Visualisasi dan Prediksi Data Inflow-Outflow Uang Kartal di Aceh
Imamatul Khoiriyah/Prof. Dr. Suhartono, M.Kom
8 Februari 2022
Prodi : Teknik Informatika
Lembaga : UIN Maulana Malik Ibrahim Malang
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2datainflowkelasc <- read_excel(path = "inflowkelascup.xlsx")
datainflowkelasc## # 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 Belitung` <dbl>library(readxl)
dataOUTflowkelasc <- read_excel(path = "OUTflowkelascup.xlsx")
dataOUTflowkelasc## # 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 Belitung` <dbl>Visualisasi Prediksi Data Inflow Uang Kartal di Aceh setiap periode
datainflowkelasc$Aceh## [1] 2307.985 2619.567 36336.547 4566.588 4709.829 5774.937 5514.223
## [8] 5799.114 7508.835 6640.830 3701.603plot(datainflowkelasc$Aceh, type = "l", col = "purple")
Visualisasi Prediksi Data Outflow Uang Kartal di Aceh setiap periode
dataOUTflowkelasc$Aceh## [1] 6338.054 6378.008 23278.066 8629.870 9636.560 11310.606 11760.247
## [8] 11449.810 13086.971 12873.680 5769.832plot(dataOUTflowkelasc$Aceh, type = "l", col = "red")
Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Aceh setiap periode
plot(datainflowkelasc$Aceh, type = "l", col = "purple")
lines(dataOUTflowkelasc$Aceh, type = "l", col = "red")
legend("top",c("Inflow","Outflow"),fill=c("purple","red"))
Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Aceh Setiap Bulan
datainflowperbulan <- read_excel(path = "inflowperbulan.xlsx")
dataoutflowperbulan <- read_excel(path = "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>dataoutflowperbulan ## # A tibble: 128 x 12
## Bulan 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$`Aceh`, type = "l", col = "purple")
lines(dataoutflowperbulan$`Aceh`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("purple","red"))
Acehtimeseries <- datainflowperbulan$Aceh
plot.ts(Acehtimeseries , type = "l", col = "purple")
logAceh <- log(datainflowperbulan$`Aceh`)
plot.ts(logAceh)
library(TTR)## Warning: package 'TTR' was built under R version 4.1.2AcehSMA3 <- SMA(datainflowperbulan$Aceh ,n=3)
plot.ts(AcehSMA3 )
library("TTR")
AcehSMA3 <- SMA(datainflowperbulan$Aceh ,n=8)
plot.ts(AcehSMA3 )
Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Aceh
Acehinflowtimeseries <- ts(datainflowperbulan$`Aceh`, frequency=12, start=c(2011,1))
Acehinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 124.33329 115.14321 154.41614 122.18349 122.75253 151.37534
## 2012 315.65341 292.57807 170.13069 139.33374 167.56600 119.32971
## 2013 5571.15653 3457.27812 3253.57336 3775.08977 3705.38033 3449.77565
## 2014 779.00596 332.03457 248.89939 260.82180 168.17801 194.97802
## 2015 836.57498 376.45107 317.53476 263.06848 256.64615 398.59527
## 2016 883.11220 498.41373 242.45180 218.98473 298.46423 450.32018
## 2017 1120.37553 452.83734 347.32016 240.71874 299.60563 194.84441
## 2018 1279.35872 366.57150 278.86587 262.95066 288.49282 1005.08498
## 2019 1293.88334 565.87121 397.27368 342.84300 420.44274 1554.92585
## 2020 1641.95487 692.74998 297.06861 281.42142 489.21304 1095.11262
## 2021 762.78539 487.91516 368.91965 308.33410 566.54102 502.60975
## Jul Aug Sep Oct Nov Dec
## 2011 107.22432 183.84525 605.62334 157.64630 287.24653 176.19523
## 2012 196.61835 420.06418 286.31394 142.89984 288.58842 80.49051
## 2013 3456.32173 8516.17096 243.91990 379.41362 322.99838 205.46842
## 2014 173.99322 1306.11875 271.45458 454.45573 219.11177 157.53593
## 2015 977.94399 495.56495 179.23767 257.65850 227.20326 123.34945
## 2016 1374.47417 310.75050 538.99459 432.31664 301.61184 225.04199
## 2017 1149.75614 264.01934 627.70230 365.36280 275.68807 175.99260
## 2018 784.64208 369.23511 426.04458 344.08223 243.18631 150.59965
## 2019 473.28934 684.81679 405.51614 467.20195 436.23339 466.53727
## 2020 257.81810 592.86464 410.39330 273.60601 438.09977 170.52803
## 2021 280.32142 424.17610Acehoutflowtimeseries <- ts(dataoutflowperbulan$`Aceh`, frequency=12, start=c(2011,1))
Acehoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 349.57673 192.62487 230.35748 528.59007 523.47023 405.84701
