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International Journal of Software Engineering and Its Applications
Vol. 10, No. 10 (2016), pp. 89-104
http://dx.doi.org/10.14257/ijseia.2016.10.10.10
ISSN: 1738-9984 IJSEIA
Copyright ⓒ 2016 SERSC
HEMS: Automated Online System for SEGAK Analysis and
Reporting
Fadzli Syed Abdullah1, Nor Saidah Abd Manan2,*, Aryati Ahmad3,*1, Sharifah
Wajihah Wafa4, Mohd Razif Shahril5, Nurzaime Zulaily6,
Rahmah Mohd Amin7 and Amran Ahmed8
1,2Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin,
Terengganu, Malaysia 3Institute for Community (Health) Development, Universiti Sultan Zainal Abidin,
Terengganu, Malaysia 4,5,6Faculty of Health Sciences, Universiti Sultan Zainal Abidin,
Terengganu, Malaysia 7Faculty of Medicin, Universiti Sultan Zainal Abidin, Terengganu, Malaysia
8Institute of Engineering Mathematics, Universiti Malaysia Perlis,
Perlis, Malaysia [email protected], [email protected],
{3aryatiahmad, 4sharifahwajihah, 5razifshahril}@unisza.edu.my, [email protected], [email protected],
Abstract
The Ministry of Education Malaysia (MOE) implemented the National Physical Fitness
Standard (SEGAK) for Malaysian School Children Assessment Program. Ever since, the
SEGAK assessment data had been collected by the respective teachers in every school
twice a year, then its summary is being submitted to the State Department of Education
manually through email. This creates problems such as lack of a standardized report
format, complex formula in calculating SEGAK score and different data interpretation. In
this paper, an integrated and automated SEGAK submission and analysis system is
proposed. The system, which is known as Health Monitoring System (HEMS), is a web
based system developed with an automated pre-processing method and implemented three
tier architecture. HEMS have a centralized database that collects the assessment data
from seven districts in Terengganu. A total of 35,681 data was collected from 213
primary schools, and 27,201 data from 44 secondary schools, giving a big total of 67,519
data. During the pre-processing, 4,637 data or 6.9% of the collected data were excluded
due to wrong and incomplete information. Using HEMS template, the submitted data have
a consistent format of data types. HEMS generates an automated analysis and reporting
for the use of related authorities.
Keywords: Data Pre-processing, Health Monitoring System, Physical Fitness
Assessment, SEGAK
1. Introduction
Physical fitness can be defined as “the ability to carry out daily tasks with vigor and
alertness, without undue fatigue and with ample energy to enjoy leisure-time pursuits and
to meet unforeseen emergencies” [1]. It can be an indicator and later-predictor of the
health of children [2]. Physical fitness can be illustrated by three main components;
1 *Corresponding Author
International Journal of Software Engineering and Its Applications
Vol. 10, No. 10 (2016)
90 Copyright ⓒ 2016 SERSC
cardiorespiratory capacity, strength and agility [3]. The measurement of physical fitness
includes cardiorespiratory endurance, muscular strength, flexibility, muscular endurance
and body composition.
As a learning institution, schools should provide physical education to all students and
it must be taught by an experienced teachers [4]. Since the 21st century, children's health
trends were at an alarming rate. This pushes the schools to remodel and enlarge their roles
in promoting physical education [5], [6].
Physical fitness affects mental health by reducing symptoms of depression, anxiety,
mood swings and increases self-esteem as well as academic performance among youth
[7]. Due to this fact, Ministry of Education Malaysia (MOE) had implemented the
National Physical Fitness Standard for Malaysian School Children (SEGAK) Assessment
Program in 2008. Moreover, during school hours, majority of Malaysian children were
not meeting the minimum, moderate to vigorous physical activity (MVPA)
recommendations [8] which is 60 min/day [9].
There are 500 schools in Terengganu which consists of 352 primary schools and 148
secondary schools [10]. The traditional way of collecting SEGAK data from all schools
was a big challenge. Teachers who teaches physical and health education (PJPK) are
being asked to assess all the students twice a year and submit the summary report of the
assessment to the State Department of Education at the end of each year. Lack of a
standardized report format, complex formula in calculating SEGAK score and different
data interpretation had led into reporting error. This paper proposes an integrated and
automated Health Monitoring System (HEMS) that acts as an online platform to collect,
pre-process and analyzes the SEGAK data for all schools in Terengganu. The system will
allow the teachers to upload the collected data in a single repository, where the data will
be automatically pre-processed to eliminate data errors that could affect data analysis and
reporting process.
