Visualisasi Data Inflow dan Outflow Di Riau
Fauzil Hidayatullah, Prof.Dr.Suhartono,M.Kom
2/20/2022
Universitas : Universitas Islam Negeri Maulana Malik Ibrahim Malang
Jurusan : Teknik Informatika
Inflow - Outflow
Inflow disebut investasi sebagai langsung dalam ekonomi pelaporan, termasuk semua kewajiban dan aset yang ditransfer antara perusahaan investasi langsung penduduk dan investor langsung mereka. Ini juga mencakup transfer aset dan kewajiban antara perusahaan yang bertempat tinggal dan yang tidak residen, jika orang tua pengendali utama adalah bukan penduduk.
Outflow disebut sebagai investasi langsung di luar negeri, termasuk aset dan kewajiban yang ditransfer antara investor langsung penduduk dan perusahaan investasi langsung mereka.
contoh penerapan visualisasi prediksi data Inflow-Outflow Uang Kartal di Riau menggunakan bahasa pemrograman R.
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2datainflow <- read_excel(path = "inflowTahunan.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 = "outflowTahunan.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>1.Visualisasi Prediksi Data Inflow Uang Kartal Riau setiap periode
plot(datainflow$Tahun,datainflow$`Riau`,type = "l", col= "steelblue")
2.Visualisasi Prediksi Data outflow Uang Kartal Riau setiap periode
plot(dataoutflow$Tahun,dataoutflow$`Riau`,type = "l", col= "red")
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"))
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
## keterangan 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
## keterangan 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$`Riau`, type = "l", col = "green")
lines(dataoutflowperbulan$`Riau`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))
Riautimeseries <- datainflowperbulan$`Riau`
plot.ts(Riautimeseries , type = "l", col = "green")
logRiau <- log(datainflowperbulan$`Riau`)
plot.ts(logRiau)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Riau
Riauinflowtimeseries <- ts(datainflowperbulan$`Riau`, frequency=12, start=c(2011,1))
Riauinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 94.24460 96.39424 287.98845 160.06180 194.70583 100.67608
## 2012 445.71970 364.44861 274.48827 235.70588 341.36393 250.99083
## 2013 1548.75771 724.83408 666.22356 1146.69694 714.10313 628.70916
## 2014 897.55475 597.76572 391.46587 414.92963 399.11419 324.09467
## 2015 1095.88812 347.44105 369.02908 424.74718 505.67346 498.57889
## 2016 1332.16109 622.76483 564.49565 377.26617 501.64829 415.02464
## 2017 1228.76098 692.52354 787.21834 671.46804 700.20181 173.00907
## 2018 1545.34390 887.66466 697.71403 627.84201 422.92181 1972.65304
## 2019 1663.41486 723.68853 671.06970 670.02297 372.20685 2633.04629
## 2020 1566.80990 900.25231 656.60197 465.35740 832.48125 1646.18946
## 2021 2241.25936 910.24470 683.86349 608.93339 1522.46355 829.78643
## Jul Aug Sep Oct Nov Dec
## 2011 143.32160 134.02960 1013.73676 341.22178 285.25779 160.83875
## 2012 390.91878 802.77936 408.83238 299.94057 391.02488 241.07860
## 2013 666.15895 1389.62436 454.88185 526.87296 302.26685 164.31963
## 2014 230.89241 1726.82385 377.03621 427.15336 334.94644 236.43117
## 2015 1399.11338 924.21942 357.65246 492.53688 457.74194 283.85194
## 2016 1858.40120 454.01158 563.71821 617.78181 426.00867 477.63763
## 2017 2114.71229 662.80534 502.47310 396.17308 428.57649 195.45782
## 2018 1293.01149 794.86546 685.77238 761.58086 774.35900 265.80837
## 2019 792.15569 841.10671 817.22178 825.61507 713.15676 192.69741
## 2020 754.19735 643.18320 372.80961 524.47867 611.53183 174.17311
## 2021 454.26751 518.24240Riauoutflowtimeseries <- ts(dataoutflowperbulan$`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 1573.91533plot.ts(Riauinflowtimeseries)
plot.ts(Riauoutflowtimeseries)
Riauintimeseriescomponents <- decompose(Riauinflowtimeseries)
Riauintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2012 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2013 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2014 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2015 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2016 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2017 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2018 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2019 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2020 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## 2021 657.38978 -24.91095 -126.73102 -129.53109 -159.03686 256.34435
## Jul Aug Sep Oct Nov Dec
## 2011 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2012 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2013 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2014 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2015 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2016 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2017 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2018 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2019 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2020 306.31477 167.03441 -119.93753 -157.53528 -213.78634 -455.61424
## 2021 306.31477 167.03441Riauouttimeseriescomponents <- decompose(Riauoutflowtimeseries)
Riauouttimeseriescomponents$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 51.92179plot(Riauintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(Riauouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Riauintimeseriescomponents$trend,type = "l", col = "orange")
lines(Riauouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Riauintimeseriescomponents$random ,type = "l", col = "orange")
lines(Riauouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Riauintimeseriescomponents$figure ,type = "l", col = "orange")
lines(Riauouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))