Manajemen Operasi Infrastruktur

TUGAS SI-5261MANAJEMEN OPERASI INFRASTRUKTUR

DOSEN PENGASUH :IR. M. ABDUH, MT. PH.D.

DISUSUN OLEH:ANNA ELVARIA

25014017

FAKULTAS TEKNIK SIPIL DAN LINGKUNGANPROGRAM MAGISTER TEKNIK SIPILINSTITUT TEKNOLOGI BANDUNG

2015

Demand Management And Forecasting

Diketahui data volume lalu-lintas kendaraan Jalan Tol Jakarta-Cikampek dari tahun 2012sampai 2014 sebagai berikut :

sumber : http://www.jasamarga.com/id_/kinerja-perusahaan/volume-lalu-lintas.html

Untuk meramalkan volume lalu lintas pada bulan/tahun selanjutnya, maka digunakanbeberapa metode forecasting seperti berikut :

1. Moving Average

2012 2013 2014January 15.498.367 16.001.566 16.063.845February 14.990.935 15.204.328 15.140.802March 15.955.957 16.848.096 17.195.315April 15.649.278 16.601.163 16.580.710May 16.519.035 17.312.428 17.439.426June 16.435.783 17.126.253 17.580.175July 16.668.715 17.053.470 16.487.958

August 16.139.345 16.619.574 12.050.234September 16.648.289 16.948.804 17.624.914October 16.957.796 17.437.787 18.287.496November 16.579.609 16.925.581 17.177.654December 16.831.675 17.518.577 17.836.115

MonthYear

Perhitungan data menggunakan program Exel-Qm menggunakan moving average 3 periode.Forecasting Moving averages - 3 period moving average

Num pds 3

Data

Month 1 15498367Month 2 14990935Month 3 15955957Month 4 15649278 15481753 167525 167525 2,81E+10 01,07%Month 5 16519035 15532057 986978,3 986978,3 9,74E+11 05,97%Month 6 16435783 16041423 394359,7 394359,7 1,56E+11 02,40%Month 7 16668715 16201365 467349,7 467349,7 2,18E+11 02,80%Month 8 16139345 16541178 -401833 401832,7 1,61E+11 02,49%Month 9 16648289 16414614 233674,7 233674,7 5,46E+10 01,40%Month 10 16957796 16485450 472346,3 472346,3 2,23E+11 02,79%Month 11 16579609 16581810 -2201 2201 4844401 00,01%Month 12 16831675 16728565 103110,3 103110,3 1,06E+10 00,61%Month 13 16001566 16789693 -788127 788127,3 6,21E+11 04,93%Month 14 15204328 16470950 -1266622 1266622 1,6E+12 08,33%Month 15 16848096 16012523 835573 835573 6,98E+11 04,96%Month 16 16601163 16017997 583166,3 583166,3 3,4E+11 03,51%Month 17 17312428 16217862 1094566 1094566 1,2E+12 06,32%Month 18 17126253 16920562 205690,7 205690,7 4,23E+10 01,20%Month 19 17053470 17013281 40188,67 40188,67 1,62E+09 00,24%Month 20 16619574 17164050 -544476 544476,3 2,96E+11 03,28%Month 21 16948804 16933099 15705 15705 2,47E+08 00,09%Month 22 17437787 16873949 563837,7 563837,7 3,18E+11 03,23%Month 23 16925581 17002055 -76474 76474 5,85E+09 00,45%Month 24 17518577 17104057 414519,7 414519,7 1,72E+11 02,37%Month 25 16063845 17293982 -1230137 1230137 1,51E+12 07,66%Month 26 15140802 16836001 -1695199 1695199 2,87E+12 11,20%Month 27 17195315 16241075 954240,3 954240,3 9,11E+11 05,55%Month 28 16580710 16133321 447389,3 447389,3 2E+11 02,70%Month 29 17439426 16305609 1133817 1133817 1,29E+12 06,50%Month 30 17580175 17071817 508358 508358 2,58E+11 02,89%Month 31 16487958 17200104 -712146 712145,7 5,07E+11 04,32%Month 32 12050234 17169186 -5118952 5118952 2,62E+13 42,48%Month 33 17624914 15372789 2252125 2252125 5,07E+12 12,78%Month 34 18287496 15387702 2899794 2899794 8,41E+12 15,86%Month 35 17177654 15987548 1190106 1190106 1,42E+12 06,93%Month 36 17836115 17696688 139427 139427 1,94E+10 00,78%

Total 4267680 27940014 5,58E+13 178,10%Average 129323,6 846667,1 1,69E+12 05,40%

Bias MAD MSE MAPESE 1341558

Next period 17767088,3

Forecasts and Error AnalysisAbs Pct ErrPeriod Actual Forecast Error Absolute Squared

Dari tabel diatas dapat dilihat bahwa peramalan untuk bulan ke-37 , januari 2015adalah 17.767.088 kendaraan.

