can be seen as further

explanations of the classification performance of KLR and Regularized LR (RLR) with TN method, especially

on imbalanced problem, which is the most classification problems to happen. In this study, the NTR-KLR

classifier is performed on six data sets which are small to medium scale data. Almost all data sets

are unbalanced data sets, except one data set is balanced data set as control. Meanwhile, the NTR-LR

classifier is applied on three large scale data sets which are unbalanced. All data sets were derived

from the UCI Machine Learning which includes the fields of social, finance, health, transportation and

other fields which relates to images segmentation. The research is conducted using 5-fold Stratified Cross

Validation (SCV). Results indicated that NTR-KLR and NTR-LR have almost equal value of sensitivity (the

accuracy of minority class) with specificity (the accuracy of majority class) value on balanced data set.

Unlike balanced data set, both classifiers have poor sensitivity values on unbalanced data sets. Other

study results showed that the g-means metric evaluates the accuracy of NTR-KLR such as total accuracy

metric on balanced data. On eight unbalanced data sets, g-means metric evaluates the accuracy of NTR-

KLR and NTR-KLR more proper than total accuracy metric. Numerical experiments have demonstrated

that the TN method performs effectively in solving the problems of the linear system of NR method on both

classifier algorithms, especially in the training time problem. Moreover, both classifier algorithms have

shown that the convergence criterion is fulfilled and have displayed optimum and stable classification

accuracy.



Keywords: imbalanced; kernel logistic regression; newton raphson; truncated newton.



ID 087

Frequency Model of Car Credit Payment using Bayesian Geometric

Regression and Bayesian Mixture Geometric Regression

IKACIPTA MEGA AYUPUTRIa NUR IRIAWANb & PRATNYA PARAMITHA OKTAVIANAc






ABSTRACT

In distributing funds to customers as credit, multi-finance companies has two necessary risks, i.e.

prepayment risk, and default risk. The default risk can be minimized by determining the factors that

affect the survival of customers to make credit payment, in terms of frequency of credit payments

by customers that are distributed geometry. The proposed modelling is using Bayesian Geometric

Regression and Bayesian Mixture Geometric Regression. The best model of this research is modelling

using Bayesian Geometric Regression method because it has lower DIC values than Bayesian Mixture

Geometric Regression. Modelling using Bayesian Geometric Regression show the significant variables are

marital status, down payment, installment length, length of stay, and insurance.

Keywords: Bayesian; credit payment; geometric regression; mixture model; multifinance company.



ID 088

Multivariate Time Series Forecasting using Hybrid Vector

Autoregressive - Neural Network for Coupled Roll-Sway-Yaw

Motions Prediction

NOVRI SUHERMA a, SUHARTONOb, SANTI PUTERI RAHAYUc, BAHARUDDIN ALId & FADILLA

INDRAYUNI PRASTYASARIe

a,b,cDepartment of Statistics,




dIndonesian Hydrodynamic Laborator

Badan Pengkajian dan Penerapan Teknologi


[email protected]

eDepartment of Marine Engineering



[email protected]

ABSTRACT

Floating Production Unit (FPU) has an important role in the production of offshore crude oil and gas.

Therefore, the stability of the FPU ship must be in control. The stability of a ship can be analyzed from the

behavior of their motions. The ship motions can be studied either in uncoupled system or in coupled system.

One of the coupling motion system that is frequently studied is roll-sway-yaw motions. In this study, we

form a multivariate time series model for predicting the roll-sway-yaw motions of a prototype FPU using

Hybrid Vector Autoregressive-Neural Network (VAR-NN) model. Hybrid VAR-NN combines the linear model

(VAR) and nonlinear model (NN) in order to capture both linear and nonlinear patterns simultaneously in a

data. The dataset is collected from the results of the FPU experiment using a generated waves in a beam

sea condition. We split the dataset into in-sample data and out-of-sample data. The in-sample data is

used to train the model. Then, the model is applied to predict the out-of-sample data. We also compare the

results of VAR-NN predictions to ARIMA, pure VAR, and pure NN models. The model selection is based on

out-of-sample criteria. We used Root Mean Square Error (RMSE) as the prediction performance measure.

The experimental results show that the Hybrid VAR-NN outperforms other models. The hybrid method is

successful in improving the prediction performance of the pure model.



Keywords: hybrid method; neural network; prediction; ship motion; vector autoregressive.



ID 089

Stock Daily Price Regime Model Detection Using Markov Switching

Model

WIWIK PRIHARTANTIa, DWILAKSANA ABDULLAH RASYIDb & NUR IRIAWANc

aDepartment of Business Administration

Faculty of Social and Political Science

Universitas WR Supratman


[email protected]





[email protected]





[email protected]

ABSTRACT

Changes in stock prices at any time can happen randomly in accordance with the will of the market.

The up-normal changes that are not just a moment or change over a certain period and repeated in the

other period can also occur, so that there is a shift in the pattern of changes in stock prices. This shift

is categorized as a changing of the model structure. Detection of such pattern changes can be predicted

using the Markov Switching Model (MSwM). Using the smallest size of the AIC model on stock price data

modeling using MSwM, it will be possible to determine how many Markov transitions occur in the stock

price movement. If in the daily price data found as much as the state Markov transition, then there are

as much as the number of state Markov transition of regime model within the daily price movement of the

stock. This method has been tested to be applied to daily stock price data in several sectors and shows the

results that the number of regime model couple with its transition probability can help investors in making

investment decisions.

Keywords: stock price; regime model; Markov switching model; AIC; transition probability.



ID 090

Better Bootstrap Confidence Intervals for Parameter Estimators of

Kriging Model Based on Semiparametric Bootstrapping

ELMANANI SIMAMORAa, SUSIANAb & ERI WIDYASTUTIc

a,b,cDepartment of Mathematic,


Universitas Negeri Medan

Medan Sumatera Utara, Indonesia


ABSTRACT

This research consider deterministic simulation to estimating parameters of kriging Model. Kriging on

the deterministic simulation is exact interpolation where there is no random error in the observed data.

Unfortunately, variance estimator of kriging prediction become underestimate of the true variance. The

semiparametric bootstrapping method is used to generate randomness of the observed data. As a result,

the parameter estimators of the kriging model becomes random which follows the certain probability

distribution. The histogram shows that the bootstrap sampling distribution of the parameters of kriging

model are symmetric and asymmetric (longer-tailed at left or right). The BCa bootstrap confidence interval

much better job than confidence intervals of percentile bootstrap and bootstrap-t. The BCa bootstrap

confidence interval has a distribution slight asimetric (longer-tailed right) than percentile bootstrap interval

(longer-tailed left). The bootstrap-t interval much worse than both intervals the exist on based measure of

the shape of distribution and the length of the interval.

Keywords: Kriging; semiparametric bootstrapping; deterministic simulation; Confidence Interval.



ID 091

An Approximate Solution of Fractional Kolmogrov-Petrovskii-Piskunov

Equations

SUMAIRA KHANa & SAHAR ALTAFb

aCollege of Computer Science and Information Systems

75190, Pakistan

Institute of Business Management (IoBM)

[email protected]

bCollege of Humanities and Sciences

PAF-KIET Karachi Institute of Economics and Technolgy

75190, Pakistan

[email protected]

ABSTRACT

In this paper, we proposed a semi-analytical method known as reduced form of Differential Transform

Method (RDTM) for the approximate study of Fractional Kolmogrov- Petrovskii-Piskunov Equations. The

method is a powerful and convenient analytical- approximate tool for various linear and nonlinear

equations that are involved in science, engineering and industrial applications. Some illustrative examples

are also given that exemplify the competence of the proposed scheme and provide accurate approximate

solution for nonlinear problems in comparison with the classical equation and Homotopy perturbation

method. The technique required minimum computational cost and revealed rapid convergence, which can

also be applied to other partial differential equations of fractional order for the analysis of their solutions.

Keywords: reduced differential transform method; Caputo derivative; fractional Kolmogrov-Petrovskii-

Piskunov equation.



ID 092

Text Mining for Identifying and Visualizing Topics of Citizen Opinion in

Media Centre Surabaya

SAIDAH ZAHROTUL JANNAHa, KARTIKA FITHRIASARIb, DEDY DWI PRASTYOc & NUR IRIAWANd

a,b,c,dDepartment of Statistics,

Faculty of Mathematics Computation, and Data Science




[email protected]

ABSTRACT

The specific jargon for building Surabaya city is "building the city civilization together with the society".

The goal of this research is to identify and visualize the topics of citizen opinion to make the city better.

Data used in this research is Surabaya citizen opinion in Media Centre Surabaya, Indonesia. The

topics were obtained by using clustering methods. The pre-processing data by cleaning the noise; i.e.

removing newlines, URL-link, username, digits, punctuation and stop-words, case folding, and tokenizing,

is primarily assigned to reach the goal. The optimum number of clusters was determined by using the

K-Means clustering with calculating the Silhouette value and the Sum of Square Error (SSE) of 2 until 10

clusters. The clusters that have the highest silhouette value and the smallest SSE was the most optimum

clusters. The result of this research showed that there were 8 clusters as the optimum clusters. Once the

optimum number of clusters were obtained, the clusters were visualized by using word clouds to highlight

the words that appear more frequently in 8 clusters. Regarding the visualized word clouds, the 8 topics

most mentioned by Surabaya citizen were clean water distribution, parking area, civil administration,

computer training by Surabaya government, electricity, roads, residence card, and government services.

The result demonstrated the information for the Surabaya government of which sector that citizen most

concerned about. Moreover, it showed that the public has the concern to solve the problems in Surabaya

together with the government.

Keywords: text mining; clustering; public opinions.



ID 097

Construction of Interval Prediction in GSTAR Model for Forecasting

Consumer Price Index

SUHARTONOa, DEDY DWI PRASTYOb, HERI KUSWANTOc, RISMA HAPSARId & MUHAMMAD HISYAM

LEEe

a,b,c,dDepartment of Statistics,



Sukolilo Surabaya 60111, Indonesia


[email protected]

eDepartment of Mathematical Sciences,

Faculty of Science



[email protected]

ABSTRACT

Consumer Price Index (CPI) is an index to measure the average change in prices in the group of goods and

services consumed by households in a certain period. CPI is a monthly data that are frequently influenced

by locations aspect due to correlation between regions in meeting the needs of certain goods or services.

