Visualisasi dan Prediksi Data Inflow dan Outflow Uang Kartal pada Daerah Kepulauan Riau
Nova Rahma Yunida P
2/20/2022
Dosen Pengempu : Prof. Dr. Suhartono, M.Kom
UIN Maulana Malik Ibrahim Malang - Teknik Informatika
Data Beberapa Wilayah di Sumatera
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2Data Inflow
datainflowkelasC <- read_excel(path = "C:/Users/ASUS PC/Documents/BUKU NOVA/RStudio/RMarkdown/kelascup.xlsx")
datainflowkelasC## # A tibble: 11 x 12
## Keterangan Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 57900. 2308. 23238. 9385. 3012.
## 2 2012 65911. 2620. 25981. 11192. 4447.
## 3 2013 98369. 36337. 18120. 14056. 8933.
## 4 2014 86024. 4567. 30503. 14103. 6358.
## 5 2015 86549. 4710. 30254. 13309. 7156.
## 6 2016 97764. 5775. 34427. 14078. 8211.
## 7 2017 103748. 5514. 35617. 15312. 8553.
## 8 2018 117495. 5799. 41769. 15058. 10730.
## 9 2019 133762. 7509. 47112. 14750. 10915.
## 10 2020 109345. 6641. 36609. 10696. 9148.
## 11 2021 89270. 3702. 31840. 10748. 7769.
## # ... with 6 more variables: Kep. Riau <dbl>, Jambi <dbl>,
## # Sumatera Selatan <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # Kep. Bangka Belitung <dbl>Data Outflow
dataoutflowkelasB <- read_excel(path = "C:/Users/ASUS PC/Documents/BUKU NOVA/RStudio/RMarkdown/kelasout.xlsx")
dataoutflowkelasB## # A tibble: 11 x 12
## Keterangan Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 80092. 6338. 22176. 5300. 12434.
## 2 2012 85235. 6378. 22495. 6434. 13014.
## 3 2013 103288. 23278. 19235. 6511. 15460.
## 4 2014 102338. 8630. 26391. 7060. 15158.
## 5 2015 109186. 9637. 27877. 7471. 15789.
## 6 2016 121992. 11311. 31959. 9198. 17645.
## 7 2017 133606. 11760. 35243. 10754. 18128.
## 8 2018 135676. 11450. 36908. 8447. 17926.
## 9 2019 153484. 13087. 44051. 9465. 19277.
## 10 2020 140589. 12874. 39758. 8763. 19139.
## 11 2021 86627. 5770. 23453. 5941. 12631.
## # ... with 6 more variables: Kep. Riau <dbl>, Jambi <dbl>,
## # Sumatera Selatan <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # Kep. Bangka Belitung <dbl>Visualisasi dan Prediksi Inflow di Daerah Kep.Riau
datainflowkelasC$`Kep. Riau`## [1] 1426.343 2236.041 3377.823 2563.001 3217.655 4316.508 4411.558 5133.658
## [9] 6077.325 6175.460 5008.786plot(datainflowkelasC$`Kep. Riau`, type = "l", col = "red")
Visualisasi dan Prediksi Outflow di Daerah Kep.Riau
dataoutflowkelasB$`Kep. Riau`## [1] 5818.626 6965.628 8747.383 10122.259 9802.519 10067.635 10749.453
## [8] 12597.089 12643.718 8461.416 5127.551plot(dataoutflowkelasB$`Kep. Riau`, type = "l", col = "blue")
Visualisasi dan Prediksi Inflow-Outflow di Daerah Kep.Riau Setiap Periode
plot(datainflowkelasC$`Kep. Riau`, type = "l", col = "red")
lines(dataoutflowkelasB$`Kep. Riau`, type = "l", col = "blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
Data pada Beberapa Wilayah di Sumatera Setiap Bulan
Data Inflow PerBulan
datainflowperbulan <- read_excel(path = "C:/Users/ASUS PC/Documents/BUKU NOVA/RStudio/RMarkdown/InflowperBulan.xlsx")
dataoutflowperbulan <- read_excel(path = "C:/Users/ASUS PC/Documents/BUKU NOVA/RStudio/RMarkdown/OutflowperBulan.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>Data Outflow PerBulan
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>Visualisasi dan Prediksi Inflow-Outflow di Daerah Kep.Riau pada Setiap Bulan
plot(datainflowperbulan$`Kep. Riau`, type = "l", col = "red")
lines(dataoutflowperbulan$`Kep. Riau`,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
Time Series dan Log pada daerah Kep.Riau
Kep.Riautimeseries <- datainflowperbulan$`Kep. Riau`
plot.ts(Kep.Riautimeseries , type = "l", col = "green")
logKep.Riau <- log(datainflowperbulan$`Kep. Riau`)
plot.ts(logKep.Riau)
library(TTR)## Warning: package 'TTR' was built under R version 4.1.2Kep.RiauSMA3 <- SMA(datainflowperbulan$`Kep. Riau` ,n=3)
plot.ts(Kep.RiauSMA3 )
library("TTR")
Kep.RiauSMA3 <- SMA(datainflowperbulan$`Kep. Riau` ,n=8)
plot.ts(Kep.RiauSMA3 )
Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Daerah Kep.Riau
Inflow TimeSeries
Kep.Riauinflowtimeseries <- ts(datainflowperbulan$`Kep. Riau`, frequency=12, start=c(2011,1))
