Komparasi Visualisasi Prediksi Data Inflow-Outflow Uang Kartal antara Aceh dengan Bengkulu
Najah Muchsin Sanin
8 Februari 2022
Dosen Pengampu : Prof. Dr. Suhartono, M.Kom
Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang
Jurusan : Teknik Informatika
Pengertian Inflow-Outflow Uang Kartal
Inflows adalah uang yang masuk ke Bank Indonesia melalui kegiatan penyetoran, dan outflows adalah uang yang keluar dari Bank Indonesia melalui kegiatan penarikan. Setiap daerah memiliki prediksi data inflow-outflow uang kartal yang berbeda-beda. Berikut komparasi visualisasi prediksi data inflow-outflow uang kartal antara Aceh dengan Bengkulu menggunakan bahasa pemrograman R.
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2datainflow <- read_excel(path = "inflowdata.xlsx")
datainflow## # A tibble: 11 x 12
## Tahun Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau `Kep. Riau`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 57900. 2308. 23238. 9385. 3012. 1426.
## 2 2012 65911. 2620. 25981. 11192. 4447. 2236.
## 3 2013 98369. 36337. 18120. 14056. 8933. 3378.
## 4 2014 86024. 4567. 30503. 14103. 6358. 2563.
## 5 2015 86549. 4710. 30254. 13309. 7156. 3218.
## 6 2016 97764. 5775. 34427. 14078. 8211. 4317.
## 7 2017 103748. 5514. 35617. 15312. 8553. 4412.
## 8 2018 117495. 5799. 41769. 15058. 10730. 5134.
## 9 2019 133762. 7509. 47112. 14750. 10915. 6077.
## 10 2020 109345. 6641. 36609. 10696. 9148. 6175.
## 11 2021 89270. 3702. 31840. 10748. 7769. 5009.
## # ... with 5 more variables: Jambi <dbl>, Sumatera Selatan <dbl>,
## # Bengkulu <dbl>, Lampung <dbl>, Kep. Bangka Belitung <dbl>library(readxl)
dataoutflow <- read_excel(path = "outflowdata.xlsx")
dataoutflow## # A tibble: 11 x 12
## Tahun Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau `Kep. Riau`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 80092. 6338. 22176. 5300. 12434. 5819.
## 2 2012 85235. 6378. 22495. 6434. 13014. 6966.
## 3 2013 103288. 23278. 19235. 6511. 15460. 8747.
## 4 2014 102338. 8630. 26391. 7060. 15158. 10122.
## 5 2015 109186. 9637. 27877. 7471. 15789. 9803.
## 6 2016 121992. 11311. 31959. 9198. 17645. 10068.
## 7 2017 133606. 11760. 35243. 10754. 18128. 10749.
## 8 2018 135676. 11450. 36908. 8447. 17926. 12597.
## 9 2019 153484. 13087. 44051. 9465. 19277. 12644.
## 10 2020 140589. 12874. 39758. 8763. 19139. 8461.
## 11 2021 86627. 5770. 23453. 5941. 12631. 5128.
## # ... with 5 more variables: Jambi <dbl>, Sumatera Selatan <dbl>,
## # Bengkulu <dbl>, Lampung <dbl>, Kep. Bangka Belitung <dbl>1. Komparasi Visualisasi Prediksi Data Inflow Uang Kartal antara Aceh dengan Bengkulu Setiap Periode
plot(datainflow$Tahun,datainflow$Aceh,type = "l", col= "dodgerblue")
lines(datainflow$Tahun,datainflow$Bengkulu,col="red")
legend("top",c("Inflow Aceh","Inflow Bengkulu"),fill=c("dodgerblue","red"))
2. Komparasi Visualisasi Prediksi Data Outflow Uang Kartal antara Aceh dengan Bengkulu Setiap Periode
plot(dataoutflow$Tahun,dataoutflow$`Aceh`,type = "l", col= "green")
lines(dataoutflow$Tahun,dataoutflow$Bengkulu,col="mediumorchid")
legend("top",c("Outflow Aceh","Outflow Bengkulu"),fill=c("green","mediumorchid"))
3. Komparasi Visualisasi Prediksi Data Inflow-Outflow Uang Kartal antara Aceh dengan Bengkulu Setiap Periode
plot(datainflow$Tahun,datainflow$`Aceh`,type = "l", col= "dodgerblue")
lines(datainflow$Tahun,datainflow$Bengkulu,col="red")
lines(dataoutflow$Tahun,dataoutflow$`Aceh`, col= "green")
lines(dataoutflow$Tahun,dataoutflow$Bengkulu,col="mediumorchid")
legend("top",c("Inflow Aceh","Inflow Bengkulu","Outflow Aceh","Outflow Bengkulu"),fill=c("dodgerblue","red","green","mediumorchid"))
4. Komparasi Visualisasi Prediksi Data Inflow-Outflow Uang Kartal antara Aceh dengan Bengkulu Setiap Bulan
library(readxl)
datainflowperbulan <- read_excel(path = "datainperbulan.xlsx")
dataoutflowperbulan <- read_excel(path = "dataoutperbulan.xlsx")