There are two main objectives of this research, i.e. to develop interval prediction of Generalized Space Time

Autoregressive (GSTAR) model, and to obtain an appropriate GSTAR with exogenous variables (GSTAR-X)

model for forecasting CPI of foods at five cities in Sumatra Island, Indonesia. The exogenous variables that

be involved in this research are Eid ul-Fitr as calendar variation, natural disaster event, and rising fuel

prices events. Moreover, five cities in Sumatra Island that the CPI measured as a case study are Padang,

Pekanbaru, Jambi, Palembang, and Bengkulu. The results show that exogenous variables in ARIMA and

GSTAR, known as ARIMAX and GSTAR-X, respectively, significantly increase the forecast accuracy. In

this research, a normalization of inference partial cross correlation is used as spatial weight in GSTAR-X

model. Additionally, the results of GSTAR-X model also show that there is no linkage between CPI of foods

in these five regions. It means that the phenomenon of high food prices at five cities in Sumatra Island

only be influenced by some exogenous variables and it proves no spatial link between CPI at these cities.

Keywords: interval prediction; GSTAR; exogenous variable; consumer price index.



ID 098

Comparison of Nonparametric Classifications Approaches for Epileptic

Seizure Detection Based on Elecroencephalogram Signals

SANTI WULAN PURNAMIa, CARIANTO HOTTUA SIHOMBINGb, YOGA PRASTYA IRFANDIc, BAIQ SISKA

FEBRIANI ASTUTId, BAYU SAMUDRAe, DIAH PUSPITO WULANDARIf , WARDAH RAHMATUL ISLAMIYAHg

& ANDA IVIANA JUNIANIh






fDepartment of Computer Engineering

Faculty of Electrical Technology



[email protected]

gDepartment of Neurology

Faculty of Medicine

University of Airlangga

Campus C Surabaya, Indonesia

[email protected]

hDepartment of Marine Engineering

Shipbuilding Institute of Polytechnic Surabaya, Indonesia

[email protected]

ABSTRACT

Epilepsy is a disorder of the brain characterized by an enduring predisposition to generate epileptic

seizures and by the neurobiologic, cognitive, psychological, and social consequence of this condition. An

epileptic seizure is a transient occurence that effected symptoms due to abnormal excessive or synchronous

neuronal activity in the brain. Diagnosis of epilepsy can be utillized Elecroencephalogram (EEG) which is

reccorded brainŠs spontaneous electrical activity from multiple electrodes placed on the scalp. Usually

neurophysiologist detect epileptic seizure or other abnormalities present with visually inspected. Because

there is no definite criteron evaluated by the experts, visual analysis of EEG signals is insufficient. Thus



are leads to improper diagnosis of the diseases because of human error causing fatal to human life.

Therefore, some automatic computerized techniques have been used for this purpose. Therefore this paper

propose nonparametric classification approaches, i.e. k-Nearest Neighbour (k-NN), Support Vector Machine

(SVM), Naive Bayes (NB) and Desicion Tree (DT) to detect seizure of epilepsy based on EEG signals. The

preprocessing data involves filtering, decomposition and feature extraction. Some of statistical features

is extracted from EEG signals, including feature energy, maximum, minimum, variance, and entropy.

Experiments were conducted on publicly available data, whose clinical EEG. The results show that SVM

can achieve highest accuracy (98.83%) than others. The obtained accuracy using k-NN, NB and DT are

98%, 95.5% and 97.5%.

Keywords: epileptic seizure; EEG; nonparametric classification; SVM.



ID 099

Microarray Classification of Prostate Cancer Using Hybrid Support

Vector Machine – Genetic Algorithm (SVM–GA)

VIOLITA PERTIWIa, IRHAMAHb & PRATNYA PARAMITHA OKTAVIANAc



Institut Teknologi Sepuluh Nopember 60111 Surabaya, Indonesia

[email protected]

b,cDepartment of Statistics





ABSTRACT

Cancer is one of health problems in world. Prostate cancer is ranked fifth on causing death of human. In

particular, microarray technology has been applied to prediction and diagnosis of cancer, so it is expected

to detect tumors or cancers more early and precisely. In order to correctly classify individuals who are

infected with a tumor or cancer, selection of variables related to cancer should be appropriate. Usually in

microarray data, the number of variables is greater than the number of observations, so it is needed to be

classified by machine learning method. One of suitable methods is Support Vector Machine (SVM). SVM

is a machine learning that has been successfully used to solve classification problems in various fields.

The problem in SVM is the difficulty of determining the optimal SVM parameter. In this research, Genetic

Algorithm (GA) is used to select features and to optimize the parameter of SVM. GA is a population-based

search that can seek a global optimum solution. The results of analysis show that the GA-SVM method

gives better classification performance than SVM for prostate data. In addition, parameter optimization

using GA can improve the accuracy value in the data classification.

Keywords: feature selection; genetic algorithm; microarray; parameter optimization; support vector

machine.



ID 100

Genetic Algorithm for Feature Selection and Parameter Optimization in

Fuzzy Support Vector Machine: Case of Colon Cancer Microarray

Classification

ELOK FAIQOHa, IRHAMAHb, HERI KUSWANTOc & NLP SATYANING PRADNYA PARAMITAd





[email protected]

b,c,dDepartment of Statistics





ABSTRACT

Colon cancer is the second leading cause of cancer-related deaths in the world, hence research on this

topic needs to be undertaken with improvement. Recent advancement in microarray technology allows the

monitoring of the expression level of a large set of genes simultaneously. Microarray data is a type of high-

dimensional data with hundreds or even thousands number of genes (features), while usually the number

of patients observed (observations) is much smaller than the number of features. This study will use an

open source colon cancer microarray dataset containing two classes of genes; normal and tumor. The aims

of this study is to develop a classification model using fuzzy support vector machine (FSVM) hybridized with

genetic algorithm (GA) for classifying individuals based on gene expression. Fuzzy memberships will be

applied to SVM in order to deal with the case of imbalanced microarray data. Meanwhile, the role of genetic

algorithm is, firstly, to select the relevant genes as the features and, secondly, to optimize the parameter of

FSVM as GA is able to handle the problem of nonlinear optimization that has a high dimension, adaptable,

and easily combined with other methods. This study shows that FSVM does not always give higher

accuracy than SVM in the case of classifying imbalanced data. Moreover, the use of genetic algorithms

for parameter optimazation, was proven can increase SVM and FSVM classification accuracy. Further

research can explore different imbalanced datasets to compare FSVM and SVM of classification problem.

Keywords: feature selection; SVM, fuzzy SVM; genetic algorithm; microarray.



ID 101

Existence Conditions of the Two and Four Periodic Solutions of

Second-order Neutral Delay Differential Equations with Piecewise

Constant Arguments

MUKHIDDIN MUMINOVa & ZAFAR JUMAEVb

aDepartment of Mathematical Sciences,

Fakulty of Science


81310 UTM Johor Bahru, Johor, Malaysia

[email protected]

bDepartment of Mathematics and Information Communication

Samarkand Agricultural Institute, Uzbekistan

[email protected]

ABSTRACT

This paper provides a method of finding periodical solutions of the second-order neutral delay differential

equations with piecewise constant arguments of the form

x′′(t) + px′′(t− 1) = q(t)x

(2

[t+ 1

2

])+ f(t),

where [·] denotes the greatest integer function, p is nonzero constant, q and f are k- periodic functions

of t , where k = 2, 4. This reduces the k - periodic solvable problem to a system of k/2 + 1 linear

equations. Furthermore, by applying the well-known properties of a linear system in the algebra, all

existence conditions are described.

Keywords: differential equation; piecewise constant argument; periodic solution.



ID 104

Algorithm of Bayesian VAR on Spatio Temporal Disaggregation Method

SUCI ASTUTIKa, UMU SA’ADAH b, SUPRIATNA ADHISUWIGNJOc & RAUZAN SUMARAd

a,b,dDepartment of Statistics


Universitas Brawijaya Malang

65145, Indonesia


cDepartment of Electrical Engineering

State Polytechnic of Malang

[email protected]

ABSTRACT

The disaggregation method can generate low time-scale data (eg hourly data) of a high-time (daily) scale

that retains the properties of high-time data. A disaggregation method involving location and time is

called the temporal spatio disaggregation. Estimation of the spatio temporal disaggregation method can

be approximated by the Bayesian method through Markov Chain Monte Carlo (MCMC). In a previously

study, Astutik et al (2013) developed State-space Model on the spatio temporal disaggregation algorithm

to generate low-precipitation rainfall in the Sampean Bondowoso watershed. This disaggregation method

algorithm can produce low-time scale rainfall data (hourly) capable of describing the phenomenon of hourly

rainfall data in the Sampean Bondowoso watershed at the sample site. However, the results of this

algorithm have not been able to predict low-time rainfall data in other locations that are not sampled. It

is therefore necessary to develop a disaggregation method that is able to predict the low time-scale data

in other locations using Bayesian Vector Autoregressive (Bayesian VAR). Therefore, this study aims to

develop an algorithm of Bayesian VAR on the disaggregation of temporal spatio data involving two time

scales of high and low time scales. The results show that the algorithm can produce low time-scale data

that approximates the observed low-time data scale (based on MSE)

Keywords: Bayesian; disaggregation; VAR; MCMC.



ID 105

Generalized Interval-Valued Intuitionistic Hesitant Fuzzy Soft Set

ADMI NAZRAa, YUDIANTRI ASDIb, HAFIZAH RAMADHANIc, SISRI WAHYUNId, RISCHA DEVITAe &

ZULYERAf

a,b,c,d,eDepartment of Mathematics

Andalas University

Kampus Unand Limau Manis

Padang, INDONESIA 25163



fDepartment of Economic Social of Agriculture

Andalas University

Kampus Unand Limau Manis

Padang, INDONESIA 25163

[email protected]

ABSTRACT

Let U be a fixed set. An interval-valued intuitionistic hesitant fuzzy set (IVIHFS) on U is a set

G = {< x, (h(x), g(x)) > |x ∈ U}

in which h(x) and g(x) are two sets of some values in [0,1], denoting the possible membership degrees

and nonmembership degrees of the element x ∈ U to the set G, respectively, with the conditions, for all

x ∈ U 0 ≤ sup h(x) + sup g(x) ≤ 1. We denote IVIHFSU as the collection of all IVIHFS on U. Now let E be

a parameter set, F : E → IVIHFSU and α be a fuzzy set on A ⊆ E. Then Fα : A → IV IHFSU × [0, 1] is a

function defined as follows:

Fα(e) = (F (e), α(e)) = ({< x, (hF (e)(x), gF (e)(x)) > |x ∈ U

}, α(e)).