Tabel diatas juga memperlihatkan besarnya Tracking Signal adalah :TRACKING SIGNAL =

Tracking SignalTrack Signal(CE/MAD)

JanuaryFebruaryMarchApril 167525 167525 167525 1May 1154503,333 1154503,333 577251,7 2June 1548863 1548863 516287,7 3July 2016212,667 2016212,667 504053,2 4

August 1614380 2418045,333 483609,1 3,33819217September 1848054,667 2651720 441953,3 4,18156065October 2320401 3124066,333 446295,2 5,19925164

November 2318200 3126267,333 390783,4 5,93218622December 2421310,333 3229377,667 358819,7 6,74798529January 1633183 4017505 401750,5 4,06516731February 366561 5284127 480375,2 0,76307231March 1202134 6119700 509975 2,35724104April 1785300,333 6702866,333 515605,1 3,46253426May 2879866 7797432 556959,4 5,17069261June 3085556,667 8003122,667 533541,5 5,78316139July 3125745,333 8043311,333 502707 6,21782786

August 2581269 8587787,667 505164 5,10976455September 2596974 8603492,667 477971,8 5,43332037October 3160811,667 9167330,333 482491,1 6,55102625

November 3084337,667 9243804,333 462190,2 6,67330799December 3498857,333 9658324 459920,2 7,60753149January 2268720,667 10888460,67 494930 4,58392202February 573521,6667 12583659,67 547115,6 1,04826407March 1527762 13537900 564079,2 2,7084177April 1975151,333 13985289,33 559411,6 3,53076595May 3108968,333 15119106,33 581504,1 5,34642557June 3617326,333 15627464,33 578795 6,24975421July 2905180,667 16339610 583557,5 4,97839659

August -2213771,67 21458562,33 739950,4 -2,9917838September 38353,33333 23710687,33 790356,2 0,04852664October 2938147,333 26610481,33 858402,6 3,42280796

November 4128253,333 27800587,33 868768,4 4,75184589December 4267680,333 27940014,33 846667,1 5,04056474

Month Cum Error Cum Abs Err Mad

= (FORECAST ERROR)/MAD

= , = 5,040564737

Tracking Signal yang positif menunjukkan bahwa nilai aktual permintaan lebih besardaripada ramalan.

2. Weighted Moving AveragePerhitungan data menggunakan program Exel-Qm menggunakan weighted moving average3 periode dengan bobot 1, 1, 2.