Three tiers architecture is used as the integrated design of the HEMS. Figure 1
illustrates the three tiers architecture consisting of data tier, application tier and
presentation tier.
Figure 1. Three Tiers Architecture for HEMS System
2. SEGAK Assessment
SEGAK assessment consists of four tests which includes step-up test, push-up test,
partial curl-up test and sit and reach test. Each student is examined using this assessment
during the PJPK class [11]. The followings were descriptions for each test:
Step-up (NTB). A test to assess the cardiorespiratory. A student must step up and
down from a bench for three minutes. The teacher must be ready with a metronome with a
standardized rhythm, 96 beats per minute. The heart rate needs to be taken five seconds
International Journal of Software Engineering and Its Applications
Vol. 10, No. 10 (2016)
Copyright ⓒ 2016 SERSC 91
after the student finishes the test. The rate should be taken within one minute. Figure 2(a)
shows the position of the student during step-up assessment.
Push-ups (TT/S). A test to exercise the pectorals and biceps. This test is different
between male and female respondents. A male student will perform this test in prone
position (lies with chest down and face up) as shown in Figure 2(b). While a female
student will perform push-ups with bent knees as shown in Figure 2(c). A student must
raise and lower the body using the arms for one minute.
Partial curl-ups (RTS). A test to assess the abdominal muscles. A student must lie
down with knees bent at a 900 angle as shown in Figure 2(d). The student needs to curl-up
within one minute, with 50 beats per minute metronome rhythm. The measurement is
based on the maximum number of curl-ups achieved in one minute.
Sit and reach (JM). A test to examine flexibility. Using a standard sit and reach
tools, a student must sit on the floor without wearing shoes. The teacher will take the
maximum distance reached by the tip of the student’s fingers. Students have a trial of
three times. Figure 2(e) shows the position of the student during sit and reach test
assessment.
Figure 2. The Set of SEGAK Tests
The scores for each item of the tests were standardized based on students’ gender and
age. The scores for each item were summed up to acquire the total score as follows:
(1)
Based on the total score, the SEGAK grades for the students were obtained. Table 1
shows the grade and score allocation for SEGAK.
Table 1. SEGAK Grade and Score Allocation
Total Score SEGAK Grade Achievement Fitness Level
18 – 20 A 4 stars Best
15 – 17 B 3 stars Good
12 – 14 C 2 stars Normal
8 – 11 D 1 star Below Normal
4 – 7 E No star Not Active
International Journal of Software Engineering and Its Applications
Vol. 10, No. 10 (2016)
92 Copyright ⓒ 2016 SERSC
3. Proposed Framework
Figure 3 shows the framework of the HEMS. The framework is divided into three
phases; data submission, data pre-processing and data analysis. Figure 4 shows the
screenshot of the HEMS.
Figure 3. Framework of HEMS Data Collection
Figure 4. HEMS Screenshot
3.1. Data Submission Phase
Data submission phase is conducted by the PJPK teacher in their respective schools.
The process starts with the SEGAK physical assessment among the students. The
assessment data is documented using HEMS template that is available on the HEMS
online portal. The HEMS data template requires the teacher to provide 13 data types
which are important to SEGAK data analysis for each student that are being assessed.
Table 2 lists the data dictionary for the HEMS data template. Completed data are then
uploaded into the HEMS centralized database through HEMS. Figure 5 shows the
screenshot of uploading form of the HEMS.
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Copyright ⓒ 2016 SERSC 93
Table 2. Data Dictionary for HEMS Template
Parameter Data Type Description
MyKid Varchar Students’ identification number
Student Name Varchar Students’ full name
Age Integer Students’ age
Gender Varchar Students’ gender
Physical Impairment Varchar Yes/No
Classroom Varchar Students’ classroom name
Assessment Date Date Assessment Date
NTB Integer Step up measure
TT/S Integer Push up measure
RTS Integer Partial curl-ups measure
JM Integer Sit and reach measure
Weight Float Students’ weight
Height Float Students’ height
Figure 5. SEGAK Data Upload Form
3.2. Data Pre-processing Phase
Since the data in the real world may be incomplete, noisy and inconsistent, the data
pre-processing become a crucial issue in data warehouse and data mining [12]. During
this process, some rules were applied in order to validate the uploaded data. Every file
uploaded by the teachers is being automatically validated. Valid data are added into
HEMS database while data with errors are cleaned using defined rules. If the data cannot
be cleaned, it will be ignored and eliminated. During the cleaning process, four (4)
parameters are being looked at, namely, MyKid, age, gender and assessment date.