Then (Fα, A) is called a generalized interval-valued intuitionistic hesitant fuzzy soft set (GIVIHFSS) over

(U,A). This paper aims to extend the interval-valued intuitionistic hesitant fuzzy set to a GIVIHFSS.

GIVIHFSSs and some operations on GIVIHFSSs are defined and some of their properties are studied.

The authors define equality of two GIVIHFSSs, subset and super set of a GIVIHFSS, complement of a

GIVIHFSS, null GIVIHFSS, and absolute GIVIHFSS with examples. Soft binary operations like AND, OR

and also the operations of union, intersection are defined. De Morgan’s laws and a number of results are

verified in GIVIHFSSs theory.



Keywords: soft set; interval-valued intuitionistic hesitant fuzzy set; generalized interval-valued

intuitionistic hesitant fuzzy set.



ID 106

Comparing Structural Equation Modelling with Robust Covariances,

Asymtotic Distribution Free Estimator, and Generalized Least Square

Methods

RINI WULANDARIa, INA FAKHRIYANAb & ABDURAKHMANc

a,b,cDepartment of Mathematical


Universitas Gadjah Mada

55281 Yogyakarta, Indonesia


ABSTRACT

Structural Equation Modeling is a comprehensive combination of factor analysis and regression or path

analysis. It provides a very general and convenient framework for statistical analysis that includes

several traditional multivariate procedures such as factor analysis, regression analysis, discriminant

analysis, and canonical correlation, as special cases. In industrial application, SEM is very useful in

decision making. For example to determine the relationship between product quality, advertising factor,

and any other factor simultaneously in order to increase net benefit. In SEM, it is usually assumed that

the sample data follow a multivariate normal distribution, so that the mean and covariance matrix contain

all the information. Non-normality in Structural Equation Modeling (SEM) analysis has potentially lead to

misleading results. Non-normality data in this case is in terms of its kurtosis. In this paper, we compare

three methods for handling non-normality data, there are by using robust covariance matrix, ADF, and GLS

methods. We use robust Huber-M estimator to generate covariance matrix by iteration algorithm called

Iteratively Reweighted Least Square (IRLS) in some data with various kurtosis. MardiaŠs multivariate

kurtosis statistics are used to measure the non-normality of data. Finally we compare these three methods

using goodness of fit criterion by their value of chi-square statistic and RMSEA.

Keywords: SEM; ADF; GLS; robust.



ID 108

Comparing Singular Spectrum Analysis and Seasonal Autoregressive

Integrated Moving Average for Forecasting Seasonal Data

NOVIA NILAM NURLAZUARDINIa & HERNI UTAMIb

a,bDepartment of Mathematical


Universitas Gadjah Mada

55281 Yogyakarta, Indonesia


ABSTRACT

There are many data sets in the geosciences and economics are seasonal data sets. The appropriate

forecasting of data sets in this sectors give large impacts for the other real life sectors. Singular Spectrum

Analysis (SSA) is nonparametric technique which is free models, it means that SSA is free of all statistical

assumptions. While the seasonal autoregressive integrated moving average (SARIMA) is parametric model

which requires several assumptions in its applications. The performance of these methods has been

considered by applying it to a rainfall data set. In this study, we forecast the data with both of the

method and compared these results with RMSE and MAE. The results yielded by SSA give smaller RMSE

and MAE than SARIMA. It means that the SSA technique gives more accurate forecast than SARIMA.

Keywords: SSA; SARIMA; seasonal data.



ID 109

Genetic Algorithm for Feature Selection in Text Clustering

IRHAMAHa & NLP SATYANING PRADNYA PARAMITAb



Kampus ITS Sukolilo, Surabaya, Indonesia


ABSTRACT

Text clustering is one of important areas in text mining that may be used for different task, such as

grouping similar document or text; so that documents in the same cluster are more similar to each other

than those in different clusters. Feature selection is a basic step to improve the performance of text

clustering. A choice of good features along with good clustering methods is of paramount importance.

In this study, Genetic Algorithm is used to select features both in hierarchical clustering methods and

k-means. Genetic Algorithm is population-based search method that can seek global optimum and can

handle various objective functions. Then, a comparative study with other feature selection methods for

text clustering is performed. The methods have been tested on benchmark and real data set. The results

show that the use of Genetic Algorithm as feature selection is very promising since it produces improvement

in clustering performance and competitive to other feature selection methods.

Keywords: text clustering; feature selection; genetic algorithm; clustering methods.



ID 111

New Three Term Conjugate Gradient Method with Exact Line Search

NURUL HAFAWATI FADHILAHa, MOHD RIVAIEb, FUZIYAH ISHAKc & NUR IDALISAd

a,b,dJabatan Matematik dan Statistik

Fakulti Sains Komputer dan Matematik

Universiti Teknologi Mara (UiTM) Cawangan Terengganu

Kampus Kuala Terengganu, Malaysia

[email protected]; [email protected]; [email protected]

cFakulti Sains Komputer dan Matematik,

Universiti Teknologi Mara (UiTM)

Kampus Shah Alam, Malaysia

[email protected]

ABSTRACT

Conjugate Gradient (CG) methods have an important role in solving large scale unconstrained optimization.

Nowadays, Three Term CG method has become a research trend of the CG methods. However, the existing

Three Term CG methods could only be used with inexact line search. When exact line search is applied,

these Three Term will reduce to the standard CG method. Hence in this paper, a new Three Term that could

be used with exact line search is proposed. This new Three Term CG method satisfies descent condition

using exact line search. Numerical results show that this proposed method outperforms the well-known

classical CG and some hybrid methods. In addition, the proposed method is also robust in term of number

of iterations and computation time.

Keywords: conjugate gradient; three term CG method; exact line search; unconstrained optimization.



ID 112

Comparison of Semivariogram Models in Rain Gauge Network Design

MOHD KHAIRUL BAZLI MOHD AZIZa, FADHILAH YUSOFb, ZALINA MOHD DAUDc, ZULKIFLI YUSOPd, &

MOHAMMAD AFIF KASNOe

aFaculty of Industrial Sciences & Technology

Universiti Malaysia Pahang,

26300 Gambang, Pahang, Malaysia

[email protected]

bDepartment of Mathematical Sciences

Faculty of Science



[email protected]

cUTM Razak School of Engineering and Advanced Technology

Universiti Teknologi Malaysia, Malaysia

dInstitute of Environmental and Water Resource Management (IPASA)

Faculty of Engineering

Universiti Teknologi Malaysia, Malaysia

eDepartment of Electronic & Computer Engineering Faculty

Universiti Teknikal Malaysia

Melaka, Malaysia

ABSTRACT

The well-known geostatistics method (variance-reduction method) is commonly used to determine the

optimal rain gauge network. The main problem in geostatistics method to determine the best semivariogram

model in order to be used in estimating the variance. An optimal choice of the semivariogram model is

an important point for a good data evaluation process. Three different semivariogram models which are

Spherical, Gaussian and Exponential are used and their performances are compared in this study. Cross

validation technique is applied to compute the errors of the semivariograms. Rainfall data for the period

of 1975-2008 from the existing 84 rain gauge stations covering the state of Johor are used in this study.



The result shows that the exponential model is the best semivariogram model and chosen to determine the

optimal number and location of rain gauge station.

Keywords: geostatistics; semivariogram; rain gauge.



ID 114

Academic Preferences Based on Students’ Personality Analysis

NORATIQAH MOHD ARIFFa, MOHD AFTAR ABU BAKARb & ZAMIRA HASANAH ZAMZURIc

a,b,cSchool of Mathematical Sciences

Faculty of Science and Technology

Universiti Kebangsaan Malaysia

43600 UKM Bangi, Selangor, Malaysia


ABSTRACT

It is believed that the quality of education will increase with the knowledge and understanding of students’

behaviour and personality. The theory and techniques for measuring skills, abilities, attitudes and other

psychological traits are studied in the field of psychometric. One of the model used for studying students’

behaviour in Malaysian universities is the Sixteen Personality Factor (16PF) model. Based on the model,

the personality traits of students and their chosen academic program from a local university is analysed by

using cluster analysis through the method of k-means. It is found that there are distinct clusters where the

personality traits of students differ and these differences affect their tendencies to choose certain programs.

By knowing the desirable personality traits for each type of academic program, educators and researchers

could plan and explore the suitable methods to increase the academic performance of students.

Keywords: psychometric test; personality analysis; k-means clustering.



ID 115

Multiple Linear Regression Model of Rice Production using Conjugate

Gradient Methods

NUR IDALISA NORDDINa, MOHD RIVAIEb, NURUL HAFAWATI FADHILAHc, NUR ATIKAH MUSTAFAd,

ANIS SHAHIDAe & NUR HIDAYAH MOHD NOHf

a,b,c,d,e,fJabatan Matematik dan Statistik,

Fakulti Sains Komputer dan Matematik

Universiti Teknologi Mara (UiTM) Cawangan Terengganu

Kampus Kuala Terengganu, Malaysia


ABSTRACT

Regression is one of the basic relationship models in statistics. This paper focuses on the formation of

regression models for the rice production in Malaysia by analysing the effects of paddy population, planted

area, human population and domestic consumption. In this study, the data are collected from the year

1980 until 2014 from the website Department of Statistics Malaysia and Index Mundi. It is well known

that the regression model can be solved by using least square method. Since least square problem is an

unconstrained optimization, the Conjugate Gradient (CG) is chosen to generate a solution for regression

model and hence to obtain the coefficient value of independent variables. All calculations are done using

MATLAB programming. Results show that the CG methods could produce a good regression equation with

acceptable Root Mean-Square Error (RMSE) value.

Keywords: conjugate gradient; regression; rice production.