Forecasting Weighted moving averages - 3 period moving average

Data

Month 1 15498367 1Month 2 14990935 1Month 3 15955957 2Month 4 15649278 15600304 48974 48974 2,4E+09 00,31%Month 5 16519035 15561362 957673 957673 9,17E+11 05,80%Month 6 16435783 16160826,3 274956,8 274956,8 7,56E+10 01,67%Month 7 16668715 16259969,8 408745,3 408745,3 1,67E+11 02,45%Month 8 16139345 16573062 -433717 433717 1,88E+11 02,69%Month 9 16648289 16345797 302492 302492 9,15E+10 01,82%Month 10 16957796 16526159,5 431636,5 431636,5 1,86E+11 02,55%Month 11 16579609 16675806,5 -96197,5 96197,5 9,25E+09 00,58%Month 12 16831675 16691325,8 140349,3 140349,3 1,97E+10 00,83%Month 13 16001566 16800188,8 -798623 798622,8 6,38E+11 04,99%Month 14 15204328 16353604 -1149276 1149276 1,32E+12 07,56%Month 15 16848096 15810474,3 1037622 1037622 1,08E+12 06,16%Month 16 16601163 16225521,5 375641,5 375641,5 1,41E+11 02,26%Month 17 17312428 16313687,5 998740,5 998740,5 9,97E+11 05,77%Month 18 17126253 17018528,8 107724,3 107724,3 1,16E+10 00,63%Month 19 17053470 17041524,3 11945,75 11945,75 1,43E+08 00,07%Month 20 16619574 17136405,3 -516831 516831,3 2,67E+11 03,11%Month 21 16948804 16854717,8 94086,25 94086,25 8,85E+09 00,56%Month 22 17437787 16892663 545124 545124 2,97E+11 03,13%Month 23 16925581 17110988 -185407 185407 3,44E+10 01,10%Month 24 17518577 17059438,3 459138,8 459138,8 2,11E+11 02,62%Month 25 16063845 17350130,5 -1286286 1286286 1,65E+12 08,01%Month 26 15140802 16642962 -1502160 1502160 2,26E+12 09,92%Month 27 17195315 15966006,5 1229309 1229309 1,51E+12 07,15%Month 28 16580710 16398819,3 181890,8 181890,8 3,31E+10 01,10%Month 29 17439426 16374384,3 1065042 1065042 1,13E+12 06,11%Month 30 17580175 17163719,3 416455,8 416455,8 1,73E+11 02,37%Month 31 16487958 17295121,5 -807164 807163,5 6,52E+11 04,90%Month 32 12050234 16998879,3 -4948645 4948645 2,45E+13 41,07%Month 33 17624914 14542150,3 3082764 3082764 9,5E+12 17,49%Month 34 18287496 15947005 2340491 2340491 5,48E+12 12,80%Month 35 17177654 16562535 615119 615119 3,78E+11 03,58%Month 36 17836115 17566929,5 269185,5 269185,5 7,25E+10 01,51%

Total 3670800 27119411 5,4E+13 172,64%Average 111236,4 821800,3 1,64E+12 05,23%


Next period 17784345

AbsoluteForecasts and Error Analysis

Period Actual Weights Forecast Error Squared Abs Pct Err

Dari tabel diatas dapat dilihat bahwa peramalan untuk bulan ke-37 , januari 2015adalah 17.784.345 kendaraan.

Tabel diatas juga memperlihatkan besarnya Tracking Signal adalah :TRACKING SIGNAL =

Track Signal(Cum Error/MAD)

JanuaryFebruaryMarchApril 48974 48974 48974 1May 1006647 1006647 503323,5 2June 1281603,75 1281603,75 427201,25 3July 1690349 1690349 422587,25 4

August 1256632 2124066 424813,2 2,95808134September 1559124 2426558 404426,3333 3,855149558October 1990760,5 2858194,5 408313,5 4,87556865

November 1894563 2954392 369299 5,130160114December 2034912,25 3094741,25 343860,1389 5,917848625January 1236289,5 3893364 389336,4 3,175376101February 87013,5 5042640 458421,8182 0,189810992March 1124635,25 6080261,75 506688,4792 2,21957928April 1500276,75 6455903,25 496607,9423 3,021048643May 2499017,25 7454643,75 532474,5536 4,693214414June 2606741,5 7562368 504157,8667 5,170486612July 2618687,25 7574313,75 473394,6094 5,531721735

August 2101856 8091145 475949,7059 4,416130473September 2195942,25 8185231,25 454735,0694 4,829058495October 2741066,25 8730355,25 459492,3816 5,965422627

November 2555659,25 8915762,25 445788,1125 5,732901301December 3014798 9374901 446423,8571 6,753218834January 1728512,5 10661186,5 484599,3864 3,566889577February 226352,5 12163346,5 528841,1522 0,428016048March 1455661 13392655 558027,2917 2,608583884April 1637551,75 13574545,75 542981,83 3,015849996May 2702593,5 14639587,5 563061,0577 4,799823151June 3119049,25 15056043,25 557631,2315 5,593390531July 2311885,75 15863206,75 566543,0982 4,080688225

August -2636759,5 20811852 717650,069 -3,674157663September 446004,25 23894615,75 796487,1917 0,559964121October 2786495,25 26235106,75 846293,7661 3,292586288

November 3401614,25 26850225,75 839069,5547 4,054031315December 3670799,75 27119411,25 821800,3409 4,466778081January 7341599,5 54238822,5 1595259,485 4,602134993

Month Cum Err Cum Abs Err Mad


= , = 4,466778081


3. Exponential SmoothingExponential smoothing forecast dicari secara manual dengan menggunakan Exel.

Dari data diatas dapat dilihat bahwa nilai forecast maksimum terletak pada forecast dengannilai terbesar, yaitu sebesar 17.780.193 (=0,9) dan nilai terakhir tersebut disebut dengannilai global optimum untuk final forecast.