Kep.Riauinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 84.22317 45.28489 87.19606 106.27655 79.41735 79.39071
## 2012 154.12964 248.64100 144.87430 208.13217 195.88684 142.58026
## 2013 386.21824 264.78916 225.74983 311.08538 210.63038 202.38804
## 2014 264.22703 270.00068 175.25704 142.22593 123.06405 103.56327
## 2015 527.48615 169.98619 240.82415 193.34540 234.14488 170.06052
## 2016 661.93008 385.82752 312.32158 276.09507 316.70196 150.48089
## 2017 512.43711 385.28011 383.55697 202.89962 208.91189 105.70146
## 2018 711.86420 353.76509 374.70466 387.21015 311.02800 979.33988
## 2019 845.27320 521.35431 474.60558 353.38377 268.14443 1193.95980
## 2020 731.48682 637.43455 386.64090 524.91472 379.63698 793.99943
## 2021 1078.46297 611.51858 423.88140 540.01754 976.15802 569.57964
## Jul Aug Sep Oct Nov Dec
## 2011 120.99479 64.58641 369.70995 126.63637 168.11264 94.51409
## 2012 206.86457 315.89649 216.54585 155.27273 155.62754 91.58926
## 2013 294.45220 919.59500 181.59798 217.10630 110.05512 54.15557
## 2014 60.20677 631.73314 222.13537 258.28860 241.39837 70.90054
## 2015 561.93669 310.80650 164.20381 281.41579 249.20775 114.23718
## 2016 809.08819 262.15868 351.87129 328.02559 261.59397 200.41305
## 2017 839.30770 414.28185 388.93568 378.94493 384.83909 206.46159
## 2018 405.77807 331.14363 383.34951 308.45259 414.23273 172.78907
## 2019 533.39994 388.53399 422.87038 421.73089 397.68404 256.38430
## 2020 507.29098 486.49911 527.42384 414.62940 516.46748 269.03594
## 2021 393.47068 415.69709plot.ts(Kep.Riauinflowtimeseries)
Outflow TimeSeries
Kep.Riauoutflowtimeseries <- ts(dataoutflowperbulan$`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.Riauoutflowtimeseries)
Visualisasi Prediksi Data Inflow-Outflow Time Series Komponen Uang Kartal di Daerah Kep.Riau
Inflow Time Series Komponen
Kep.Riauintimeseriescomponents <- decompose(Kep.Riauinflowtimeseries)
Kep.Riauintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2012 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2013 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2014 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2015 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2016 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2017 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2018 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2019 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2020 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## 2021 230.03092 24.94082 -37.75013 -53.06279 -95.02471 79.65725
## Jul Aug Sep Oct Nov Dec
## 2011 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2012 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2013 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2014 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2015 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2016 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2017 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2018 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2019 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2020 104.58593 76.67545 -16.74583 -53.76999 -58.44211 -201.09480
## 2021 104.58593 76.67545Outflow Time Series Komponen
Kep.Riauouttimeseriescomponents <- decompose(Kep.Riauoutflowtimeseries)
Kep.Riauouttimeseriescomponents$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 38.30166Grafik TimeSeries Kep.Riau
plot(Kep.Riauintimeseriescomponents$seasonal,type = "l", col = "red")
lines(Kep.Riauouttimeseriescomponents$seasonal,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
plot(Kep.Riauintimeseriescomponents$trend,type = "l", col = "red")
lines(Kep.Riauouttimeseriescomponents$trend,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
plot(Kep.Riauintimeseriescomponents$random ,type = "l", col = "red")
lines(Kep.Riauouttimeseriescomponents$random,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
plot(Kep.Riauintimeseriescomponents$figure ,type = "l", col = "red")
lines(Kep.Riauouttimeseriescomponents$figure,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))