Visualisasi dan Prediksi Data Inflow-Outflow Uang Kartal di Sumatera
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 Sumatera setiap periode
datainflowkelasc$Sumatera## [1] 57900.30 65910.57 98369.30 86023.55 86549.30 97763.55 103747.82
## [8] 117494.94 133762.18 109344.58 89270.03plot(datainflowkelasc$Sumatera, type = "l", col = "tomato")
Visualisasi Prediksi Data Outflow Uang Kartal di Sumatera setiap periode
dataOUTflowkelasc$Sumatera## [1] 80092.18 85234.64 103287.72 102338.21 109185.81 121991.73 133605.71
## [8] 135676.35 153484.27 140588.81 86627.29plot(dataOUTflowkelasc$Sumatera, type = "l", col = "cyan")
Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sumatera setiap periode
plot(datainflowkelasc$Sumatera, type = "l", col = "tomato")
lines(dataOUTflowkelasc$Sumatera, type = "l", col = "cyan")
legend("top",c("Inflow","Outflow"),fill=c("tomato","cyan"))
Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sumatera 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$Sumatera, type = "l", col = "tomato")
lines(dataoutflowperbulan$Sumatera,col="cyan")
legend("top",c("Inflow","Outflow"),fill=c("tomato","cyan"))
Sumateratimeseries <- datainflowperbulan$Sumatera
plot.ts(Sumateratimeseries , type = "l", col = "tomato")
logSumatera <- log(datainflowperbulan$Sumatera)
plot.ts(logSumatera)
library(TTR)## Warning: package 'TTR' was built under R version 4.1.2SumateraSMA3 <- SMA(datainflowperbulan$Sumatera ,n=3)
plot.ts(SumateraSMA3 )
library("TTR")
SumateraSMA3 <- SMA(datainflowperbulan$Sumatera ,n=8)
plot.ts(SumateraSMA3 )
Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Sumatera
Sumaterainflowtimeseries <- ts(datainflowperbulan$Sumatera, frequency=12, start=c(2011,1))
Sumaterainflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 4164.243 3337.607 4878.287 3156.548 3821.275 3686.394 4369.643
## 2012 7371.435 5443.242 5022.248 4102.575 5321.981 4064.952 5489.699
## 2013 13436.780 8035.017 7017.142 8267.947 7623.367 6961.815 7552.290
## 2014 11612.677 6964.701 5238.644 5988.730 4921.418 5591.202 3440.204
## 2015 12838.000 5173.687 5600.472 4954.255 5358.552 5936.657 15050.224
## 2016 13692.690 7760.507 6313.597 5254.795 6761.434 5066.314 20548.504
## 2017 12734.711 7752.686 7568.883 6638.224 7317.874 4071.240 21208.720
## 2018 16240.858 7668.179 7130.231 7628.992 5973.344 19402.076 14326.890
## 2019 17413.907 9281.546 8215.984 9406.456 7523.072 26667.739 11014.410
## 2020 19330.620 10365.349 7128.873 6536.998 7788.132 14946.781 8278.451
## 2021 21182.291 9983.745 8648.731 9095.626 16275.454 10211.629 6787.420
## Aug Sep Oct Nov Dec
## 2011 3668.498 12874.594 4776.883 5669.993 3496.335
## 2012 9422.659 6813.338 4563.922 5452.494 2842.029
## 2013 19523.108 5265.619 6181.279 5347.888 3157.046
## 2014 19746.407 6305.927 6798.485 5515.775 3899.380
## 2015 8915.131 5710.106 6763.497 6087.162 4161.556
## 2016 6548.412 7498.570 6952.295 6098.330 5268.100
## 2017 8722.990 8250.898 7610.729 7122.755 4748.106
## 2018 9119.047 8886.660 8429.308 8078.990 4610.368
## 2019 10707.883 9462.332 10195.256 8492.726 5380.872
## 2020 8012.437 7559.106 5735.149 8462.623 5200.062
## 2021 7085.136Sumateraoutflowtimeseries <- ts(dataoutflowperbulan$Sumatera, frequency=12, start=c(2011,1))
Sumateraoutflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 3441.614 3989.113 4228.628 6721.276 5787.181 7394.536 7154.223
## 2012 3200.178 4100.054 6605.179 6665.551 7147.179 8560.319 7711.993
## 2013 2221.436 4621.158 8219.574 4613.748 8423.251 7790.216 17485.108
## 2014 4289.908 4820.657 7088.166 8015.452 7757.313 8157.185 24722.650
## 2015 2036.392 5682.352 6300.508 10051.597 7592.788 12421.852 22934.645
## 2016 2804.053 4909.740 6985.628 8649.278 10859.812 28813.953 6455.632
## 2017 4855.706 6495.905 9234.822 9234.883 11638.176 29889.710 3252.637
## 2018 2424.451 7487.879 10455.312 9952.146 19165.027 25439.136 6324.910
## 2019 3735.569 7719.811 11089.472 15127.060 37664.505 2465.417 10575.813
## 2020 4693.754 6958.705 12667.832 11775.906 19644.928 3971.849 12710.177
## 2021 1990.678 6099.024 9638.351 19930.265 22004.413 7748.386 11650.666
## Aug Sep Oct Nov Dec
## 2011 16042.967 1914.778 5173.616 5609.913 12634.335
## 2012 13610.489 3180.756 6273.015 5018.851 13161.070
## 2013 10207.967 6806.163 8014.259 8355.285 16529.555
## 2014 2377.454 6171.922 7655.389 7005.319 14276.798
## 2015 4668.410 6733.060 5783.016 8056.207 16924.980
## 2016 7937.744 10071.108 7571.519 9563.416 17369.845
## 2017 11015.435 6693.301 8559.331 12083.466 20652.339
## 2018 10042.081 7060.453 8155.825 9944.104 19225.025
## 2019 11780.050 8535.685 8868.257 12462.606 23460.023
## 2020 9744.450 9247.241 14431.727 9435.013 25307.224
## 2021 7565.509plot.ts(Sumaterainflowtimeseries)
plot.ts(Sumateraoutflowtimeseries)