Month Actual Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast(Vehicle) = 0,1 = 0,2 = 0,3 = 0,4 = 0,5 = 0,6 = 0,7 = 0,8 = 0,9

1 15498367 15498367 15498367 15498367 15498367 15498367 15498367 15498367 15498367 154983672 14990935 15498367 15498367 15498367 15498367 15498367 15498367 15498367 15498367 154983673 15955957 15447623,8 15396881 15346137 15295394 15244651 15193907,8 15092421,4 15092421,4 15041678,24 15649278 15498457,1 15508696 15529083 15559619 15600304 15651137,3 15783249,9 15783249,9 15864529,125 16519035 15513539,2 15536812 15565142 15595483 15624791 15650021,7 15676072,4 15676072,4 15670803,116 16435783 15614088,8 15733257 15851310 15964904 16071913 16171429,7 16350442,5 16350442,5 16434211,817 16668715 15696258,2 15873762 16026652 16153255 16253848 16330041,7 16418714,9 16418714,9 16435625,888 16139345 15793503,9 16032753 16219271 16359439 16461281,5 16533245,7 16618715 16618715 16645406,099 16648289 15828088 16054071 16195293 16271402 16300313,3 16296905,3 16235219 16235219 16189951,1110 16957796 15910108,1 16172915 16331192 16422157 16474301,1 16507735,5 16565675 16565675 16602455,2111 16579609 16014876,9 16329891 16519173 16636412 16716048,6 16777771,8 16879371,8 16879371,8 16922261,9212 16831675 16071350,1 16379835 16537304 16613691 16647828,8 16658874,1 16639561,6 16639561,6 16613874,2913 16001566 16147382,6 16470203 16625615 16700885 16739751,9 16762554,6 16793252,3 16793252,3 16809894,9314 15204328 16132800,9 16376475 16438400 16421157 16370658,9 16305961,5 16159903,3 16159903,3 16082398,8915 16848096 16039953,6 16142046 16068179 15934425 15787493,5 15644981,4 15395443,1 15395443,1 15292135,0916 16601163 16120767,9 16283256 16302154 16299894 16317794,7 16366850,2 16557565,4 16557565,4 16692499,9117 17312428 16168807,4 16346837 16391857 16420401 16459478,9 16507437,9 16592443,5 16592443,5 16610296,6918 17126253 16283169,4 16539955 16668028 16777212 16885953,4 16990431,9 17168431,1 17168431,1 17242214,8719 17053470 16367477,8 16657215 16805496 16916828 17006103,2 17071924,6 17134688,6 17134688,6 17137849,1920 16619574 16436077 16736466 16879888 16971485 17029786,6 17060851,8 17069713,7 17069713,7 17061907,9221 16948804 16454426,7 16713088 16801794 16830721 16824680,3 16796085,1 16709601,9 16709601,9 16663807,3922 17437787 16503864,4 16760231 16845897 16877954 16886742,2 16887716,5 16900963,6 16900963,6 16920304,3423 16925581 16597256,7 16895742 17023464 17101887 17162264,6 17217758,8 17330422,3 17330422,3 17386038,7324 17518577 16630089,1 16901710 16994099 17031365 17043922,8 17042452,1 17006549,3 17006549,3 16971626,7725 16063845 16718937,9 17025083 17151442 17226250 17281249,9 17328127 17416171,5 17416171,5 17463881,9826 15140802 16653428,6 16832836 16825163 16761288 16672547,4 16569557,8 16334310,3 16334310,3 16203848,727 17195315 16502166 16494429 16319855 16113093 15906674,7 15712304,3 15379503,7 15379503,7 15247106,6728 16580710 16571480,9 16634606 16582493 16545982 16550994,9 16602110,7 16832152,7 16832152,7 17000494,1729 17439426 16572403,8 16623827 16581958 16559873 16565852,4 16589270,3 16630998,5 16630998,5 16622688,4230 17580175 16659106 16786947 16839198 16911694 17002639,2 17099363,7 17277740,5 17277740,5 17357752,2431 16487958 16751212,9 16945592 17061491 17179087 17291407,1 17387850,5 17519688,1 17519688,1 17557932,7232 12050234 16724887,4 16854066 16889431 16902635 16889682,6 16847915 16694304 16694304 16594955,4733 17624914 16257422,1 15893299 15437672 14961675 14469958,3 13969306,4 12979048 12979048 12504706,1534 18287496 16394171,3 16239622 16093845 16026970 16047436,1 16162671 16695740,8 16695740,8 17112893,2135 17177654 16583503,7 16649197 16751940 16931181 17167466,1 17437566 17969145 17969145 18170035,7236 17836115 16642918,8 16754888 16879654 17029770 17172560 17281618,8 17335952,2 17335952,2 17276892,1737 16762238,4 16971134 17166592 17352308 17504337,5 17614316,5 17736082,4 17736082,4 17780192,72