datainflowperbulan## # A tibble: 128 x 41
## Bulan Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 4164. 124. 2068. 545. 94.2
## 2 2011-02-01 00:00:00 3338. 115. 1826. 450. 96.4
## 3 2011-03-01 00:00:00 4878. 154. 2028. 849. 288.
## 4 2011-04-01 00:00:00 3157. 122. 1429. 539. 160.
## 5 2011-05-01 00:00:00 3821. 123. 1539. 692. 195.
## 6 2011-06-01 00:00:00 3686. 151. 1637. 592. 101.
## 7 2011-07-01 00:00:00 4370. 107. 1791. 800. 143.
## 8 2011-08-01 00:00:00 3668. 184. 1256. 586. 134.
## 9 2011-09-01 00:00:00 12875. 606. 4172. 2176. 1014.
## 10 2011-10-01 00:00:00 4777. 158. 1941. 787. 341.
## # ... with 118 more rows, and 35 more variables: Kep. Riau <dbl>, Jambi <dbl>,
## # Sumatera Selatan <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # Kep. Bangka Belitung <dbl>, DKI Jakarta <dbl>, Jawa <dbl>,
## # Jawa Barat <dbl>, Jawa Tengah <dbl>, Yogyakarta <dbl>, Jawa Timur <dbl>,
## # Banten <dbl>, Bali Nusra <dbl>, Bali <dbl>, Nusa Tenggara Barat <dbl>,
## # Nusa Tenggara Timur <dbl>, Kalimantan <dbl>, Kalimantan Barat <dbl>,
## # Kalimantan Tengah <dbl>, Kalimantan Selatan <dbl>, ...dataoutflowperbulan## # A tibble: 128 x 41
## Bulan Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau
## <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 3442. 350. 941. 307. 478.
## 2 2011-02-01 00:00:00 3989. 193. 990. 228. 400.
## 3 2011-03-01 00:00:00 4229. 230. 1209. 347. 621.
## 4 2011-04-01 00:00:00 6721. 529. 1653. 336. 1006.
## 5 2011-05-01 00:00:00 5787. 523. 1465. 328. 1000.
## 6 2011-06-01 00:00:00 7395. 406. 2167. 399. 1366.
## 7 2011-07-01 00:00:00 7154. 958. 1695. 449. 815.
## 8 2011-08-01 00:00:00 16043. 1046. 4104. 1376. 2729.
## 9 2011-09-01 00:00:00 1915. 124. 824. 148. 154.
## 10 2011-10-01 00:00:00 5174. 634. 1392. 299. 830.
## # ... with 118 more rows, and 35 more variables: Kep. Riau <dbl>, Jambi <dbl>,
## # Sumatera Selatan <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # Kep. Bangka Belitung <dbl>, DKI Jakarta <dbl>, Jawa <dbl>,
## # Jawa Barat <dbl>, Jawa Tengah <dbl>, Yogyakarta <dbl>, Jawa Timur <dbl>,
## # Banten <dbl>, Bali Nusra <dbl>, Bali <dbl>, Nusa Tenggara Barat <dbl>,
## # Nusa Tenggara Timur <dbl>, Kalimantan <dbl>, Kalimantan Barat <dbl>,
## # Kalimantan Tengah <dbl>, Kalimantan Selatan <dbl>, ...plot(datainflowperbulan$`Aceh`, type = "l", col = "tomato")
lines(datainflowperbulan$Bengkulu,col="pink")
lines(dataoutflowperbulan$`Aceh`, col = "green")
lines(dataoutflowperbulan$Bengkulu,col="purple")
legend("top",c("Inflow Aceh","Inflow Bengkulu","Outflow Aceh","Outflow Bengkulu"),fill=c("tomato","pink","green","purple"))
Acehtimeseries <- datainflowperbulan$`Aceh`
Bengkulutimeseries <- datainflowperbulan$Bengkulu
plot.ts(Acehtimeseries , type = "l", col = "cyan")
lines(Bengkulutimeseries , type = "l", col = "darkorchid")
legend("top",c("Aceh Timeseries","Bengkulu Timeseries"),fill=c("cyan","darkorchid"))
logAceh <- log(datainflowperbulan$`Aceh`)
logBengkulu <- log(datainflowperbulan$`Bengkulu`)
plot.ts(logAceh, type = "l", col = "peachpuff")
lines(logBengkulu , type = "l", col = "gold")
legend("top",c("logAceh","logBengkulu"),fill=c("peachpuff","gold"))
library(TTR)## Warning: package 'TTR' was built under R version 4.1.2AcehSMA3 <- SMA(datainflowperbulan$`Aceh`,n=3)
BengkuluSMA3 <- SMA(datainflowperbulan$`Bengkulu`,n=3)
plot.ts(AcehSMA3, type = "l", col = "black")
lines(BengkuluSMA3, type = "l", col = "red")
legend("top",c("AcehSMA3","BengkuluSMA3"),fill=c("black","red"))
library(TTR)
AcehSMA3 <- SMA(datainflowperbulan$`Aceh`,n=8)
BengkuluSMA3 <- SMA(datainflowperbulan$`Bengkulu`,n=8)
plot.ts(AcehSMA3, type = "l", col = "black")
lines(BengkuluSMA3, type = "l", col = "red")
legend("top",c("AcehSMA3","BengkuluSMA3"),fill=c("black","red"))
5. Komparasi Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal antara Aceh dengan Bengkulu
Acehinflowtimeseries <- ts(datainflowperbulan$`Aceh`, frequency=12, start=c(2011,1))
Bengkuluinflowtimeseries <- ts(datainflowperbulan$Bengkulu, frequency=12, start=c(2011,1))
Acehinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 124.33329 115.14321 154.41614 122.18349 122.75253 151.37534
