Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Riau
Heny Rimadana, Prof. Dr. Suhartono, M.Kom
2/19/2022
Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang
Fakultas : Sains dan Teknologi
Jurusan : Teknik Informatika
Mata Kuliah : Linier Algebra
library(readxl)
## Warning: package 'readxl' was built under R version 4.1.2
datainflow <- read_excel(path = "inflow sumatera.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 Bellitung` <dbl>
library(readxl)
dataoutflow <- read_excel(path = "outflow sumatera.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 Bellitung` <dbl>0.1 1.Visualisasi Prediksi Data Inflow Uang Kartal Riau Setiap Periode
plot(datainflow$Tahun,datainflow$`Riau`,type = "l", col= "steelblue")
0.2 2.Visualisasi Prediksi Data Outflow Uang Kartal di Riau Setiap Periode
plot(dataoutflow$Tahun,dataoutflow$`Riau`,type = "l", col= "red")
0.3 3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Riau Setiap Periode
plot(datainflow$Tahun,datainflow$`Riau`,type = "l", col= "steelblue")
lines(dataoutflow$Tahun,dataoutflow$`Riau`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
0.4 4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Riau Setiap Bulan
library(readxl)
datainflowperbulan <- read_excel(path = "inflowbulanan.xlsx")
dataoutflowperbulan <- read_excel(path = "outflowbulanan.xlsx")
datainflowperbulan
## # A tibble: 128 x 12
## Bulanan 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 6 more variables: `Kep. Riau` <dbl>, Jambi <dbl>,
## # `Sumatera Selatan` <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # `Kep. Bangka Belitung` <dbl>
dataoutflowperbulan
## # A tibble: 128 x 12
## Bulanan 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 6 more variables: `Kep. Riau` <dbl>, Jambi <dbl>,
## # `Sumatera Selatan` <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # `Kep. Bangka Belitung` <dbl>
plot(datainflowperbulan$`Riau`, type = "l", col = "red")
lines(dataoutflowperbulan$`Riau`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("red","yellow"))
Riautimeseries <- datainflowperbulan$`Riau`
plot.ts(Riautimeseries , type = "l", col = "green")
logRiau <- log(datainflowperbulan$`Riau`)
plot.ts(logRiau)
0.5 5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Riau
Riauinflowtimeseries <- ts(datainflowperbulan$`Riau`, frequency=12, start=c(2011,1))
Riauinflowtimeseries
## Jan Feb Mar Apr May Jun
## 2011 94.24460 96.39424 287.98845 160.06180 194.70583 100.67608
## 2012 445.71970 364.44861 274.48827 235.70588 341.36393 250.99083
## 2013 1548.75771 724.83408 666.22356 1146.69694 714.10313 628.70916
## 2014 897.55475 597.76572 391.46587 414.92963 399.11419 324.09467
## 2015 1095.88812 347.44105 369.02908 424.74718 505.67346 498.57889
## 2016 1332.16109 622.76483 564.49565 377.26617 501.64829 415.02464
## 2017 1228.76098 692.52354 787.21834 671.46804 700.20181 173.00907
## 2018 1545.34390 887.66466 697.71403 627.84201 422.92181 1972.65304
## 2019 1663.41486 723.68853 671.06970 670.02297 372.20685 2633.04629
## 2020 1566.80990 900.25231 656.60197 465.35740 832.48125 1646.18946
## 2021 2241.25936 910.24470 683.86349 608.93339 1522.46355 829.78643
## Jul Aug Sep Oct Nov Dec
## 2011 143.32160 134.02960 1013.73676 341.22178 285.25779 160.83875
## 2012 390.91878 802.77936 408.83238 299.94057 391.02488 241.07860
## 2013 666.15895 1389.62436 454.88185 526.87296 302.26685 164.31963
## 2014 230.89241 1726.82385 377.03621 427.15336 334.94644 236.43117
## 2015 1399.11338 924.21942 357.65246 492.53688 457.74194 283.85194
## 2016 1858.40120 454.01158 563.71821 617.78181 426.00867 477.63763
## 2017 2114.71229 662.80534 502.47310 396.17308 428.57649 195.45782
## 2018 1293.01149 794.86546 685.77238 761.58086 774.35900 265.80837
## 2019 792.15569 841.10671 817.22178 825.61507 713.15676 192.69741
## 2020 754.19735 643.18320 372.80961 524.47867 611.53183 174.17311
## 2021 454.26751 518.24240
Riauoutflowtimeseries <- ts(dataoutflowperbulan$`Riau`, frequency=12, start=c(2011,1))
Riauoutflowtimeseries