Forecasting Exponential smoothingAlpha 0,9Data Forecasts and Error Analysis

Month 1 15498367 15498367 0 0 0 00,00%Month 2 14990935 15498367 -507432 507432 2,57E+11 03,38%Month 3 15955957 15041678 914278,8 914278,8 8,36E+11 05,73%Month 4 15649278 15864529 -215251 215251,1 4,63E+10 01,38%Month 5 16519035 15670803 848231,9 848231,9 7,19E+11 05,13%Month 6 16435783 16434212 1571,189 1571,189 2468634 00,01%Month 7 16668715 16435626 233089,1 233089,1 5,43E+10 01,40%Month 8 16139345 16645406 -506061 506061,1 2,56E+11 03,14%Month 9 16648289 16189951 458337,9 458337,9 2,1E+11 02,75%Month 10 16957796 16602455 355340,8 355340,8 1,26E+11 02,10%Month 11 16579609 16922262 -342653 342652,9 1,17E+11 02,07%Month 12 16831675 16613874 217800,7 217800,7 4,74E+10 01,29%Month 13 16001566 16809895 -808329 808328,9 6,53E+11 05,05%Month 14 15204328 16082399 -878071 878070,9 7,71E+11 05,78%Month 15 16848096 15292135 1555961 1555961 2,42E+12 09,24%Month 16 16601163 16692500 -91336,9 91336,91 8,34E+09 00,55%Month 17 17312428 16610297 702131,3 702131,3 4,93E+11 04,06%Month 18 17126253 17242215 -115962 115961,9 1,34E+10 00,68%Month 19 17053470 17137849 -84379,2 84379,19 7,12E+09 00,49%Month 20 16619574 17061908 -442334 442333,9 1,96E+11 02,66%Month 21 16948804 16663807 284996,6 284996,6 8,12E+10 01,68%Month 22 17437787 16920304 517482,7 517482,7 2,68E+11 02,97%Month 23 16925581 17386039 -460458 460457,7 2,12E+11 02,72%Month 24 17518577 16971627 546950,2 546950,2 2,99E+11 03,12%Month 25 16063845 17463882 -1400037 1400037 1,96E+12 08,72%Month 26 15140802 16203849 -1063047 1063047 1,13E+12 07,02%Month 27 17195315 15247107 1948208 1948208 3,8E+12 11,33%Month 28 16580710 17000494 -419784 419784,2 1,76E+11 02,53%Month 29 17439426 16622688 816737,6 816737,6 6,67E+11 04,68%Month 30 17580175 17357752 222422,8 222422,8 4,95E+10 01,27%Month 31 16487958 17557933 -1069975 1069975 1,14E+12 06,49%Month 32 12050234 16594955 -4544721 4544721 2,07E+13 37,71%Month 33 17624914 12504706 5120208 5120208 2,62E+13 29,05%Month 34 18287496 17112893 1174603 1174603 1,38E+12 06,42%Month 35 17177654 18170036 -992382 992381,7 9,85E+11 05,78%Month 36 17836115 17276892 559222,8 559222,8 3,13E+11 0,0313534

Total 2535362 30419787 6,66E+13 191,51%Average 70426,72 844994,1 1,85E+12 05,32%


Next period 17780192,7

Abs Pct ErrPeriod Actual Forecast Error Absolute Squared

TRACKING SIGNAL =


= , = 3,000449

Tracking SignalTrack Signal

(Cum Error/MAD)JanuaryFebruary -507432 507432 253716 -2March 406846,8 1421710,8 473903,6 0,85850118April 191595,68 1636961,92 409240,5 0,468173823May 1039827,568 2485193,81 497038,8 2,092045225June 1041398,757 2486765 414460,8 2,512659036July 1274487,876 2719854,12 388550,6 3,280107958

