Visualisasi Data Inflow dan Outflow Di Kep. 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 Kep. 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 Kep. Riau setiap periode
plot(datainflow$Tahun,datainflow$`Kep. Riau`,type = "l", col= "steelblue")
2.Visualisasi Prediksi Data outflow Uang Kartal Kep. Riau setiap periode
plot(dataoutflow$Tahun,dataoutflow$`Kep. Riau`,type = "l", col= "red")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Kep. Riau Setiap Periode
plot(datainflow$Tahun,datainflow$`Kep. Riau`,type = "l", col= "steelblue")
lines(dataoutflow$Tahun,dataoutflow$`Kep. Riau`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Kep. 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$`Kep. Riau`, type = "l", col = "green")
lines(dataoutflowperbulan$`Kep. Riau`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))
Kep.Riautimeseries <- datainflowperbulan$`Kep. Riau`
plot.ts(Kep.Riautimeseries , type = "l", col = "green")
logKep.Riau <- log(datainflowperbulan$`Kep. Riau`)
plot.ts(logKep.Riau)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Kep. Riau
Kep.Riauinflowtimeseries <- ts(datainflowperbulan$`Kep. Riau`, frequency=12, start=c(2011,1))
Kep.Riauinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 84.22317 45.28489 87.19606 106.27655 79.41735 79.39071
## 2012 154.12964 248.64100 144.87430 208.13217 195.88684 142.58026
## 2013 386.21824 264.78916 225.74983 311.08538 210.63038 202.38804
## 2014 264.22703 270.00068 175.25704 142.22593 123.06405 103.56327
## 2015 527.48615 169.98619 240.82415 193.34540 234.14488 170.06052
## 2016 661.93008 385.82752 312.32158 276.09507 316.70196 150.48089
## 2017 512.43711 385.28011 383.55697 202.89962 208.91189 105.70146
## 2018 711.86420 353.76509 374.70466 387.21015 311.02800 979.33988
## 2019 845.27320 521.35431 474.60558 353.38377 268.14443 1193.95980
## 2020 731.48682 637.43455 386.64090 524.91472 379.63698 793.99943
## 2021 1078.46297 611.51858 423.88140 540.01754 976.15802 569.57964
## Jul Aug Sep Oct Nov Dec
## 2011 120.99479 64.58641 369.70995 126.63637 168.11264 94.51409
## 2012 206.86457 315.89649 216.54585 155.27273 155.62754 91.58926
## 2013 294.45220 919.59500 181.59798 217.10630 110.05512 54.15557
## 2014 60.20677 631.73314 222.13537 258.28860 241.39837 70.90054
## 2015 561.93669 310.80650 164.20381 281.41579 249.20775 114.23718
## 2016 809.08819 262.15868 351.87129 328.02559 261.59397 200.41305
## 2017 839.30770 414.28185 388.93568 378.94493 384.83909 206.46159
## 2018 405.77807 331.14363 383.34951 308.45259 414.23273 172.78907
## 2019 533.39994 388.53399 422.87038 421.73089 397.68404 256.38430
## 2020 507.29098 486.49911 527.42384 414.62940 516.46748 269.03594
## 2021 393.47068 415.69709Kep.Riauoutflowtimeseries <- ts(dataoutflowperbulan$`Kep. Riau`, frequency=12, start=c(2011,1))
Kep.Riauoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 189.20654 268.01656 208.80011 364.35734 447.61217 516.05275
## 2012 332.54370 239.53906 479.70454 362.89160 542.67878 658.10047
## 2013 119.26413 365.97218 463.90646 372.88312 673.61694 581.56634
## 2014 517.98070 246.77804 530.04786 715.09716 830.04557 997.34576
## 2015 192.79623 628.08355 542.19874 855.97355 724.82924 1138.74670
## 2016 256.75804 506.42349 672.97048 840.07221 983.30103 1966.97714
## 2017 410.59624 367.54302 749.04887 703.31521 964.80569 2092.64435
## 2018 229.17137 850.81662 993.83877 936.80576 1739.35274 1649.76547
## 2019 351.32570 533.80541 1070.37711 1147.55958 2819.62986 249.37983
## 2020 627.16179 494.16093 823.30668 707.72583 963.88952 220.66070
## 2021 140.35818 543.53780 588.91635 1222.73228 1161.79670 437.98386
## Jul Aug Sep Oct Nov Dec
## 2011 584.09410 1311.58555 99.21788 270.28783 510.72809 1048.66737
## 2012 660.22824 1072.58101 276.95017 630.29531 519.38376 1190.73164
## 2013 1117.71986 754.58448 735.90065 919.20095 866.05783 1776.70961
## 2014 2056.31540 207.71173 816.86614 1059.52199 601.55529 1542.99310
## 2015 1695.67523 534.01001 678.06544 545.91971 784.86345 1481.35677
## 2016 604.40807 711.35343 871.77960 638.81776 828.88407 1185.88923
## 2017 461.27959 1028.34957 764.82488 906.81927 1121.10595 1179.11994
## 2018 941.81987 1152.64514 825.48506 895.10397 878.93357 1503.35024
## 2019 971.67306 1124.34920 811.30642 969.06768 1018.72975 1576.51468
## 2020 615.39884 525.84998 521.60564 967.01965 506.01366 1488.62254
## 2021 611.98142 420.24414plot.ts(Kep.Riauinflowtimeseries)
plot.ts(Kep.Riauoutflowtimeseries)
Kep.Riauintimeseriescomponents <- decompose(Kep.Riauinflowtimeseries)
Kep.Riauintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2012 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2013 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2014 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2015 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2016 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2017 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2018 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2019 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2020 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2021 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## Jul Aug Sep Oct Nov Dec
## 2011 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2012 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2013 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2014 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2015 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2016 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2017 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2018 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2019 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2020 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2021 104.58593 76.67545Kep.Riauouttimeseriescomponents <- decompose(Kep.Riauoutflowtimeseries)
Kep.Riauouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2012 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2013 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2014 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2015 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2016 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2017 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2018 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2019 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2020 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## 2021 -503.09067 -339.62254 -122.99077 -92.95804 303.81877 225.47459
## Jul Aug Sep Oct Nov Dec
## 2011 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2012 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2013 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2014 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2015 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2016 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2017 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2018 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2019 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2020 167.80534 38.30166 -166.53199 -31.68714 -54.81934 576.30014
## 2021 167.80534 38.30166plot(Kep.Riauintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(Kep.Riauouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Kep.Riauintimeseriescomponents$trend,type = "l", col = "orange")
lines(Kep.Riauouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Kep.Riauintimeseriescomponents$random ,type = "l", col = "orange")
lines(Kep.Riauouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Kep.Riauintimeseriescomponents$figure ,type = "l", col = "orange")
lines(Kep.Riauouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))