## 2012 315.65341 292.57807 170.13069 139.33374 167.56600 119.32971
## 2013 5571.15653 3457.27812 3253.57336 3775.08977 3705.38033 3449.77565
## 2014 779.00596 332.03457 248.89939 260.82180 168.17801 194.97802
## 2015 836.57498 376.45107 317.53476 263.06848 256.64615 398.59527
## 2016 883.11220 498.41373 242.45180 218.98473 298.46423 450.32018
## 2017 1120.37553 452.83734 347.32016 240.71874 299.60563 194.84441
## 2018 1279.35872 366.57150 278.86587 262.95066 288.49282 1005.08498
## 2019 1293.88334 565.87121 397.27368 342.84300 420.44274 1554.92585
## 2020 1641.95487 692.74998 297.06861 281.42142 489.21304 1095.11262
## 2021 762.78539 487.91516 368.91965 308.33410 566.54102 502.60975
## Jul Aug Sep Oct Nov Dec
## 2011 107.22432 183.84525 605.62334 157.64630 287.24653 176.19523
## 2012 196.61835 420.06418 286.31394 142.89984 288.58842 80.49051
## 2013 3456.32173 8516.17096 243.91990 379.41362 322.99838 205.46842
## 2014 173.99322 1306.11875 271.45458 454.45573 219.11177 157.53593
## 2015 977.94399 495.56495 179.23767 257.65850 227.20326 123.34945
## 2016 1374.47417 310.75050 538.99459 432.31664 301.61184 225.04199
## 2017 1149.75614 264.01934 627.70230 365.36280 275.68807 175.99260
## 2018 784.64208 369.23511 426.04458 344.08223 243.18631 150.59965
## 2019 473.28934 684.81679 405.51614 467.20195 436.23339 466.53727
## 2020 257.81810 592.86464 410.39330 273.60601 438.09977 170.52803
## 2021 280.32142 424.17610Bengkuluinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 122.17640 42.56978 56.79831 27.06372 33.27979 25.84131
## 2012 229.63010 125.41615 65.93120 27.71178 17.46938 17.46938
## 2013 225.33676 240.39147 247.97928 232.80433 158.28819 99.59913
## 2014 708.02522 269.13089 173.04810 221.13003 102.52019 131.58252
## 2015 644.62293 221.83713 163.04665 105.55613 96.35064 84.34825
## 2016 702.39709 293.29774 185.31632 73.73894 119.25824 76.02947
## 2017 705.34454 296.38089 218.07302 108.20777 124.26259 38.37514
## 2018 885.45535 277.07756 207.05547 156.74029 120.71976 669.85657
## 2019 902.06334 384.59633 283.98631 340.23492 256.59610 1294.68991
## 2020 983.83714 517.87037 322.68228 295.68625 330.78731 594.49286
## 2021 1134.14469 507.34820 410.99660 309.79568 798.17998 293.65593
## Jul Aug Sep Oct Nov Dec
## 2011 98.70596 64.44523 430.67254 100.84602 111.67560 39.03351
## 2012 74.43659 207.95245 172.87088 104.67443 134.41372 23.27873
## 2013 135.59282 392.32979 166.69236 194.90184 165.05959 118.56169
## 2014 83.35252 899.76893 204.79900 245.78856 146.50267 75.86238
## 2015 662.75459 223.16428 168.84114 212.90720 127.31721 80.51677
## 2016 661.14587 110.45568 243.85150 175.18164 136.70141 111.48900
## 2017 919.91900 300.75244 296.76196 275.01659 201.18931 135.31315
## 2018 423.32742 286.78781 368.53402 286.96586 286.34575 181.12197
## 2019 381.33964 428.71096 432.36290 498.97557 330.91527 254.67978
## 2020 289.77418 409.26120 438.92378 281.96995 320.24937 185.53660
## 2021 350.87090 355.37500Acehoutflowtimeseries <- ts(dataoutflowperbulan$`Aceh`, frequency=12, start=c(2011,1))
Bengkuluoutflowtimeseries <- ts(dataoutflowperbulan$Bengkulu, frequency=12, start=c(2011,1))
Acehoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 349.57673 192.62487 230.35748 528.59007 523.47023 405.84701
## 2012 420.97459 217.70857 503.88607 600.25342 429.26734 606.25526
## 2013 758.75245 1850.82994 2442.65886 1618.83372 2777.13063 2209.74012
## 2014 288.10448 489.92887 504.83732 773.93194 485.84142 912.38357
## 2015 269.29171 255.71308 521.69449 1125.85624 564.44034 1011.07770
## 2016 307.32375 172.45727 730.75026 667.74179 1079.70825 2642.66616
## 2017 247.27680 344.01370 677.88709 850.88747 1157.54011 2346.78323
## 2018 120.03917 266.02669 996.17473 707.23188 1634.43230 1889.68997
## 2019 85.36298 400.92165 964.22663 1218.54729 3312.30047 122.91218
## 2020 182.38950 426.10026 1433.83382 1432.33902 1689.68700 436.00470
## 2021 56.98918 60.56520 591.41875 1789.00566 2112.99646 176.09492
## Jul Aug Sep Oct Nov Dec
## 2011 957.58488 1046.09243 123.98156 634.45751 595.14381 750.32697
## 2012 600.85083 791.04331 303.81795 854.83456 207.40890 841.70726
## 2013 5383.25551 2570.21842 566.69494 895.80335 699.91951 1504.22825
## 2014 1538.16507 285.80192 611.06198 902.31059 586.60778 1250.89508
## 2015 1558.53777 301.57675 1040.79937 316.39942 824.14892 1847.02405
## 2016 692.69059 653.34617 1027.41562 515.70143 962.49370 1858.31057
## 2017 282.09425 1520.49864 354.31840 667.42332 766.85433 2544.67010
## 2018 278.98765 1155.80705 609.61619 549.33587 622.53291 2619.93560
## 2019 687.35821 1230.78590 600.44006 552.87679 1055.67352 2855.56524
## 2020 1768.67777 455.94178 829.58521 1174.85940 774.36491 2269.89617
## 2021 662.04381 320.71844Bengkuluoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 