## Jan Feb Mar Apr May Jun
## 2011 478.18402 400.24595 621.35321 1005.56107 1000.35374 1365.96130
## 2012 292.47450 399.76750 880.86006 1049.68113 1055.29479 1142.69911
## 2013 116.34632 569.05345 2345.35727 412.85210 1045.96329 1004.92649
## 2014 517.96101 526.24079 1089.97967 1000.53879 1182.86056 1199.39334
## 2015 133.58209 757.00411 1048.19275 1317.24918 1173.47065 1965.00327
## 2016 264.81101 670.51938 998.35476 1250.91662 1523.48445 4170.88866
## 2017 733.56292 981.17365 1359.41399 1239.79585 1413.94085 3856.69476
## 2018 233.11415 1118.03060 1545.86969 1215.64481 2476.59753 3343.03974
## 2019 455.48443 1012.74002 1340.33344 1521.82191 4902.80531 241.49091
## 2020 739.71921 831.87016 1264.41224 1774.60350 2925.82841 282.77052
## 2021 311.09352 805.14586 1430.24476 2632.46893 3111.28761 1073.67143
## Jul Aug Sep Oct Nov Dec
## 2011 815.43379 2729.10217 154.42178 829.93388 873.64100 2159.95096
## 2012 1196.25287 2392.32861 381.04524 883.96286 968.57206 2370.85940
## 2013 1473.20994 1758.54800 892.49248 1341.31082 1558.92781 2941.37515
## 2014 3974.55298 13.89336 971.59826 969.79530 1076.07146 2634.65301
## 2015 3286.54673 393.89838 718.78270 935.00142 1054.45513 3005.38270
## 2016 515.04790 1100.53865 1629.71683 1273.01584 1438.08721 2809.65000
## 2017 330.25241 1530.30977 896.72821 1317.25781 1705.10587 2763.50350
## 2018 735.25593 1364.76585 955.53100 1303.13335 1240.43316 2394.18052
## 2019 1223.33771 1452.78989 1124.43995 1242.01385 1649.73723 3110.25361
## 2020 1530.19271 1470.10144 1394.12769 2017.60832 1409.04284 3498.29809
## 2021 1692.92089 1573.91533
plot.ts(Riauinflowtimeseries)
plot.ts(Riauoutflowtimeseries)
Riauintimeseriescomponents <- decompose(Riauinflowtimeseries)
Riauintimeseriescomponents$seasonal
## Jan Feb Mar Apr May Jun
## 2011 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2012 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2013 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2014 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2015 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2016 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2017 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2018 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2019 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2020 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2021 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## Jul Aug Sep Oct Nov Dec
## 2011 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2012 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2013 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2014 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2015 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2016 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2017 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2018 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2019 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2020 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2021 306.31477 167.03441
Riauouttimeseriescomponents <- decompose(Riauoutflowtimeseries)
Riauouttimeseriescomponents$seasonal
## Jan Feb Mar Apr May Jun
## 2011 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2012 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2013 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2014 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2015 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2016 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2017 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2018 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2019 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2020 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## 2021 -1029.68788 -641.19116 -51.82000 -184.13231 576.46181 512.97160
## Jul Aug Sep Oct Nov Dec
## 2011 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2012 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2013 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2014 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2015 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2016 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2017 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2018 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2019 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2020 140.29335 51.92179 -461.87487 -172.60911 -102.07941 1361.74622
## 2021 140.29335 51.92179
plot(Riauintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(Riauouttimeseriescomponents$seasonal,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("orange","blue"))
plot(Riauintimeseriescomponents$trend,type = "l", col = "orange")
lines(Riauouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Riauintimeseriescomponents$random ,type = "l", col = "black")
lines(Riauouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("black","grey"))
plot(Riauintimeseriescomponents$figure ,type = "l", col = "orange")
lines(Riauouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
#Referensi
https://ejurnal.its.ac.id/index.php/sains_seni/article/download/12401/2433#:
https://www.bi.go.id/id/fungsi-utama/sistem-pembayaran/pengelolaan-rupiah/default.aspx