3.2.1. MyKid: MyKid is the most important data since it will assign as primary key in the
database. The data with empty MyKid will not be accepted. The MyKid data is cleaned
first by removing the symbols such as (-) and (.) that may cause the data inconsistencies.
There are two main patterns of MyKid; birth certificate (aa00000) and MyKid number
(000000000000). In order to standardize the form of MyKid, a digit (0) will be
concatenated to the MyKid in order to get the correct length of MyKid. This data error
commonly happened due to the incorrect data input in excel file. Figure 6 shows the
pseudocode for MyKid cleaning and validation.
International Journal of Software Engineering and Its Applications
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94 Copyright ⓒ 2016 SERSC
Figure 6. Pseudocode for MyKid Cleaning and Validation
3.2.2. Age: Age is another data type that is also crucial for the data analysis. Hence, this
data should be cleaned from any noises. The data were cleaned by first removing not-digit
characters. The input age should be in a range between 10 to 17 years old. If the age was
less than 10 or greater than 17 years old, the data will be rejected since it become an
outlier. Figure 7 shows the pseudocode for age cleaning and validation.
Figure 7. Pseudocode for Age Cleaning and Validation
3.2.3. Gender: Gender is another data that commonly have problems during the data
entry. Firstly, the spaces in the data were trimmed. Then the length of the data was
checked. If the length is 1, the value of the data then checked. The inconsistencies of data
may occur here such as the value of data is entered as ‘l’ instead of ‘L’. The value then
will be repaired. If the length is 1 but the value is not equal to ‘P’ or ‘L’, the data will be
rejected. Other than that, if the length is more than 1, the first character of the string will
be checked. If the first character is equal to ‘P’ or ‘L’, the data will be accepted, or else it
will be rejected. Figure 8 shows the pseudocode for gender cleaning and validation.
Figure 8. Pseudocode for Gender Cleaning and Validation
International Journal of Software Engineering and Its Applications
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Copyright ⓒ 2016 SERSC 95
3.2.4. Assessment Date: Assessment Date is a sensitive data input and commonly prone
to data error. The data need to be cleaned in order to maintain the consistency for the
purpose of database entry. The common date data errors when the month was type using
words (e.g., ’01 January 2015’), usage of single digit on day and/or month (e.g.,
‘1/1/2015’), usage of two digits for year (e.g., ‘1/1/15’) and inconsistent use of date
separator (e.g., ‘1/1/2015’, ‘1-1-2015’ or ‘1.1.2015’). Data cleaning was applied to the
data to make the data format consistent. The standard pattern of a date was declared,
yyyy-mm-dd or yy-mm-dd. The input data with another pattern will be rewritten. Figure 9
shows the pseudocode for assessment date cleaning and validation.
Figure 9. Pseudocode for Assessment Date Cleaning and Validation
3.2.5. Final Validation: Final validation is the final process that will determine whether
to use or to eliminate the data. During the process, a rule was created where MyKid, BMI
category and SEGAK grade must not be empty or missing. If these three crucial data are
missing, the whole set of data for a student will be automatically rejected. Figure 10
shows the pseudocode for the final validation.
Figure 10. Pseudocode for Final Validation
4. Data Collection
This study involves students from primary schools (aged between 10 and 12 years) in
standard 4 to 6 and secondary schools (aged between 13 and 17 years) in form 1 to 5, in
all the seven districts of Terengganu. A total of 366 primary schools and 146 secondary
schools were involved. SEGAK was examined on all students during PJPK class. A total
of 35,681 data was collected from primary schools, and 27,201 from secondary school,
giving a big total of 67,519 data. However, 4,637 data or 6.9% of the collected data was
excluded during the pre-processing process due to incomplete information. Table 3 lists
the submission statistic according to district. Figure 11 shows the screenshot of the
submission statistic on the HEMS.