August 768426,7876 3225915,2 403239,4 1,905634188September 1226764,679 3684253,09 409361,5 2,996776232October 1582105,468 4039593,88 403959,4 3,916496344

November 1239452,547 4382246,81 398386,1 3,111184427December 1457253,255 4600047,51 383337,3 3,801490964January 648924,3255 5408376,44 416029 1,559805668February -229146,567 6286447,34 449032 -0,510312387March 1326814,343 7842408,25 522827,2 2,537768314April 1235477,434 7933745,15 495859,1 2,491589856May 1937608,743 8635876,46 507992,7 3,814245002June 1821646,874 8751838,33 486213,2 3,746600713July 1737267,687 8836217,52 465064,1 3,735544761

August 1294933,769 9278551,44 463927,6 2,791241235September 1579930,377 9563548,05 455407 3,469270793October 2097413,038 10081030,7 458228,7 4,577219152

November 1636955,304 10541488,4 458325,6 3,571599229December 2183905,53 11088438,7 462018,3 4,726881241January 783868,553 12488475,6 499539 1,569183813February -279178,145 13551522,3 521212,4 -0,535632203March 1669030,186 15499730,7 574064,1 2,907393422April 1249246,019 15919514,8 568554,1 2,197233325May 2065983,602 16736252,4 577112,2 3,57986501June 2288406,36 16958675,2 565289,2 4,048204831July 1218431,636 18028649,9 581569,4 2,095075389

August -3326289,84 22573371,4 705417,9 -4,715346812September 1793918,016 27693579,2 839199,4 2,137654149October 2968520,802 28868182 849064,2 3,496226648

November 1976139,08 29860563,7 853159 2,316261287December 2535361,908 30419786,6 844994,1 3,000449345

Cum Error Cum Abs Err MadMonth


KESIMPULAN :1. Dari hasil diatas dapat diliat bahwa nilai MAD, MSE dan MAPE dari Metode

Weight Moving Average lebih kecil daripda Metode Moving Average, inimenandakan bahwa Metode Weight Moving Average memiliki tingkat akurasi yanglebih tinggi. Namun jika dibandingkan lagi dengan metode lain, ExponentialSmoothing dengan = 0,9 memiliki keakuratan yang lebih mendekati data actualdibandingkan dengan moving average dan weighted moving average (dapat dilihatpada tabel).

2. Suatu tracking signal disebut baik apabila memiliki nilai cumulatif forecast erroryang rendah dan mempunyai positive error yang sama banyak atau seimbang dengannegative error sehingga pusat tracking signal mendekati nol. Suatu Tracking signalyang baik adalah nilai forecasting yang masih berada dalam batas-batas yang dapatditerima (maksimum 4) , dan nilai yang diluar batas maksimum 4 MADsmerupakan nilai yang tidak valid sehingga harus dilakukan perhitungan ulangdengan menggunakan metode yang lain.

3. Dari ketiga metode forecasting, Exponential Smoothing memiliki nilai Trackingsignal yang baik, karena nilai forecastnya banyak berada di batasan yang diterima.Jadi dapat disimpulkan bahwa Metode Forecasting yang paling baik adalahExponential smoothing.

Dari tabel diatas dapat dilihat bahwa forecasting menggunakan Ekponential Smoothing dengan = 0,9 memiliki keakuratan data yangmendekati data aktual.

4. Dari forecasting yang dilakukan, dapat disarankan kepada PT Jasa Marga untuk terus meningkatkan kapasitas jalan tol yang ada. Karenasemakin banyaknya penggunaan kendaraan pribadi dari tahun ke tahun, diharapkan dengan bertambahnya kapasitas jalan tol, maka akanmenekan kemacetan yang terjadi dan jalan tol tetap menjadi pilihan pengguna jalan sebagai jalan yang diharapkan bebas hambatan.

02000000400000060000008000000

100000001200000014000000160000001800000020000000

Janua

ryFebru

aryMa

rch April

May

June

July

Augu

stSepte

mber

Octob

erNo

vembe

rDe

cembe

rJan

uary

Febru

aryMa

rch April

May

June

July

Augu

stSepte

mber

Octob

erNo

vembe

rDe

cembe

rJan

uary

Febru

aryMa

rch April

May

June

July

Augu

stSepte

mber

Octob

erNo

vembe

rDe

cembe

r

ActualForecasting MAForecasting WMAForecasting ES = 0,9

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