Visualisasi dan Prediksi Data Inflow dan Outflow Uang kartal di Bengkulu Menggunakan RStudio
Nurul Izzah, Prof. Dr. Suhartono, M.Kom
4 Maret 2022
Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang
Jurusan : Teknik Informatika
Data Inflow dan Outflow Wilayah di Pulau Sumatera
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2Data Inflow
datainflow <- read_excel(path = "C:/Users/User/Documents/SEMESTER 2/LINEAR ALGEBRA/inflowsumatera.xlsx")
datainflow## # 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
dataoutflow <- read_excel(path = "C:/Users/User/Documents/SEMESTER 2/LINEAR ALGEBRA/outflowsumatera.xlsx")
dataoutflow## # 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>1. Visualisasi Prediksi Data Inflow Uang Kartal Bengkulu pada Setiap Periode
datainsumatera <- datainflow$`Bengkulu`
plot(datainsumatera,type = "l", col= "blue")
2. Visualisasi Prediksi Data Outflow Uang Kartal Bengkulu pada Setiap Periode
dataoutsuamtera <- dataoutflow$`Bengkulu`
plot(dataoutsuamtera, type = "l", col = "red")
3. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Bengkulu Setiap Periode
plot(datainflow$`Bengkulu`, type = "l", col = "blue")
lines(dataoutflow$`Bengkulu`, type = "l", col = "red")
legend("top",c("Inflow","Outflow"),fill=c("blue","red"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Bengkulu Setiap Bulan
Data Inflow Setiap Bulan
datainflowperbulan <- read_excel(path = "C:/Users/User/Documents/SEMESTER 2/LINEAR ALGEBRA/inflowsumateraperbulan.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 Setiap Bulan
dataoutflowperbulan <- read_excel(path = "C:/Users/User/Documents/SEMESTER 2/LINEAR ALGEBRA/outflowsumateraperbulan.xlsx")
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
plot(datainflowperbulan$`Bengkulu`, type = "l", col = "purple")
lines(dataoutflowperbulan$`Bengkulu`,col="pink")
legend("top",c("Inflow","Outflow"),fill=c("purple","pink"))
5. Time Series dan Log di Bengkulu
bengkulutimeseries <- datainflowperbulan$`Bengkulu`
plot.ts(bengkulutimeseries , type = "l", col = "maroon")
logbengkulu <- log(datainflowperbulan$`Bengkulu`)
plot.ts(logbengkulu)
6. Visualisasi dan Prediksi Data Inflow-Outflow Time Series Uang Kartal di Bengkulu
Data Inflow
bengkuluinflowtimeseries <- ts(datainflowperbulan$`Bengkulu`, frequency=12, start=c(2011,1))
bengkuluinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 122.17640 42.56978 56.79831 27.06372 33.27979 25.84131
## 2012 229.63010 125.41615 65.93120 27.71178 17.46938 17.46938
## 2013 225.33676 240.39147 247.97928 232.80433 158.28819 99.59913
## 2014 708.02522 269.13089 173.04810 221.13003 102.52019 131.58252
## 2015 644.62293 221.83713 163.04665 105.55613 96.35064 84.34825
## 2016 702.39709 293.29774 185.31632 73.73894 119.25824 76.02947
## 2017 705.34454 296.38089 218.07302 108.20777 124.26259 38.37514
## 2018 885.45535 277.07756 207.05547 156.74029 120.71976 669.85657
## 2019 902.06334 384.59633 283.98631 340.23492 256.59610 1294.68991
## 2020 983.83714 517.87037 322.68228 295.68625 330.78731 594.49286
## 2021 1134.14469 507.34820 410.99660 309.79568 798.17998 293.65593
## Jul Aug Sep Oct Nov Dec
## 2011 98.70596 64.44523 430.67254 100.84602 111.67560 39.03351
## 2012 74.43659 207.95245 172.87088 104.67443 134.41372 23.27873
## 2013 135.59282 392.32979 166.69236 194.90184 165.05959 118.56169
## 2014 83.35252 899.76893 204.79900 245.78856 146.50267 75.86238
## 2015 662.75459 223.16428 168.84114 212.90720 127.31721 80.51677
## 2016 661.14587 110.45568 243.85150 175.18164 136.70141 111.48900
## 2017 919.91900 300.75244 296.76196 275.01659 201.18931 135.31315
## 2018 423.32742 286.78781 368.53402 286.96586 286.34575 181.12197
## 2019 381.33964 428.71096 432.36290 498.97557 330.91527 254.67978
## 2020 289.77418 409.26120 438.92378 281.96995 320.24937 185.53660
## 2021 350.87090 355.37500plot.ts(bengkuluinflowtimeseries)
Data Outflow
bengkuluoutflowtimeseries <- ts(dataoutflowperbulan$`Bengkulu`, frequency=12, start=c(2011,1))
bengkuluoutflowtimeseries## 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(bengkuluoutflowtimeseries)
7. Visualisasi dan Prediksi Data Inflow-Outflow Time Series Komponen Uang Kartal di Bengkulu
bengkuluintimeseriescomponents <- decompose(bengkuluinflowtimeseries)
bengkuluintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2012 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2013 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2014 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2015 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2016 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2017 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2018 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2019 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2020 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2021 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## Jul Aug Sep Oct Nov Dec
## 2011 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2012 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2013 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2014 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2015 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2016 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2017 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2018 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2019 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2020 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2021 99.818044 52.992951bengkuluouttimeseriescomponents <- decompose(bengkuluoutflowtimeseries)
bengkuluouttimeseriescomponents$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(bengkuluintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(bengkuluouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(bengkuluintimeseriescomponents$trend,type = "l", col = "orange")
lines(bengkuluouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(bengkuluintimeseriescomponents$random ,type = "l", col = "orange")
lines(bengkuluouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(bengkuluintimeseriescomponents$figure ,type = "l", col = "orange")
lines(bengkuluouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
Sumber :
https://www.bi.go.id/id/statistik/ekonomi-keuangan/ssp/indikator-pengedaran-uang.aspx
https://rpubs.com/suhartono-uinmaliki/861286
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00