International Journal of Software Engineering and Its Applications
Vol. 10, No. 10 (2016)
96 Copyright ⓒ 2016 SERSC
Table 3. Statistic of Data Submission
District Primary School High School
No. of
School
No. of
Submission
% of
Submission
No. of
School
No. of
Submission
% of
Submission
Besut 54 28 52% 19 5 26%
Dungun 48 21 44% 23 6 26%
Hulu
Terengganu
48 28 58% 15 6 40%
Kemaman 47 27 57% 24 6 25%
Kuala
Terengganu
98 66 67% 39 13 33%
Marang 28 15 54% 13 4 31%
Setiu 43 28 65% 13 4 31%
TOTAL 366 213 58.2% 146 44 30.1%
Figure 11. The Screenshot of Submission Statistic
Figure 12 shows the total number of students’ data collected by HEMS based on
districts, age and gender. The highest data collected were from Kuala Terengganu with
41.3%, while the lowest data collected were from Setiu with 12.0%. The total number of
female students is 30,960 while the total number of male students is 31,922. Almost
56.7% of the collected data were from the primary schools (age 10-12) and 43.3% of the
collected data were from the secondary schools (age 13-17).
International Journal of Software Engineering and Its Applications
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Copyright ⓒ 2016 SERSC 97
Figure 12. Number of Students Based on District, Age and Gender
Figure 13 shows the total number of schools submitted SEGAK data using HEMS
based on districts and school level. The highest number of school who has submitted the
data is from primary schools in Kuala Terengganu with 66 schools, while the lowest
number of school who has submitted the data is from secondary schools in Marang and
Setiu with only four schools each. The total number of primary schools is 366 while the
total number of secondary schools is 146. From the total number of schools (512), almost
58.2% of the primary schools and 30.1% of the secondary schools have submitted the
SEGAK data via HEMS. Since the primary schools in two districts; Kuala Terengganu
and Besut were involved directly during our study, the number of schools submitted the
data was significantly different compared to other districts.
Figure 13. Number of Primary and Secondary Schools Based on District
International Journal of Software Engineering and Its Applications
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98 Copyright ⓒ 2016 SERSC
5. Data Analysis Phase
During the data analysis phase, the cleaned data from data pre-processing phase were
analyzed. The HEMS generates two types of report i.e., submission report and SEGAK
report. While SEGAK report can be further categorized into three types of sub-report;
BMI category report, SEGAK grade report and BMI category vs. SEGAK grade report.
The submission report was reported based on district, gender and school level (primary
or secondary school). This report depicts the number and percentage of data submitted via
the HEMS (Figure 14). While the BMI category and SEGAK grade report were reported
based on age, BMI category / SEGAK grade and gender (Figure 15). Besides that, BMI
category vs. SEGAK grade report also will be reported (Figure 16). The data in this report
were presented based on district, school and student.
Figure 14. Screenshot of Submission Report
Figure 15. Screenshot of BMI Category Report
International Journal of Software Engineering and Its Applications
Vol. 10, No. 10 (2016)
Copyright ⓒ 2016 SERSC 99
Figure 16. Screenshot of BMI Category vs. SEGAK Grade Report
6. Results and Discussions
Figure 17 shows the percentage of SEGAK grade among school children in
Terengganu. The majority of students managed to score B and C in the SEGAK
assessment, with 31% and 51% respectively. This means the majority of the students are
either Good or Normal in terms of SEGAK fitness.
Figure 17. Percentage of SEGAK Grade among School Children in Terengganu
Figure 18 shows the proportion of students based on SEGAK grade and age. The graph
shows that majority of students' scores C in SEGAK grades across all ages. From the
collected data, 1,753 of male students and 1,756 of the female students have scored A,
10,131 of male students and 9,271 of female students have scored B. Almost a total of
51% of students have scored C of which 16,271 and 15,936, are male and female
respectively. Whereas 3,204 of male students and 3,401 of female students have scored D.
The number of students scored in E was the lowest. They were 563 of male students and
596 of female students. The graph also shows that a gender difference does not give any
significant impact on the SEGAK grade scores.
International Journal of Software Engineering and Its Applications
Vol. 10, No. 10 (2016)
100 Copyright ⓒ 2016 SERSC
Figure 18. Number of Students on SEGAK Grade by Age and Gender
Figure 19 shows the percentage of students based on SEGAK grade and district. From
the collected data, the percentage of students in each SEGAK grade based on seven
districts in Terengganu was not significantly different. The percentage of students from
Kuala Terengganu was highest among each SEGAK grade. Meanwhile, the percentage of
students from Marang was lowest among each SEGAK grade. The distributions of
SEGAK grade by the district shows that most students from Kuala Terengganu have
scored the highest.