ID 117

Reduction of the Drag Coefficient on the Circular Cylinder with

Three Passive Controls with Re = 10,000

CHAIRUL IMRONa, BASUKI WIDODOb & TRI YOGI YUWONOb

a,bDepartment of Mathematical, FMKSD


Surabaya, Indonesia


cDepartment of Mechanical Engineering,


Surabaya, Indonesia

[email protected]

ABSTRACT

The addition of smaller objects placed at the front and/or rear of the main object allows to reduce the

drag force received by the main object. The main object is a circular cylinder and the small object is called

passive control. In this paper, two passive controls are used, the first passive controller is cylindrical type-I

and placed in front of the main object and the second passive control is placed behind the main object

which has three kinds of cylinders of type-I, elliptical cylinder and circular cylinder. The distance between

the passive control and the main object varies and the Reynolds number used is 10,000. We want to find

the magnitude of the drag coefficient received by the main object due to the second passive control, in other

words we want to find the effectiveness of both passive controls over the main object in obtaining the drag

coefficient. We also want to see the wake effect that occurs due to two passive controls.

Keywords: drag coefficient; passive controls; circular cylinder.



ID 118

Giffler and Thompson Algorithm and Disjunctive Programming as

Initial Solution for TABU search in Solving Job Shop Scheduling

Problem

K.L.WONGa, S.Z.NORDINb & R.AHMADc


Faculty of Science,




ABSTRACT

In this paper, tabu search has been selected to solve the job shop scheduling problem. Disjunctive

Programming (DP) with shortest total processing time (STPT) and longest total processing time (LTPT) rules

has been used to find the initial solution for tabu search. Furthermore, the type of neighborhood structure

used in tabu search is neighborhood structure 1 (N1). Comparison of results on different tabu list length

and with benchmark values will be done to investigate the effect of the proposed disjunctive programming

as initial solution for tabu search. In conclusion, DP is managed to produce an overall good result in job

shop scheduling.

Keywords: job shop scheduling; tabu search; disjunctive programming.



ID 119

Dynamic Analysis of the Mathematical Model for Microalgae

Production

MARDLIJAHa, PRISMAHARDI AJI RIYANTOKOb, LUKMAN HANAFIc & SUHARMADI SANJAYAd

a,b,c,dDepartment of Mathematics,


Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo-Surabaya

60111, Indonesia

[email protected], [email protected],


ABSTRACT

Consumer demand for fuel is increasing, while the suply of fuel is began dwindling. Therefore, it is

necessary to undertake an effort to develop a renewable energy alternative such as the utilization of

microalgae. Microalgae has four main components of substance i.e. carbohydrates, proteins, nucleic acids,

and lipid. The relatively high lipid levels can be used as a source of biomass with the help of sunlight,

glucose, nutrients, carbon dioxide, and water. The stability and control analysis has proofed to trials the

mathematical model for microalgae production. The result of the modified model with carbon dioxide cycle

is stable, and the control system for both of matrix dimension and rank are same condition. Therefore, the

optimal control will be found for carbon dioxide cycle.

Keywords: microalgae production; stability; control; carbon dioxide.



ID 120

An Analytic Valuation of A Deposit Insurance

ENDAH RM PUTRIa, VENANSIUS R. TJAHJONOb & DARYONO B. UTOMOc

a,b,cDepartment of Mathematics,





ABSTRACT

A deposit insurance is a measure to protect bank depositors fully or partly from the risk of losses caused

by the bank’s failure to pay its debts when due. The guarantor if the bank do not meet the payment since

the asset value of the bank is less than debt, is considered as a deposit insurance. Similar mechanism of

the insurance to the Europen put option model, motivates the use of Black-Scholes model in the valuation.

The deposit insurance model is solved using a Fourier transform method analytically. The numerical result

based on the solution confirms the one obtained by previous research.

Keywords: insurance; European options; Fourier transform; analytic.



ID 121

Time Series Regression and ARIMA Modeling for Forecasting Stock

Price Index in ASEAN Countries

AGUS SUHARSONOa, IMAM SAFAWI AHMADb, RADEN MUHAMAD ATOKc & ARYO WIBISONOd

a,b,cDepartment of Statistics,

Faculty of Mathematics, Computing and Data Sciences




dDepartment of Economy

Faculty of Economy

Wiraraja University

Sumenep 69451 Madura , Indonesia

[email protected]

ABSTRACT

Capital markets can be an indicator of the development of a country’s economy. The presence of capital

markets also encourages investors to make trade investments, therefore investors need information and

knowledge about which shares are better for the funds invested. One way of making decisions for short-

term investments is the need for modeling to forecast stock prices in the period to come. This research aims

at modeling the share price of Indonesia with ASEAN countries (Association of South East Asia Nations)

including developed and developing countries such as Malaysia, Singapore, Thailand, and Philippines.

These countries are the founders of ASEAN and have stock price indices. They have close relations

with Indonesia in terms of trade, especially exports and imports. The existence of capital markets in the

modern economy is inevitable for all countries in the world, not least in Indonesia. The high demand for

goods and services resulting from the increasing number of human beings in the world makes companies,

both engaged in services and trade, to be able to meet all the world’s desirable demand globally. As

Indonesia being one of the developing countries, the society’s need for goods and services is very high.

This is evidenced by the increasing number of new companies emerging in Indonesia, both domestic and

foreign, because of the potential market share in Indonesia. This is what supports the development of

capital markets in Indonesia that can be an alternative in the country’s economic development. Stock

price modeling in this research use time series analysis that is Regression Time Series and ARIMA

(Autoregressive Integrated Moving Average). The data used are the ASEAN share price index data. The

best model selection criteria are based on the Root Mean Square Error (RSME) value indicating that the time

series regression method provides a better level of accuracy for predicting currency circulation in Surabaya

than the ARIMA method does. The results of this study indicate that a more complex model does not

always provide a more accurate prediction than does a simpler model.



Keywords: ASEAN stock price; regression time series; ARIMA.



ID 122

Deep Learning for Sentiment Analysis

KARTIKA FITHRIASARIa, SAIDAH ZAHROTUL JANNAHb & ZAKYA REYHANAc


Institut Teknologi Sepuluh Nopember (ITS)

Surabaya, Indonesia


ABSTRACT

Social media is a tool that many people use to express opinions. Sentiment analysis for text content on

social media is very important to get public opinion information on government performance. The data

used are the public opinion from GovernmentŠs official social media in Surabaya, Indonesia. The data

are grouped into two categories: positive and negative sentiments. In this paper, deep learning methods

are proposed for sentiment classification modeling. The methods are Backpropagation neural network

(BNN) and convolutional neural network (CNN). The both methods are compared in terms of classification

accuracy. Data preprocessing needs to be done in order to improve the accuracy before the both modeling

approaches started. The step aims to transform unstructured text data into structured data. Preprocessing

performed include case folding, tokenizing, and stopword. After we applied the both algorithms, word

cloud is used for visualizing data to get the most frequent words for each class. Experimental results

indicate that the accuracy of sentiment classification with CNN is better than BNN.

Keywords: convolutional neural network; sentiment analysis; social media.



ID 123

Numerical Simulation of Fluid Flow Around Circular Cylinder

and Three Passive Control Modifications to Reduce Drag Coefficient

at Re= 500

AMIRUL HAKAMa, CHAIRUL IMRONb, BASUKI WIDODOc & TRI YOGI YUWONOd

a,b,cDepartment of Mathematical Sciences,



60111 Surabaya,Indonesia


dDepartment of Mechanical Engineering

Faculty of Industrial Technology



[email protected]

ABSTRACT

The flow around circular cylinder with three passive control modifications simulated numerically to

investigate the effectiveness of three passive controls for decreases the drag coefficient. The Navier-Stokes

equation for incompressible, viscous and unsteady fluid flows is solved based on SIMPLE (Semi Implicit

for Pressure Linked Equations) algorithms and discretized using finite different method. The effectiveness

of the modification of three passive controls is influenced by the passive control distance with the circular

cylinder. The first passive control is placed at a distance of 2.4D from the circular cylinder, while the

other two passive controls are placed behind the circular cylinder at a distance of 1.6D and 1.8D, and

symmetrical at angles 30o, 45o & 60o. Simulation performed at the Reynolds number 500 for laminar

flow. Fluid flow visualization, streamline profile, velocity profile at wake area and drag coefficient is

obtained at each variation of the distances and Reynolds number.

Keywords: drag coefficient; wake; circular cylinder; SIMPLE algorithm.



ID 124

Clustering of Rainfall Distribution Patterns using Time Series

Clustering Method

NORATIQAH MOHD ARIFFa MOHD AFTAR ABU BAKARb, SHARIFAH FARIDAH SYED MAHBARc & MOHD

SHAHRUL MOHD NADZIRd

a,bSchool of Mathematical Sciences



43600 UKM Bangi, Selangor, Malaysia


cPusat Operasi Cuaca & Geofizik Nasional

Jabatan Meteorologi Malaysia,

Kementerian Tenaga, Sains, Teknologi, Alam Sekitar & Perubahan Iklim,

Jalan Sultan, 46667 Petaling Jaya, Selangor, Malaysia

[email protected]

dSchool of Environmental and Natural Resource Sciences,


Universiti Ke-bangsaan Malaysia,

43600, UKM Bangi, Selangor, Malaysia.

[email protected]

dCentre for Tropical Climate Change System,

Institute of Climate Change

Universiti Kebangsaan Malaysia,

43600, UKM Bangi, Selangor, Malaysia

ABSTRACT

Rainfall series from 1970 to 2014 from 12 meteorological observation stations were used in this study to

cluster rainfall patterns in Peninsular Malaysia. Four methods of dissimilarity measures were examined

for their accuracy and suitability which are the Euclidean distance (ED), complexity-invariant (CID),

correlation-based distance (COR) and integrated periodogram-based distance (IP). The average silhouette

width (ASW) were used to determine the optimal number of groups for rainfall time series. Using Ward’s

hierarchical clustering method, this study found that time series of rainfall in Peninsular Malaysia can be

divided into four regions of homogeneous climatological zones.



Keywords: clustering; dissimilarity measures; rainfall patterns; peninsular Malaysia.



