Visualisasi dan Prediksi Data Inflow-Outflow Uang Kartal di Lampung
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 Lampung setiap periode
datainflowkelasc$Lampung## [1] 7690.123 6969.230 3473.825 9447.838 8159.769 9373.141 12078.088
## [8] 13415.256 17046.457 15157.525 10697.166plot(datainflowkelasc$Lampung, type = "l", col = "magenta")
Visualisasi Prediksi Data Outflow Uang Kartal di Lampung setiap periode
dataOUTflowkelasc$Lampung## [1] 5724.398 6375.646 4571.017 8339.149 9945.827 10435.507 13358.784
## [8] 13725.388 15626.394 13873.408 8050.092plot(dataOUTflowkelasc$Lampung, type = "l", col = "dodger blue")
Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Lampung setiap periode
plot(datainflowkelasc$Lampung, type = "l", col = "magenta")
lines(dataOUTflowkelasc$Lampung, type = "l", col = "dodger blue")
legend("top",c("Inflow","Outflow"),fill=c("magenta","dodger blue"))
Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Lampung 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$`Lampung`, type = "l", col = "magenta")
lines(dataoutflowperbulan$`Lampung`,col="dodger blue")
legend("top",c("Inflow","Outflow"),fill=c("magenta","dodger blue"))
Lampungtimeseries <- datainflowperbulan$Lampung
plot.ts(Lampungtimeseries , type = "l", col = "magenta")
logLampung <- log(datainflowperbulan$`Lampung`)
plot.ts(logLampung)
library(TTR)## Warning: package 'TTR' was built under R version 4.1.2LampungSMA3 <- SMA(datainflowperbulan$Lampung ,n=3)
plot.ts(LampungSMA3 )
library("TTR")
LampungSMA3 <- SMA(datainflowperbulan$Lampung ,n=8)
plot.ts(LampungSMA3 )
Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Lampung
Lampunginflowtimeseries <- ts(datainflowperbulan$`Lampung`, frequency=12, start=c(2011,1))
Lampunginflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 621.71179 358.56622 550.36496 340.44445 402.03710 573.97617
## 2012 1054.26685 666.52412 517.06758 282.85569 344.24522 206.42495
## 2013 234.65931 117.20955 170.13195 75.87996 74.77127 36.67275
## 2014 1433.51885 725.39736 590.72966 568.85388 487.63656 605.40178
## 2015 1360.19086 508.30661 417.04559 277.84130 383.31675 415.35766
## 2016 1390.23556 804.10302 598.27040 555.00940 286.75963 158.32177
## 2017 1134.20195 690.26841 655.03228 794.03117 675.69061 531.31408
## 2018 1802.74124 949.48387 814.34998 689.44512 370.42214 2491.07250
## 2019 2147.18917 921.91017 900.51341 1104.01903 842.23750 3364.05676
## 2020 2551.78237 1446.11620 939.33048 955.09673 1276.19200 1889.39473
## 2021 2555.46285 1243.57068 936.61307 1164.78835 2166.96750 1237.16558
## Jul Aug Sep Oct Nov Dec
## 2011 656.24294 542.87169 1775.98512 623.85717 801.97986 442.08517
## 2012 412.76796 1054.75071 949.52907 542.34897 684.54376 253.90505
## 2013 44.55553 417.59331 503.81158 545.79969 811.39285 441.34723
## 2014 405.66825 2092.45192 643.26178 797.24338 708.30613 389.36846
## 2015 1428.15031 593.52078 619.52684 913.62659 703.11334 539.77191
## 2016 2223.80337 456.30742 715.20587 756.16663 791.67218 637.28563
## 2017 2604.58586 1140.22051 1078.63653 1103.29322 928.41949 742.39402
## 2018 1695.17386 1035.94201 1075.27013 999.38822 1026.78169 465.18491
## 2019 1323.56631 1497.04385 1400.45661 1499.49687 1110.19777 935.76984
## 2020 1067.22370 1128.19519 1159.70107 738.52056 1396.10786 609.86422
## 2021 636.42925 756.16900Lampungoutflowtimeseries <- ts(dataoutflowperbulan$`Lampung`, frequency=12, start=c(2011,1))
Lampungoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 171.73514 219.94503 342.64595 449.19497 435.48670 560.37199
## 2012 158.37385 143.61587 394.89727 507.72792 767.30148 655.28330
## 2013 22.45428 29.23682 110.38391 131.24521 202.68550 265.22837
## 2014 176.19089 461.64557 620.25400 823.10212 860.99213 627.46225
## 2015 79.28158 339.63124 533.63173 1128.60610 824.42210 1345.73686
## 2016 90.45391 366.34626 546.39793 569.14521 878.53762 3098.46776
## 2017 237.91153 511.86310 849.69294 966.64675 1462.29777 3500.09080
## 2018 318.72999 882.14666 1174.25565 998.17193 2665.43895 2743.98079
## 2019 404.72220 917.99585 1094.90498 1598.16522 4619.20707 177.59795
## 2020 456.44219 786.94826 1872.12587 872.29617 2180.29038 535.27930
## 2021 101.59299 535.40874 1170.44151 1897.92824 2151.89979 841.46432
## Jul Aug Sep Oct Nov Dec
## 2011 666.16768 1300.12070 85.77778 360.29523 363.14330 769.51399
## 2012 1070.05511 1224.03581 