43.00021 82.23542 143.53922 246.22066 202.80478 265.84634
## 2012 77.67069 136.45409 214.08931 230.04005 343.95133 343.95133
## 2013 150.23670 309.92998 431.93072 314.02314 742.58906 664.43864
## 2014 184.84757 233.07711 359.39862 524.14915 447.54582 377.69263
## 2015 103.40197 176.91637 236.82757 435.72702 510.20743 474.21976
## 2016 59.75611 134.50325 206.17499 355.34003 506.32330 1581.42961
## 2017 156.75645 191.46206 341.51406 410.43977 612.92546 1597.77779
## 2018 104.78294 200.91583 399.37190 498.39520 866.36789 1137.64484
## 2019 136.77104 354.05007 432.66657 755.79629 1646.68269 168.74806
## 2020 256.84547 331.85653 442.42736 531.24172 969.68490 209.58637
## 2021 95.04035 340.25426 457.19172 920.71828 1096.04779 629.30605
## Jul Aug Sep Oct Nov Dec
## 2011 263.31558 497.98805 73.97831 188.67118 175.22115 377.68102
## 2012 205.01716 360.89097 153.25346 209.32113 202.05658 482.63553
## 2013 1563.65149 783.20289 262.44591 260.53121 382.27823 624.35333
## 2014 949.04614 161.37331 247.44909 317.04213 292.98312 488.31758
## 2015 1085.06420 246.35914 274.35432 250.71305 309.02593 748.71687
## 2016 212.21523 567.18382 238.44064 187.43127 384.85065 729.08792
## 2017 110.49356 216.10078 248.63583 249.51486 472.84165 838.28091
## 2018 233.48894 261.42442 225.52806 344.89425 470.11011 752.32699
## 2019 653.94175 479.32908 380.83854 386.78029 650.26438 795.78060
## 2020 680.85829 483.37874 506.16610 625.26947 575.95459 950.75046
## 2021 676.14611 466.14904plot.ts(Acehinflowtimeseries,type = "l", col = "khaki")
lines(Bengkuluinflowtimeseries, type = "l", col = "sienna")
legend("top",c("Acehinflowtimeseries","Bengkuluinflowtimeseries"),fill=c("khaki","sienna"))
plot.ts(Acehoutflowtimeseries,type = "l", col = "khaki")
lines(Bengkuluoutflowtimeseries, type = "l", col = "sienna")
legend("top",c("Acehoutflowtimeseries","Bengkuluoutflowtimeseries"),fill=c("khaki","sienna"))
Acehintimeseriescomponents <- decompose(Acehinflowtimeseries)
Bengkuluintimeseriescomponents <- decompose(Bengkuluinflowtimeseries)
Acehintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2012 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2013 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2014 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2015 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2016 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2017 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2018 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2019 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2020 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2021 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## Jul Aug Sep Oct Nov Dec
## 2011 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2012 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2013 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2014 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2015 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2016 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2017 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2018 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2019 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2020 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2021 209.38959 624.31306Bengkuluintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2012 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2013 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2014 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2015 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2016 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2017 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2018 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2019 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2020 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2021 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## Jul Aug Sep Oct Nov Dec
## 2011 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2012 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2013 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2014 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2015 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2016 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2017 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2018 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2019 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2020 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2021 99.818044 52.992951Acehouttimeseriescomponents <- decompose(Acehoutflowtimeseries)
Bengkuluouttimeseriescomponents <- decompose(Bengkuluoutflowtimeseries)
Acehouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2012 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2013 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2014 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2015 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2016 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2017 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2018 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2019 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2020 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2021 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## Jul Aug Sep Oct Nov Dec
## 2011 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2012 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2013 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2014 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2015 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2016 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2017 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2018 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2019 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2020 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2021 414.184121 42.244469Bengkuluouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2012 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2013 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2014 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2015 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2016 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2017 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2018 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2019 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2020 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2021 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## Jul Aug Sep Oct Nov Dec
## 2011 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2012 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2013 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2014 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2015 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2016 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2017 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2018 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2019 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2020 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2021 169.42047 -21.85756plot(Acehintimeseriescomponents$seasonal,type = "l", col = "purple")
lines(Bengkuluintimeseriescomponents$seasonal,col="palegreen")
lines(Acehouttimeseriescomponents$seasonal, type = "l", col = "lightskyblue")
lines(Bengkuluouttimeseriescomponents$seasonal,col="orange")
legend("top",c("Aceh Inflow","Bengkulu Inflow", "Aceh Outflow","Bengkulu Outflow"),fill=c("purple","palegreen","lightskyblue","orange"))
plot(Acehintimeseriescomponents$trend,type = "l", col = "purple")
lines(Bengkuluintimeseriescomponents$trend,col="palegreen")
lines(Acehouttimeseriescomponents$trend, type = "l", col = "lightskyblue")
lines(Bengkuluouttimeseriescomponents$trend,col="orange")
legend("top",c("Aceh Inflow","Bengkulu Inflow", "Aceh Outflow","Bengkulu Outflow"),fill=c("purple","palegreen","lightskyblue","orange"))
plot(Acehintimeseriescomponents$random,type = "l", col = "purple")
lines(Bengkuluintimeseriescomponents$random,col="palegreen")
lines(Acehouttimeseriescomponents$random, type = "l", col = "lightskyblue")
lines(Bengkuluouttimeseriescomponents$random,col="orange")
legend("top",c("Aceh Inflow","Bengkulu Inflow", "Aceh Outflow","Bengkulu Outflow"),fill=c("purple","palegreen","lightskyblue","orange"))
plot(Acehintimeseriescomponents$figure,type = "l", col = "purple")
lines(Bengkuluintimeseriescomponents$figure,col="palegreen")
lines(Acehouttimeseriescomponents$figure, type = "l", col = "lightskyblue")
lines(Bengkuluouttimeseriescomponents$figure,col="orange")
legend("top",c("Aceh Inflow","Bengkulu Inflow", "Aceh Outflow","Bengkulu Outflow"),fill=c("purple","palegreen","lightskyblue","orange"))