ID 125

Application of Genetic Algorithm for Large Scale Quadratic

Optimization of Probabilistic Supplier Selection Problem with

Inventory Management

WIDOWATIa, SUTRISNOb & R. HERU TJAHJANAc



Jalan Prof. Soedarto, SH, Tembalang

Semarang, Indonesia


ABSTRACT

This paper is considered to analyse how a classic metaheuristic optimization method, genetic algorithm

can be used to solve a large scale integer quadratic programming of a probabilistic supplier selection with

inventory management problem. The word "probabilistic" in this case refers to the problem that involves

some uncertain parameters approached by random variable (probabilistic parameter). We used the existing

mathematical model of probabilistic supplier selection problem with inventory management provided in

our previous works that only considering few number of decision variable then the occurred optimization

problem is a small scale problem that can be solved efficiently by analytical method or numerical method.

Then, in this paper we resolved this model with huge number of decision variable indicated by the number

of the supplier and time period that is large by using an existing metaheuristics method, genetic algorithm

to analyse how the decision variable, is it reliable to be used or not. We generate some random data to

simulate the problem and analyse the results. From our computational experiment, the optimal decision

variables obtained by the genetic algorithm is acceptable to be used as the decision that can be used to be

applied by the decision maker.

Keywords: genetic algorithm; supplier selection; inventory management; large scale optimization.



ID 126

Probabilistic Approach for Failure Mechanism of Offshore Structures

Subjected to Ship Collision

YOYOK SETYO HADIWIDODOa, DANIEL MUHAMMAD ROSYIDb & RIANJAR HAKIMc

a,b,cDepartment of Ocean Engineerng

Faculty of Marine Technology


Surabaya, Indonesia

[email protected]

ABSTRACT

The failure mechanism of a jacket offshore structure due to ship collision will be analyzed using

probabilistic approach. Ship collision load will affect the integrity of the structure, in the form of plastic

deformation at the element level. Collision energy is simulated with monte carlo simulation approach.

This research is conducted on offshore oil platform of jacket that operates in Indonesian waters. Three

parts of the structure observed are conductor, jacket leg and Caisson. The results of this study found that,

the probability of failure of 0.648 for Conductor, 0.137 for Jacket and 0.009 for Caisson. The probability

of failure at conductor is higher than the other two components, because the conductor has the smallest

diameter, followed by jacket leg and caisson respectively.

Keywords: probabilistic approach analysis; jacket offshore structure; ship collision load.



ID 127

A Mathematical Proof of Explicit Formulas for the Coefficients of

Finite Difference Approximations of Second Derivatives

HAVID SYAFWANa, YULIADI YUNA SUTRAb, RAHMI ALKHAIRIc, MAHDHIVAN SYAFWANd, WILLIAM

RAMDHANe & RIKI ANDRI YUSDAf

a,e,fDepartment of Informatics Management Academy of Management

Informatics and Computer of Royal

Kisaran 21222, Indonesia


b,c,dDepartment of Mathematics


Andalas University, Limau Manis

Padang 25163, Indonesia


ABSTRACT

Explicit formulas for the coefficients of finite difference approximations of first and higher derivatives in

any order of accuracy have been presented by Khan and Ohba in [J. Comput. Appl. Math. 107 (1999)

179-193]. They also have provided a mathematical proof of the formulas for first derivatives. In this paper,

we complete the proof for second derivatives. The proof is constructed based on Taylor series and employs

some properties of Vandermonde determinant which is not found in the proof for first derivatives. A

computer program written in MATLAB is also given, which can be implemented in many industrial problems

involving second derivatives.

Keywords: finite difference approximations; Taylor series; Vandermonde determinant.



ID 128

Modeling of Clean Water Consumption using Tobit Double Hurdle Model

ISMAINI ZAINa, ERMA OKTANIA PERMATASARIb, I NYOMAN BUDIANTARAc & PUTU GDE ARIASTITAd




Sukolilo Surabaya 60111, Indonesia


dDepartment of Urban Planning


Kampus ITS Sukolilo


[email protected]

ABSTRACT

Modeling of clean water consumption is one of the main research topics in Sustainable Development Goals

(SDGs). Zero data in dependent variable as clean water consumption is one of the problems that frequently

occur in the modeling of clean water consumption. The objective of this research is to apply the Tobit Double

Hurdle Model for explaining the relationship between factors affecting household expenditure for clean

water consumption, particularly for poor and non-poor households. Raw data from SUSENAS 2017 for East

Java on a household scale was used as a case study. This research uses some independent variables, i.e.

the head of household education, household status, location of residence, number of household member,

drinking water source, main water source for cooking, main water source for bathing and washing, and

total monthly expenditure. The result of data analysis shows that there are many zero values data for

household expenditure for clean water consumption variable. Moreover, the application of the Tobit Double

Hurdle model yields a different model for poor and non-poor households, particularly on factors that affect

expenditure of clean water consumption. In addition, the results of Tobit Double Hurdle model can also

show areas in East Java that have problems in accessing clean water.

Keywords: clean water consumption; Tobit Hurdle model; censored data.



ID 129

Variational Approximation for Intersite Dark Solitons in a Discrete

Nonlinear Schrödinger Equation

MAHDHIVAN SYAFWANa, ZITA PUTRI NETRISb, IFRIANI BAKRIc, RIZA ASFAd & AZZAHRO FITRI AZADIe



Andalas University, Limau Manis

Padang 25163, Indonesia



ABSTRACT

In this paper, the method of variational approximation (VA) is applied to find stationary intersite dark

solitons in a discrete nonlinear Schrödinger equation with defocusing nonlinearity. The equation can be

realized, e.g., as an array of optical waveguides made of a Kerr material. Our study is carried out in the

vicinity of the anticontinuum limit, and a trial function that can capture the nature of the soliton profile

in the considered region is proposed. The variational results are then compared with the corresponding

numerical calculations. From the comparisons we obtain that the VA provides a good estimate for the dark

solitons.

Keywords: variational approximations; intersite dark solitons; discrete nonlinear Schrödinger equation;

anticontinuum limit.



ID 130

Grouping Districts/Cities Based on Environment Sanitation Indicators

as Basic Evaluation of SDGs Goals achievement in East Java Province

ERMA OKTANIA PERMATASARIa & ISMAINI ZAINb



Indonesia


ABSTRACT

Environmental sanitation is the health status of an environment that includes housing, sewerage, and

clean water supply. Guaranteeing the availability and management of sustainable water and sanitation

is one of the global goals of Presidential Regulation Number 59 of 2017 signed by President Joko Widodo

on July 4, 2017, to fulfill the government’s commitment in achieving the achievement of Sustainable

Development Goals (SDGs). East Java Province, as one of the provinces that carry out Presidential

Regulation Number 59 of 2017, make efforts to improve the condition of environmental health and basic

sanitation by implementing Total Sanitation Based Community (STBM). STBM is an activity that focuses

on integrated preventive and promotive efforts to trigger and maintain the sustainability of changes in the

behavior of clean and healthy living communities. Despite efforts to improve environmental health and

basic sanitation conditions have been done, but there are still gaps in some districts/cities in East Java

Province. Suppose for a healthy house, the coverage for Trenggalek District that fulfill for healthy house is

93,97%, while for Sumenep District is 6,69%. The percentage of districts/cities that have access to proper

sanitation, with the best sanitation access is Madiun City of 100% and the lowest is Situbondo District

of 13%. Based on the problem, this research was done for grouping districts/cities in East Java Province

based on environmental sanitation indicators, so it is expected to provide information about the groups

of districts/cities that have good access and the less good of environmental sanitation. This information

can also be a reference for East Java Provincial Health Office in making policies or improvement efforts

for regions belonging to districts/cities that have poor environmental sanitation access. So that, one of

the goals of SDGs, that is good water and sanitation can be achieved. The grouping was done using two

methods. For continuous data, the grouping was done using cluster hierarchy method and for category

data, the grouping was done using robust clustering using links method. The results show that the

optimum grouping was three groups.

Keywords: sanitation; districts/cities grouping; east Java province.



ID 131

A New Test of Discordancy in Cylindrical Data

IBRAHIM MOHAMEDa, N.H. SADIKONb, A.I.N IBRAHIMc & K. SHIMIZUd

a,b,cInstitute of Mathematical Sciences

Faculty of Science



[email protected]

dSchool of Statistical Thinking

The Institute of Statistical Mathematics

Tokyo, Japan

ABSTRACT

Cylindrical data are bivariate data from the combination of circular and linear variables. However, up to

now no work has been done on the detection of outlier in cylindrical data. We introduce a definition of

outlier for cylindrical data and present a new test of discordancy to detect outlier in this type of data,

based on the k-nearest neighbor’s distance. Cut-off points of the new test statistic based on the Johnson-

Wehrly distribution are calculated and its performance is examined using simulation. A practical example

is presented using wind speed and wind direction data obtained from the Malaysian Meteorological

Department.

Keywords: cylindrical data; outlier; neighbor’s distance.



ID 132

A Modified Model-Selection Criteria in Generalized Estimating

Equation for Latent Class Regression Model

JERRY DWI TRIJOYO PURNOMOa





[email protected]

ABSTRACT

In recent years, generalized estimating equation (GEE) plays an important role in many fields of research,

such as biomedical. In this paper, we use GEE for latent class models with covariate effects on underlying

and measured variables. However, there merely a few model-selection criteria in GEE. The widely known

Akaike information criterion (AIC) cannot directly be used since AIC is a full likelihood-based model, while

GEE is nonlikelihood based. Hence, we propose a modification to AIC in GEE for LCR models, where the

likelihood is replaced by the quasi-likelihood, and a proper adjustment is made by giving penalty term.

The data of the modified hospital elder life program (mHELP) project are used to illustrate our method

Keywords: generalized estimating equation; latent class regression; quasi-likelihood.



ID 133

Assessing Dynamic-Time-Warping Dissimilarity Measures in

Regionalization of River Discharges

NUR SYAZWIN MANSORa, NORHAIZA AHMADb & ARIEN HERYANSYAHc


Faculty of Science




cDepartment of Civil Engineering

Faculty of Engineering,

Universitas Ibn Khaldun Bogor

Gedung Fakultas Teknik "Ir. H. Prijantono"’

Jalan KH Sholeh Iskandar KM.2,

Kedung Badak, Tanah Sereal,Kota Bogor

Jawa Barat 16162, Indonesia

[email protected]

ABSTRACT

Regionalization of river discharges is a process of transferring hydrological information to generalize

hydrological information from one river to another. One approach used for this purpose is using a distance-

based regional analysis by employing a Dynamic Time Warping (DTW) dissimilarity measure to cluster

homogeneous river flow patterns based on sequenced o time series discharge data. However, clustering

homogeneous river flow patterns can be sensitive to the choice of distance metric measures used due to

out of phase behavior in the flow time series. In this study, we assess three types of Dynamic Time

Warping (DTW) measures specifically conventional DTW, a feature based DTW and a weighted based DTW

on four annual flow time series from six rivers in the state of Johor in Malaysia by comparing eight different

clustering validation indexes to determine the optimal number of rivers clusters with similar flow patterns.