191.19995 311.82312 165.81554 785.51667
## 2013 716.58596 270.42444 682.06111 561.75263 495.94527 1083.01386
## 2014 2409.46995 269.11197 419.94532 498.62539 574.09363 598.25583
## 2015 2563.10561 767.93798 447.47874 410.26991 567.34463 938.38091
## 2016 500.52865 1026.05478 1034.08560 685.71598 788.86106 850.91234
## 2017 331.22315 1081.53768 589.27201 743.74975 1260.73121 1823.76710
## 2018 608.59506 813.89609 640.85045 760.05619 901.58457 1217.68173
## 2019 1207.01071 973.97942 907.99564 781.79512 1193.23921 1749.78046
## 2020 1538.36508 948.45298 1152.95654 1422.69234 897.20976 1210.34936
## 2021 1102.00325 249.35351plot.ts(Lampunginflowtimeseries)
plot.ts(Lampungoutflowtimeseries)
Lampungintimeseriescomponents <- decompose(Lampunginflowtimeseries)
Lampungintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2012 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2013 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2014 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2015 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2016 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2017 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2018 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2019 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2020 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2021 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## Jul Aug Sep Oct Nov Dec
## 2011 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2012 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2013 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2014 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2015 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2016 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2017 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2018 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2019 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2020 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2021 320.071011 118.042127Lampungouttimeseriescomponents <- decompose(Lampungoutflowtimeseries)
Lampungouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2012 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2013 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2014 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2015 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2016 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2017 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2018 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2019 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2020 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2021 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## Jul Aug Sep Oct Nov Dec
## 2011 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2012 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2013 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2014 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2015 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2016 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2017 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2018 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2019 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2020 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2021 308.82481 14.24713plot(Lampungintimeseriescomponents$seasonal,type = "l", col = "magenta")
lines(Lampungouttimeseriescomponents$seasonal,col="dodger blue")
legend("top",c("Inflow","Outflow"),fill=c("magenta","dodger blue"))
plot(Lampungintimeseriescomponents$trend,type = "l", col = "magenta")
lines(Lampungouttimeseriescomponents$trend,col="dodger blue")
legend("top",c("Inflow","Outflow"),fill=c("magenta","dodger blue"))
plot(Lampungintimeseriescomponents$random ,type = "l", col = "magenta")
lines(Lampungouttimeseriescomponents$random,col="dodger blue")
legend("top",c("Inflow","Outflow"),fill=c("magenta","dodger blue"))
plot(Lampungintimeseriescomponents$figure ,type = "l", col = "magenta")
lines(Lampungouttimeseriescomponents$figure,col="dodger blue")
legend("top",c("Inflow","Outflow"),fill=c("magenta","dodger blue"))
REFERENSI:
https://rpubs.com/suhartono-uinmaliki/861286
https://www.bi.go.id/id/fungsi-utama/sistem-pembayaran/pengelolaan-rupiah/default.aspx