Figure 19. Number of Students on SEGAK Grade by District
The percentage students based on SEGAK grade stratified according to age and gender
is shown in Table 4. It shows that 51.2% or 32,207 of students recorded a normal SEGAK
grade and 3,186 of male students recorded the highest normal SEGAK grade represented
International Journal of Software Engineering and Its Applications
Vol. 10, No. 10 (2016)
Copyright ⓒ 2016 SERSC 101
by 51.5%. From the total number of students, 62,882 (1.8%) were not active. Most of the
male students had recorded a low percentage in grade E. The distribution of SEGAK
grades by ages and genders indicates that most of the students scored C.
Table 4. Percentage of Students on SEGAK Grade Based on Age and Gender
Age
(years
old)
Gender Total
Student
Scale of SEGAK Grade
A B C D E
Total % Total % Total % Total % Total %
10 Male 5845 297 5.1 1819 31.1 2847 48.7 739 12.6 143 2.4
Female 5800 312 5.4 1723 29.7 2922 50.4 685 11.8 158 2.7
11 Male 6247 376 6.0 1905 30.5 3082 49.3 744 11.9 140 2.2
Female 5795 360 6.2 1857 32.0 2845 49.1 610 10.5 123 2.1
12 Male 6189 334 5.4 1816 29.3 3186 51.5 719 11.6 134 2.2
Female 5805 360 6.2 1746 30.1 2966 51.1 615 10.6 118 2.0
13 Male 2809 88 3.1 837 29.8 1603 57.1 248 8.8 33 1.2
Female 2521 164 6.5 909 36.1 1188 47.1 240 9.5 20 0.8
14 Male 2769 145 5.2 798 28.8 1588 57.3 212 7.7 26 0.9
Female 2724 147 5.4 828 30.4 1392 51.1 302 11.1 55 2.0
15 Male 3083 176 5.7 1125 36.5 1570 50.9 175 5.7 37 1.2
Female 2678 113 4.2 718 26.8 1459 54.5 338 12.6 50 1.9
16 Male 2482 191 7.7 883 35.6 1207 48.6 178 7.2 23 0.9
Female 2843 162 5.7 780 27.4 1570 55.2 294 10.3 37 1.3
17 Male 2498 146 5.8 948 38.0 1188 47.6 189 7.6 27 1.1
Female 2794 138 4.9 710 25.4 1594 57.1 317 11.3 35 1.3
Total 62882 3509 5.6 19402 30.9 32207 51.2 6605 10.5 1159 1.8
The percentage of students based on SEGAK grade stratified according to the districts
and gender is shown in Table 5. The highest number of male and female students is from
Kuala Terengganu who have scored C, 6,875 (21.3%) and 6,515 (20.2%), respectively.
While the lowest number of male and female students is from Kemaman who have scored
E, 89 (7.7%) and 138 (11.9%), respectively. The distribution of SEGAK grades according
to districts and genders shows that most of the students scored C.
Table 5. Percentage of Students on SEGAK Grade Based on District and Gender
Districts Gender Total
Student
Scale of SEGAK Grade
A B C D E
Total % Total % Total % Total % Total %
KTG Male 13289 639 4.8 4088 30.8 6875 51.7 1393 10.5 291 2.2
Female 12712 784 6.2 4011 31.6 6515 51.3 1164 9.2 238 1.9
MRG Male 2361 139 5.9 830 35.2 1202 50.9 168 7.1 22 0.9
Female 2279 121 5.3 835 36.6 1178 51.7 132 5.8 13 0.6
HTG Male 2438 184 7.5 877 36.0 1157 47.5 182 7.5 38 1.6
Female 2173 151 6.9 756 34.8 1052 48.4 183 8.4 31 1.4
DGN Male 3509 198 5.6 1224 34.9 1626 46.3 408 11.6 53 1.5
Female 3434 209 6.1 1036 30.2 1616 47.1 474 13.8 99 2.9
KMM Male 4333 196 4.5 1190 27.5 2290 52.9 568 13.1 89 2.1
Female 4380 153 3.5 907 20.7 2414 55.1 768 17.5 138 3.2
STU Male 2172 177 8.1 790 36.4 1037 47.4 146 6.7 22 1.0
Female 2253 139 6.2 705 31.3 1189 52.8 193 8.6 27 1.2
BST Male 3820 220 5.8 1131 29.6 2082 54.5 339 8.9 48 1.3
Female 3729 199 5.3 1021 27.4 1972 52.9 487 13.1 50 1.3
Total 62882 3509 5.6 19401 30.9 32205 51.2 6605 10.5 1159 1.8
International Journal of Software Engineering and Its Applications
Vol. 10, No. 10 (2016)
102 Copyright ⓒ 2016 SERSC
7. Conclusion
The SEGAK assessment may help the related authorities in monitoring students’
fitness level regularly. However, poor data management and analysis had created
drawbacks for the monitoring exercise. This study proposes an automated system called
HEMS to help the ministry and the schools in managing and analyzing SEGAK data
effectively. All submitted data are being pre-processed in order to ensure the reliability of
the data submitted. During pre-processing, almost 21% of the submitted data were
removed due to the incorrectness and incompleteness of data. This incorrect and
incomplete data should be filtered out in order to ensure the accuracy and reliability of the
analysis results.