These indexes are used to measures the internal and external strength of the identified clusters. The

results show that weighted based DTW shows the best method regarding the highest consistency in the

validation procedures with the same number of clusters. By using weight as a function in DTW, it helps

to cater the out of phase behavior in river flow time series with the highest consistency compared to other

types of DTW measures. We also found that three of the rivers (Sayong, Bekok, and Segamat) have similar

river discharge patterns and could be used together in the generalization process. Meanwhile, the other

rivers (Johor, Kahang, and Muar) varies in their time series patterns.



Keywords: dynamic time warping; clustering; dissimilarity measure.



ID 134

Bayesian Models for Small Area Estimation of Binary Responses

based on Unequal Probability Samples

A.TUTI RUMIATIa & KHAIRIL ANWAR NOTODIPUTROb




[email protected]

bInstitute of Mathematical Sciences

Institut Pertanian Bogor

Bogor, Indonesia

[email protected]

ABSTRACT

It is common in practice that random samples are obtained through equal probability selection methods in

which each element of the population has the same probability to be selected. However, there are situations

in which this idea of equal selection probabilities does not appear reasonable especially if some elements

need to be given a greater chance to be selected since they carry much more information about the target

variable. Such situations often happen in Small Area Estimation (SAE) problems and they have to be taken

into account while developing the models. This paper discusses such issues and proposes a proper model

for the estimation of parameters assuming that the response variable is binary. The proposed model is

the unit level model taking into account the unequal probability sampling methods. The empirical Bayes

approach has been utilized to estimate the parameters of interest as well as the predictors. The proposed

SAE model has been applied to estimate literacy rates at sub-district levels whereas the auxiliary variables

used were demographic variables including age (divided into five categories) and gender. To understand

the behavior of the estimators, a simulation study was conducted by selecting 100 samples through a two-

stage sampling method with unequal probability of selection. These samples were taken from the Indonesia

population census conducted in 2010. The results showed that the proposed method has provided small

biases and mean square errors (MSE). It also revealed that inclusion of sampling probability in the form of

an exponential function on the SAE model produced a very small bias but a high MSE when compared to

the SAE model based on a weighted average of the logistic mixed model.

Keywords: inclusion of probability sampling; binomial responses; empirical Bayes method; literacy rate;

logistic mixed models.



ID 135

Estimation and Model Reduction of Water Level in Bengawan Solo river

DIDIK KHUSNUL ARIFa, DIEKY ADZKIYAb & HELISYAH NUR FADHILAHc




60111 East Java, Indonesia


ABSTRACT

Indonesia is a maritime country with 2/3 of the area is water in the form of sea, lake, and river. River is one

form of water that is useful for the life of Indonesian citizens. Furthermore, the river can also be disastrous

if the volume of water in the river exceeds its capacity. Flood is a disaster on such event. So in anticipation

of flooding due to the inability of the river to accommodate the increase in water volume, we estimate the

river water level by taking into account the flow velocity using an estimator. As a case study, we observe

the Bengawan Solo river, one of the longest rivers in Indonesia that often cause disastrous flooding. With

a river length of 548 km, it is difficult to obtain complete data at each point, as well as this will lead to a

model of river flow with a large magnitude. Thus, we estimate the water level by using Kalman Filter at

unknown altitudes, and then we apply the model reduction by using Singular Perturbation Approximation

to reduce the computational time.

Keywords: estimation; Kalman filter; model reduction; singular perturbation approximation; Bengawan

solo river.



ID 136

Modeling Study of Food Necessity Forecasting in Indonesia

SETIAWANa & R. MOHAMAD ATOKb


Faculty of Mathematics,Computing and Data Science


Surabaya, Indonesia


ABSTRACT

Food necessity increases due to the growing number of population. Nowadays, the necessity of food are

still able to be fulfilled in Indonesia. However, food crisis could possibly happen in the future. The growing

number of population and the switching function of production land are said to be the reasons of food

crisis. Therefore, to overcome these issues, it is necessary to forecast the food necessity, especially on the

aspects of consumption and production. This research was aimed to modelling the food necessity forecast

in Indonesia. The data used was secondary data of yearly series of time series data. The methods used

on this research were Trend Analysis, ARIMA, and ARIMAX. The results of the analysis indicated that the

best model to forecast the food necessity in Indonesia is ARIMAX. Due to its least score of mean absolute

percentage error (MAPE).

Keywords: food necessity; ARIMAX; MAPE.



ID 137

A Comparative Assessment of Classification Methods for RNA-Seq Data

SETIA PRAMANAa & SITI MARIYAHb

a,bCenter for Computational Statistics Studies

STIS Polytechnic of Statistics

Jakarta Indonesia


ABSTRACT

Next Generation Sequencing or massively parallel sequencing have revolutionised genomic research. RNA

sequencing (RNA-Seq) can profile the gene-expression used for molecular diagnosis, disease classification,

and providing potential markers of diseases. For classification of gene expressions, several methods

that have been proposed are based on microarray data which is a continuous scale or require a normal

distribution assumption. As the RNA-Seq data do not meet those requirements, these methods cannot

be applied directly. In this study, we compare several classifiers including K-Nearest Neighbor, Logistic

Regression, Suppport Vector Machine, Random Forest, Boosting Tree, classification and regression trees

(CART), CHAID, Bagging Tree. A simulation study with different parameters such as over dispersion,

differential-expression rate is conducted and the results are compared with two mRNA experimental

datasets. To measure predictive accuracy five performance indicators are used: percentage correctly

classified, AUC, partial gini Index, H-measure, and Brier Score. The result shows that random forest

outperform the other methods.

Keywords: classification; RNA sequencing; gene expression.



ID 138

Temperature and Humidity Forecast via Univariate Partial

Least Square and Principal Component Analysis

SUTIKNOa, ZAHROTUN NISAA’b & KARTIKA NUR ’ANISA’c



Institut teknologi Sepuluh Nopember, Kampus Sukolilo



ABSTRACT

The accurate and fast weather forecast is currently of much importance. Weather is considered to be

the part that cannot be separated from human activity such as in agriculture, fishery, and transportation.

Indonesian official agency regarding Meteorology, Climatology, and Geophysics (BMKG) is regularly issuing

weather forecast information such as temperature and humidity, but the accuracy relies the most on the

forecasters. In recent years, this agency uses Numerical Weather Prediction (NWP) data to do a short-term

forecast but the forecast shows bias result. Thus, this study implements a statistical process to the NWP

data using Model Output Statistics (MOS), a relation modeling between the weather observation result and

the NWP output that based on regression. The weather observation results, such as maximum temperature

(Tmax), minimum temperature (Tmin), and humidity (RH) are the response variables. NWP data is the

predictor variable that contains 32 variables within 9 measurement grids. The predictor variables are

reduced using Principal Component Analysis (PCA). The PCA result shows that most of the predictor

variable will have one principal component. These components are analyzed using Univariate Partial Least

Square (PLS 1) to obtain the prediction model. The result shows the performance of the prediction model is

considered to be good and intermediate based on the Root Mean Square Error of Prediction (RMSEP). The

prediction of maximum temperature on four stations has an intermediate criterion, prediction of minimum

temperature on three stations has a good criterion, and humidity on three stations has a good criterion. The

best Percentage Improval (%IM) is obtained on minimum temperature because the PLS 1 model is capable

to correct 89.94% of biased NWP.

Keywords: MOS; NWP; PCA; PLS 1; temperature and humidity.



ID 139

Named Entity Recognition Application for Short Message Service Text

In Business Corporation

SITI MARIYAHa, RANU YULIANTOb & SETIA PRAMANAc

a,cCenter for Computational Statistics Studies,

STIS Polytechnic of Statistic

Jakarta, Indonesia


bBPS Statistics Indonesia, Kabupaten Wakatobi

Sulawesi Tenggara, Indonesia

[email protected]

ABSTRACT

Texts are generated every second, almost every transaction that we do. Texts are either in the form of

digital or non-digital. Even a small organization generate large amounts of text every day. The text can

come from many sources such as internal documents, reports, forms and notes in the customer relationship

management, short message service (SMS) text, customer’s emails, customer’s social media, etc. Texts

owned by an organization can provide early warning of risk and compliance issues. They will bring

new insight into how the customer acts, what the interest of each customer, how the customer consumes

the business product and what the customer. They offer a detailed understanding of business that will

drive better decisions or the opportunity to create new business products which match the needs of the

customers. The challenge is how to take out the potential benefit of text, and this is not an easy task.

Text mining is a solution. In this paper, we report our research in developing text mining application

namely named entity recognition (NER) applied to the collection of short message service owned by a

business corporation. We built NER modules which can perform NER modeling and NER prediction to

extract important entities stated in each SMS text from three SMS text collection (Bank X, Bank Y, and

Accommodation Reservation Company Z). We conducted supervised learning to construct three NER models

by using three SMS corpora which each corpus contains 5000 SMS text. The NER models can predict

entities from 195,036 SMS text of Bank X with accuracy 99.5%, 26,072 SMS text of Bank Y with accuracy

99.74% and 525,121 SMS text of Accommodation Reservation Company Z with accuracy 94.33%. The

extracted entities are analyzed to profile customer behavior aggregately and individually. The analysis

leads the CEO of Business Corporation to make an appropriate decision regarding the business products.

Keywords: text mining; named entity recognition; short message service.