## 2012 420.97459 217.70857 503.88607 600.25342 429.26734 606.25526
## 2013 758.75245 1850.82994 2442.65886 1618.83372 2777.13063 2209.74012
## 2014 288.10448 489.92887 504.83732 773.93194 485.84142 912.38357
## 2015 269.29171 255.71308 521.69449 1125.85624 564.44034 1011.07770
## 2016 307.32375 172.45727 730.75026 667.74179 1079.70825 2642.66616
## 2017 247.27680 344.01370 677.88709 850.88747 1157.54011 2346.78323
## 2018 120.03917 266.02669 996.17473 707.23188 1634.43230 1889.68997
## 2019 85.36298 400.92165 964.22663 1218.54729 3312.30047 122.91218
## 2020 182.38950 426.10026 1433.83382 1432.33902 1689.68700 436.00470
## 2021 56.98918 60.56520 591.41875 1789.00566 2112.99646 176.09492
## Jul Aug Sep Oct Nov Dec
## 2011 957.58488 1046.09243 123.98156 634.45751 595.14381 750.32697
## 2012 600.85083 791.04331 303.81795 854.83456 207.40890 841.70726
## 2013 5383.25551 2570.21842 566.69494 895.80335 699.91951 1504.22825
## 2014 1538.16507 285.80192 611.06198 902.31059 586.60778 1250.89508
## 2015 1558.53777 301.57675 1040.79937 316.39942 824.14892 1847.02405
## 2016 692.69059 653.34617 1027.41562 515.70143 962.49370 1858.31057
## 2017 282.09425 1520.49864 354.31840 667.42332 766.85433 2544.67010
## 2018 278.98765 1155.80705 609.61619 549.33587 622.53291 2619.93560
## 2019 687.35821 1230.78590 600.44006 552.87679 1055.67352 2855.56524
## 2020 1768.67777 455.94178 829.58521 1174.85940 774.36491 2269.89617
## 2021 662.04381 320.71844plot.ts(Acehinflowtimeseries)
plot.ts(Acehoutflowtimeseries)
Acehintimeseriescomponents <- decompose(Acehinflowtimeseries)
Acehintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2012 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2013 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2014 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2015 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2016 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2017 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2018 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2019 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2020 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## 2021 746.11568 48.27704 -118.98158 -92.82414 -59.75309 202.79450
## Jul Aug Sep Oct Nov Dec
## 2011 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2012 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2013 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2014 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2015 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2016 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2017 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2018 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2019 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2020 209.38959 624.31306 -292.95893 -366.68400 -392.77633 -506.91179
## 2021 209.38959 624.31306Acehouttimeseriescomponents <- decompose(Acehoutflowtimeseries)
Acehouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2012 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2013 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2014 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2015 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2016 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2017 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2018 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2019 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2020 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## 2021 -708.278397 -529.248520 -10.306620 8.333003 464.415314 350.679991
## Jul Aug Sep Oct Nov Dec
## 2011 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2012 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2013 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2014 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2015 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2016 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2017 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2018 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2019 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2020 414.184121 42.244469 -353.047814 -260.176871 -268.937025 850.138349
## 2021 414.184121 42.244469plot(Acehintimeseriescomponents$seasonal,type = "l", col = "purple")
lines(Acehouttimeseriescomponents$seasonal,col="red")
legend("top",c("Inflow","Outflow"),fill=c("purple","red"))
plot(Acehintimeseriescomponents$trend,type = "l", col = "purple")
lines(Acehouttimeseriescomponents$trend,col="red")
legend("top",c("Inflow","Outflow"),fill=c("purple","red"))
plot(Acehintimeseriescomponents$random ,type = "l", col = "purple")
lines(Acehouttimeseriescomponents$random,col="red")
legend("top",c("Inflow","Outflow"),fill=c("purple","red"))
plot(Acehintimeseriescomponents$figure ,type = "l", col = "purple")
lines(Acehouttimeseriescomponents$figure,col="red")
legend("top",c("Inflow","Outflow"),fill=c("purple","red"))
REFERENSI:
https://rpubs.com/suhartono-uinmaliki/861286
https://www.bi.go.id/id/fungsi-utama/sistem-pembayaran/pengelolaan-rupiah/default.aspx