Acknowledgments
This study was funded by Ministry of Higher Education via Fundamental Research
Grant Scheme, Grant no. [FRGS/2/2013/SKK/UNISZA/01/1].
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International Journal of Software Engineering and Its Applications
Vol. 10, No. 10 (2016)
Copyright ⓒ 2016 SERSC 103
Authors
Fadzli Syed Abdullah, received his PhD in Engineering
(Computational Engineering) from Cardiff University in 2012, M.Sc
in Computer Science from Universiti Teknologi Malaysia (UTM) in
2005 and B.Sc in Management Information System from Universiti
Islam Antarabangsa Malaysia (UIAM) in 2001. Currently he is a
senior lecturer at Faculty of Informatics and Computing, UniSZA.
His research interests are in the area of Knowledge Engineering
including text-based information retrieval, natural language
processing, semantic based systems and ontology based systems also
Software Engineering.
Nor Saidah Abd Manan, received B.Sc. in Computer Science
(Software Development) from Universiti Sultan Zainal Abidin
(UniSZA), Terengganu, Malaysia in 2014. Currently she is doing her
masters at UniSZA, Her research interests are in Software
Development, Data Management and Data Mining.
Aryati Ahmad, received her PhD in Dietetics from University of
Surrey in 2013 and B.Sc in Dietetics from Universiti Kebangsaan
Malaysia (UKM) in 2007. Currently, she is a senior lecturer at
Faculty of Health Science, UniSZA. Her research interests are in the
area of dietetics and nutrition including nutrition and metabolism,
clinical dietetics, Atherogenic Lipoprotein Phenotype, Carbohydrate
and CVD.
Syarifah Wajihah Wafa, received her PhD from University of
Glasgow in 2013, M.Sc from Glasgow Caledonian University in 2007
and B.Sc in Nutririon from Universiti Putra Malaysia (UPM) in 2006.
Now she is a senior lecturer at Faculty of Health Science, UniSZA.
Her research interests are in the area of childhood obesity and
community nutrition.
Mohd Razif Shahril, received his PhD in Nutrition from UniSZA
in 2014 and B.Sc in Nutrition from UKM in 2005. Currently, he is a
senior lecturer at Faculty of Health Science, UniSZA. His research
interests are in the area of Nutritional Epidemiology, Obesity, Dietary
Assessment, Cancer Prevention, Oncology Nutrition and Nutrition
Education.
International Journal of Software Engineering and Its Applications
Vol. 10, No. 10 (2016)
104 Copyright ⓒ 2016 SERSC
Nurzaime Zulaily, received her BSc in Dietetics from UniSZA in
2014. Currently, she is doing her masters at UniSZA. Her research
interests are in the area of childhood obesity and community dietetics.
Rahmah Mohd Amin, received her PhD in Gerantology from
Keele University in 2005, M.Sc in Public Health from UKM in 2005
and MD from Universiti Sains Malaysia (USM) in 1987. Currently,
she is an associate professor at Faculty of Medicine, UniSZA. Her
research interests are in the area of Public Health and Gerantology.
Amran Ahmed, received his PhD in Medical Statistics from
Hiroshima University, M.Sc in Statistics from UKM and B.Sc in
Mathematics from USM. Currently, he is a dean and professor at
Institute of Engineering Mathematics, Universiti Malaysia Perlis
(UniMaP). His research interests are in the area of Statistics.