ID 140

Optimal Control of Lipid Extraction Model on Microalgae Using

LQR (Linear Quadratic Regulator) and PMP (Pontryagin Maximum

Principle) Methods

NUR ILMA YASINTAa, MARDLIJAHb, LUKMAN HANAFIc & SUHARMADI SANJAYAd

a,b,c,dDepartment of Mathematical

Faculty of Mathematics, Computation and Data Sciences




[email protected]

ABSTRACT

Plants that are good as raw materials for biodiesel are algae. Chlorella Vulgaris is one of the most

economical algae to produce biodiesel, because these green algae are rich in carbohydrates, require no

special care, and are easy to grow. Algal oil used for biodiesel production is obtained through a fairly long

process, one of them the process of lipid extraction. Optimal control can be used to get more optimal

results. In this study, Linear Quadratic Regulation (LQR) formulation has the advantages of easy to

analyse and implementation. In comparison, optimum control is performed using the Pontryagin Maximum

Principle (PMP) method, to obtain the best control of the dynamic system from the initial state to the end by

maximizing the objective function. This method is more modern method than the LQR method. Furthermore,

optimal control is performed in order to optimize the yield of lipid concentration in the flow of solvent (Cs), in microalgae particles (Cp ), by minimizing the volume of solvent (v) so as to refine the previous study,

also provide information on how the results obtained using the LQR and the PMP methods used in the

mathematical model obtained.

Keywords: optimal control; linear quadratic regulator; Pontryagin maximum principle; model verification.



ID 142

Positive Normalization of Discrete Descriptor System under

Disturbance

AHMAD IQBAL BAQIa, ADMI NAZRAb, ZULAKMALc, LYRA YULIANTId & MUHAFZANe


Faculty of Mathematics and Natural Science,

Universitas Andalas, Kampus Unand Limau Manis

Padang, Indonesia, 25163


[email protected],[email protected]

ABSTRACT

It is well known the descriptor systems have a wide application field. Usually it appear as a mathematical

model of a real problem, mainly the model that involves the input output relationship. It is well known that

a descriptor linear system has a unique solution if the pencil matrix of the system is regular. However,

there are some systems that are not regular. Moreover, even though the system is regular the solution

can contain the impulsive. Therefore, it is necessary to normalize the descriptor system so as it has

well behavior. In this paper, we propose a feedback to normalize a discrete descriptor system under

disturbance. Furthermore, we establish a sufficient condition in order for the solution of the discrete

descriptor system under normalization is positive.

Keywords: positive normalization; descriptor system; disturbance.



ID 143

SEM BMARS: An Alternative Methods of Nonlinear SEM

MARGARETHA ARI ANGGOROWATIa

aInstitute of Statistics (STIS)

Jakarta, Indonesia

[email protected]

ABSTRACT

Structural Equation Method (SEM) standard has normality and linearity assumptions constraint. Based

on computational statistics SEM already developed with Bayesian SEM that focus on normality constraint.

The next development of computational SEM Bayesian is SEM Bayesian Multivariate Adaptive Regression

Spline (SEM BMARS). SEM BMARS is another computational method for non standard SEM that focus

both on non normality and non linearity constraints. The basic differences between SEM Bayesian and

BMARS SEM was based on computational methods and statistical solution in estimating the parameter.

SEM BMARS method close to non parametric approach and the modeling will be based on regression basis

function.

Keywords: SEM; Bayesian; multivariate adaptive regression spline (MARS).



ID 144

Indonesian Sentiment Analysis using Combination of Algorithms

to Improve the Performance in Imbalanced Dataset

REZKYA PUTRI SEPTIANIa & MARGARETHA ARI ANGGOROWATIb

aIndonesian Central Bureau of Statistics (BPS)

Jakarta, Indonesia

[email protected]

b Institute of Statistics (STIS)

Jakarta, Indonesia

[email protected]

ABSTRACT

Sentiment analysis is most widely used technique to predict the sentiment of textual data. People today

addictly use microblogging such as Twitter to communicate with others or just give opinion of controversial

issues. Indonesian tax amnesty program is one of public policy controversial issue regarding the benefit

and cost are hard to measure. Indonesian Twitter users massively conduct the public campaign of tax

amnesty and give critics of Indonesian government policy. We aim to predict the public sentiment of the

policy use supervised machine learning and try to attempt several experiments of model combinations. In

order to obtain good performance of model, we conducted 48 combination models of preprocessing, N-Gram

feature extraction, feature selection, and machine learning and choose the best appropriate combination

of all probability. The measurement of each models use 5X5 nested cross validation which consist of

inner and outer cross validation to ensure the performance capability of each model towards different sets

of data. We obtain that the combination of SVM, normalization and stemming techniques, Information

Gain feature selection, and Bigram outperform other combination in this research which give the highest

F1-score, accuracy and AUC values.

Keywords: machine learning; sentiment analysis; preprocessing; n-gram; feature selection.



ID 146

Mechanistic Model of Radiation-Induced Bystander Effects

Using Structured Cells Population Approach

FUAADA MOHD SIAMa & MUHAMAD HANIS NASIRb


Faculty of Science,




ABSTRACT

Ionizing radiation is used during radiotherapy treatment in order to damage and hence, kill the cancer

cells. After the ionizing radiation process on the targeted cells, there is side effect to the nearby non-

targeted cells. This effect happens because the targeted cells that had been radiated with ionizing radiation

emits damaging signal molecules to the surrounding and then, damaging the bystander cells. The type

of damage considered in this work is the number of double-strand breaks (DSBs) of deoxyribonucleic acid

(DNA) in cell’s nucleus. This phenomenon has been studied experimentally and the result showed the

death of bystander cells following ionizing radiation. By using mathematical approach, a mechanistic

model that can describe the phenomenon is developed in this paper. A structured cell population model is

used to model this biological system. Then, the accuracy of the model is validated by its ability to match

with the experimental data. The Nelder-Mead Simplex (NMS) algorithm is employed for the optimization

process. NMS algorithm searches the parameter value that minimize the sum-squared error (SSE) between

the model simulation data and experimental data. The mathematical modelling proposed in this paper

is strongly in line with the experimental data. The results show that the model provides a mechanistic

explanation for the bystander effects phenomenon in aspect of cellular activity that can be manipulated

experimentally.

Keywords: bystander effects; double-strand breaks; Nelder-Mead Simplex; survival fraction; model

validation.



ID 147

Solving Surface Decontamination Model Using Laplace Transform

ALI H. M. MURIDa & AMIR S. A. HAMZAHb

aDepartment of Mathematical Sciences

Faculty of Science



[email protected]

bDepartment of Fundamental & Applied Sciences

Faculty of Science & Information Technology

Universiti Teknologi PETRONAS

32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia

[email protected]

ABSTRACT

There are several methodologies for surface decontamination being applied by industries involving with

cleansing process. This paper considers a mathematical model for solving a hard surface bacteria

decontamination by disinfectant solution. The mathematical model is a reaction-diffusion type. A non-

dimensionalised model is obtained and solved by using Laplace transform. The results have shown that,

depending on the value of a certain parameter, the bacteria may grow or decay. Some numerical results

are also presented and discussed.

Keywords: surface decontamination; mathematical model; reaction-diffusion model; Laplace transform.



ID 149

Estimation of Rainfall Curve by using Functional Data Analysis

and Ordinary Kriging Approach

MUHAMMAD FAUZEE HAMDANa, ABDUL AZIZ JEMAINb & SHARIFFAH SUHAILA SYED JAMALUDINc

a,cDepartment of Mathematical Sciences

Faculty of Science




bFaculty of Science and Technology


43600 Bangi, Selangor, Malaysia

[email protected]

ABSTRACT

Rainfall is an interesting phenomenon to be investigated since it is directly related to all aspects of life

on earth. One of the important studies is to investigate and understand the rainfall patterns that occur

throughout the year. To identify the pattern, it requires a rainfall curve to represent daily observations of

rainfall received during the year. Functional data analysis methods are capable to convert discrete data

into a function that can represent the rainfall curve to describe the hidden patterns of the rainfall. This

study focused on the distribution of daily rainfall amount using functional data analysis. Fourier basis

functions are used for periodic rainfall data. Generalized cross-validation showed 123 basis functions

were sufficient to describe the pattern of daily rainfall amount. North and west areas of the peninsula

show a significant bimodal pattern with the curve decline between two peaks at the mid-year. Meanwhile

in the east, it shows unimodal patterns that reached a peak in the last three months. Southern areas show

more uniform trends throughout the year. Finally, the functional spatial method is introduced to overcome

the problem to estimate the rainfall curve in the locations with no data recorded. We use a leave one out

cross-validation as a verification method to compare between the real curve and the predicted curve. We

used coefficient of basis functions to get the predicted curve. It was found that the methods of spatial

prediction can match up with the existing spatial prediction methods in terms of accuracy yet provide a

simple calculation.

Keywords: functional data analysis; ordinary kriging; rainfall curve.



ID 150

g–Jitter Induced Mixed Convection Flow of Casson Fluid with

the Presence of Metallic Nanoparticles

NORAIHAN AFIQAH RAWIa, MOHD RIJAL ILIASb, RAHIMAH MAHATc, ZAITON MAT ISAd &

SHARIDAN SHAFIEe

a,c,d,eDepartment of Mathematical Sciences

Faculty of Science,




bDepartment of Mathematical Sciences,

Faculty of Computer and Mathematical Sciences,

Universiti Teknologi MARA (UiTM)


[email protected]

cUniversiti Kuala Lumpur

Malaysian Institute of Industrial Technology,

81750 Johor Bahru, Johor, Malaysia

[email protected]

ABSTRACT

The unsteady mixed convection flow of non-Newtonian Casson past an inclined stretching sheet with the

presence of g-jitter effect and metallic nanoparticles is investigated in this paper. The governing equations

which consist of coupled non-linear partial differential equations are derived based on Tiwari and Das

model to study the influence of solid nanoparticles volume fraction by taking human blood as base fluid

to represent Casson fluid model. The transformed governing equations are solved numerically using an

implicit finite-difference scheme known as Keller-box method. The numerical results of surface shear stress

in terms of skin friction and heat transfer coefficient in terms of Nusselt number as well as the velocity

and temperature profiles for amplitude of modulation, frequency of oscillation, solid nanoparticles volume

fraction, inclination angle and Casson parameter specifically for assisting flow are presented graphically

and analyzed in details. The presence of amplitude of modulation and frequency of oscillation give a

fluctuating behaviour on the variation of skin friction and heat transfer coefficients. It is also found that,

the increasing of solid nanoparticles volume fraction give rise to the values of the heat transfer coefficient

but contradict the inclination angle for both viscous and Casson nanofluids. It is anticipated that, the



numerical results derived from this research should be helpful in understanding the g-jitter effects on fluid

mechanics process specifically for non-Newtonian fluid flow in microgravity environment.

Keywords: g-jitter; Casson nanofluid; Keller-box method; Tiwari and Das model; inclined stretching sheet.



ID 151

The use of RWikiStat for Fuzzy Learning System using Android

Software

HIZIR SOFYANa & MARZUKIb


Faculty of Science

Syiah Kuala University

23111 Banda Aceh, Indonesia


ABSTRACT

Education requires alternative learning methods that allow learners to develop the students’ knowledge

through the visualization facilities that make learning more attractive and easy for the learners to digest.

Mobile learning is a part of mobile computing and e-learning systems that provide resources accessible

without being bounded by time and space. The purpose of developing the RWikiStat mobile learning is to

assist students using fuzzy learning system through the use of smartphone. The development of RWikiStat

that was made in this study was by applying the Android Studio. The logics of fuzzy are the processes

of decision-making based on how to solve problems that are ambiguous and unclear. The fuzzy logic

systems consist of fuzzy and logical fuzzy. The fuzzy learning system uses mobile learning that provides

the learning module contents that consist of materials related to lectures with features for R and others

software that may be used or applied in this study. Therefore, the use of RWikiStat mobile learning will

help the students in fuzzy system learning.

Keywords: rwikistat; fuzzy; e-learning system; android software.



ID 152

Forecasting East Kalimantan Hotspots Using Kalman Filter

SITI AMINAHa, SRI WAHYUNINGSIHb & MEILIYANI SIRINGORINGOc

aLaboratory of Applied Statistics

Department of Mathematics

Faculty of Science

Mulawarman University,

75119 Samarinda, Indonesia

[email protected]

b,cStudy Program of Statistics

Department of Mathematics

Faculty of Science

Mulawarman University

75119 Samarinda, Indonesia


ABSTRACT

The objective of this research is to forecast hotspots in East Kalimantan. Hotspots are usually used as an

indicator of burned land and forest in a region. In this study hotspots are modeled using Autoregressive

Fractional Integrated Moving Average (ARFIMA), because hotspot data contains long memory. Long

memory data can be identified using Hurst statistics (H). Based on the ARFIMA(1, 0.477, 0) model,

the state space model is constructed using the Kalman filter. The forecasting results using the Kalman

filter show that the hotspots pattern tend to increase until September 2017 which are 94 hotspots, and

tend to decrease begin October 2017 until the end of 2017 with 71 hotspots, 2 hotspots and 2 hotspots,

respectively.

Keywords: ARFIMA; forecasting; hotspots; Kalman filter.



ID 153

A Hybrid Multivariate Time Series Model in Forecasting

Meteorological Data

FADHILAH YUSOFa & SITI MARIAM NORRULASHIKINb


Faculty of Science




ABSTRACT

The impact of climate change may result to the devastation of the earth and life. For early preparations to

face the challenges of climate change, a model that can forecast future weather variables is needed. There

exist several weather models that forecast the future atmospheric data; however, the existing models

which are not station-based models, hence will have an incomplete understanding of climate system of a

particular case study area. To improve on the climatic modelling, this study developed a new model where

the model used data collected from Alor Setar weather stations in Peninsular Malaysia by taking into

consideration all the identified dynamic features of the variables. The model is an extension of multivariate

time series method, namely vector autoregressive (VAR) model. Dynamic conditional correlation (DCC)

model from generalised autoregressive conditional heteroscedasticity (GARCH) model was applied in this

study since weather variable has high volatility and DCC model is able to capture the volatility of the

model. However, because of the high persistence in the volatility, DCC model alone is not able to capture the

structural changes in the volatility. To improve the model, a joint model with hidden Markov model (HMM)

is proposed whereby HMM method will consider the structural changes in the volatility that experienced

high, moderate and low volatility. The findings presented that, due to neglected of structural change in

volatility, the VAR multivariate time series with the hybrid of DCC model was not able to capture closely

the volatility of the weather data. Nevertheless, the proposed joint model that uses the HMM to consider

the structural changes in the volatility was able to capture the degree of persistence in the weather data.

The out-sample forecasting accuracy gives less than ten percent of the mean absolute percentage error

(MAPE) for the proposed joint model. Simulation study proves that the VAR-HMM-DCC proposed model has

better result as compare to the hybrid of the conventional VAR-DCC model. The newly joint VAR-HMM-DCC

model is the contribution that provides strategies for the future forecasting weather data.

Keywords: vector autoregressive (VAR); generalised autoregressive conditional heteroscedasticity

(GARCH); Dynamic conditional correlation (DCC); Hidden Markov Model (HMM).



ID 154

Genetic Algorithm for Inventory Routing Problem with Carbon

Emission Consideration

NUR ARINA BAZILAH BT AZIZa & CHONG JING YEEb


Faculty of Science



[email protected]

ABSTRACT

Inventory Routing Problem (IRP) has been continuously developed and improved due to pressure from

global warming issue particularly related to greenhouse gases (GHGs) emission. The burning of fossil fuel

for transportations such as cars, trucks, ships, trains, and planes primarily emits GHGs. Carbon dioxide

(CO2) from burning of fossil fuel to power transportation and industrial process is the largest contributor

to global GHGs emission. Therefore, the focus of this study is on solving a multi-period inventory routing

problem (MIRP) involving carbon emission consideration based on carbon cap and offset policy. Hybrid

genetic algorithm (HGA) based on allocation first and routing second is used to compute a solution for the

MIRP in this study. The objective of this study is to solve the proposed MIRP model with HGA then validate

the effectiveness of the proposed HGA on data of different sizes. Upon validation, the proposed MIRP model

and HGA is applied on real-world data. The HGA is found to be able to solve small size and large size

instances effectively by providing near optimal solution in relatively short CPU execution time.

Keywords: inventory routing problem; carbon emission; genetic algorithms.



ID 155

On the Performance of Robust Augmented Approach to Desirability

Function for Optimizing Multiple Responses

HABSHAH MIDIa & NASUHAR AB. AZIZb

aFaculty of Science and Institute for Mathematical Research

Universiti Putra Malaysia

43400, Serdang, Selangor, Malaysia

[email protected]

bFaculty of Computer and Mathematical Sciences


18500, Kota Bharu, Kelantan, Malaysia

[email protected]

ABSTRACT

The desirability function is commonly used in applied sciences such as in engineering field to handle the

problem of optimizing multiple responses simultaneously. However, this approach has its shortcoming

whereby it does not take into consideration for the variability in each predicted response. As a result, the

actual response may fall outside the acceptable region even though the predicted response at the optimal

solution has high overall desirability score. An augmented approach to the desirability function (AADF-

OLS based) is put forward to remedy this problem. It is now evident that this approach can reduce the

variances of all predicted responses resulting in narrower prediction intervals. Nonetheless, in the presence

of outliers, the AADF-OLS is not reliable since its formulation is constructed based on the Ordinary Least

Squares (OLS) and geometric mean estimates which are not resistant to outliers. As an alternative, we

propose to integrate robust MM-estimator and geometric median in the augmented approach framework.

We call our proposed method as Augmented Approach to the Desirability Function based on MM estimator

and Geometric Median and denote as (AADF-MM based). A numerical example and simulation study are

presented to assess the performance of the AADFUMM based method. The results of the study signify that

the AADF-MM based is more efficient than the AADF-OLS based method.

Keywords: augmented approach; desirability function; geometric median; MM estimator; outliers.



ID 156

Transcritical Flow Over a Bump Using Forced Korteweg-de Vries

(fKdV) Equation

VINCENT DANIEL DAVIDa, ARIFAH BAHARb & ZAINAL ABDUL AZIZc


Faculty of Science,


81310 UTM Johor Bahru, Malaysia

[email protected]

bCentre for Industrial and Applied Mathematics,

Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor

bFaculty of Computer and Mathematical Sciences,


40450 Shah Alam, Selangor, Malaysia

[email protected]

dMYHIMS SOLUTIONS PLT

Level 3, C08, Universiti Teknologi Malaysia

81310, UTM Johor, Malaysia

[email protected]

ABSTRACT

This research investigates the transcritical flow over a bump as localized obstacles where the bump

consequently generates upstream and downstream flows. Nonlinear shallow water forced Korteweg-de

Vries (fKdV) model is used to analyse the flow over the bump. This theoretical model, containing forcing

functions represents bottom topography is considered as the simplified model to describe water flows

over a bump. The effect of water dispersion over the forcing region is investigated using the fKdV model.

Homotopy Analysis Method (HAM) is used to solve this theoretical fKdV model. The HAM solution which

is chosen with a special choice of h -value describes the physical flow of waves and the significance of

dispersion over a bump is elaborated. Transcritical flows are applied in hydraulic engineering such as

Venturi Channel.



Keywords: approximate analytic solution; fKdV equation; h-curve; homotopy analysis method;

transcritical flow.



ID 157

Heat and Mass Transfer of Magnetohydrodynamics (MHD)

Boundary Layer Flow using Homotopy Analysis Method

NUR LIYANA NAZARIa, AHMAD SUKRI ABD AZIZb, VINCENT DANIEL DAVIDc & ZAILEHA MD ALId

a,b,c,dFaculty of Computer and Mathematical Sciences,


40450 Shah Alam, Selangor, Malaysia


[email protected]

ABSTRACT

Heat and mass transfer of MHD boundary-layer flow of a viscous incompressible fluid over an

exponentially stretching sheet in the presence of radiation is investigated. The two-dimensional boundary-

layer governing partial differential equations are transformed into a system of nonlinear ordinary

differential equations by using similarity variables. The transformed equations of momentum, energy and

concentration are solved by Homotopy Analysis Method (HAM). The validity of HAM solution is ensured

by comparing the HAM solution with existing solutions. The influence of physical parameters such as

magnetic parameter, Prandtl number, radiation parameter, and Schmidt number on velocity, temperature

and concentration profiles are discussed. It is found that the increasing values of magnetic parameter

reduces the dimensionless velocity field but enhances the dimensionless temperature and concentration

field. The temperature distribution decreases with increasing values of Prandtl number. However, the

temperature distribution increases when radiation parameter increases. The concentration boundary layer

thickness decreases as a result of increase in Schmidt number.

Keywords: heat and mass transfer; MHD; stretching sheet; radiation; homotopy analysis method.


Documents

International Seminar on Mathematics in Industry