Sumateraintimeseriescomponents <- decompose(Sumaterainflowtimeseries)
Sumateraintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 6152.5962 -614.2449 -1728.6408 -1759.2576 -1797.7115 1972.5387
## 2012 6152.5962 -614.2449 -1728.6408 -1759.2576 -1797.7115 1972.5387
## 2013 6152.5962 -614.2449 -1728.6408 -1759.2576 -1797.7115 1972.5387
## 2014 6152.5962 -614.2449 -1728.6408 -1759.2576 -1797.7115 1972.5387
## 2015 6152.5962 -614.2449 -1728.6408 -1759.2576 -1797.7115 1972.5387
## 2016 6152.5962 -614.2449 -1728.6408 -1759.2576 -1797.7115 1972.5387
## 2017 6152.5962 -614.2449 -1728.6408 -1759.2576 -1797.7115 1972.5387
## 2018 6152.5962 -614.2449 -1728.6408 -1759.2576 -1797.7115 1972.5387
## 2019 6152.5962 -614.2449 -1728.6408 -1759.2576 -1797.7115 1972.5387
## 2020 6152.5962 -614.2449 -1728.6408 -1759.2576 -1797.7115 1972.5387
## 2021 6152.5962 -614.2449 -1728.6408 -1759.2576 -1797.7115 1972.5387
## Jul Aug Sep Oct Nov Dec
## 2011 3070.5444 2282.6974 -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2012 3070.5444 2282.6974 -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2013 3070.5444 2282.6974 -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2014 3070.5444 2282.6974 -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2015 3070.5444 2282.6974 -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2016 3070.5444 2282.6974 -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2017 3070.5444 2282.6974 -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2018 3070.5444 2282.6974 -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2019 3070.5444 2282.6974 -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2020 3070.5444 2282.6974 -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2021 3070.5444 2282.6974Sumateraouttimeseriescomponents <- decompose(Sumateraoutflowtimeseries)
Sumateraouttimeseriescomponents$seasonal## Jan Feb Mar Apr May
## 2011 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681
## 2012 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681
## 2013 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681
## 2014 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681
## 2015 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681
## 2016 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681
## 2017 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681
## 2018 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681
## 2019 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681
## 2020 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681
## 2021 -6834.998330 -4154.095027 -1069.366771 -541.827422 4487.308681
## Jun Jul Aug Sep Oct
## 2011 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549
## 2012 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549
## 2013 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549
## 2014 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549
## 2015 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549
## 2016 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549
## 2017 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549
## 2018 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549
## 2019 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549
## 2020 4146.113221 2196.088359 3.268476 -3129.321269 -1799.750549
## 2021 4146.113221 2196.088359 3.268476
## Nov Dec
## 2011 -1217.537318 7914.117948
## 2012 -1217.537318 7914.117948
## 2013 -1217.537318 7914.117948
## 2014 -1217.537318 7914.117948
## 2015 -1217.537318 7914.117948
## 2016 -1217.537318 7914.117948
## 2017 -1217.537318 7914.117948
## 2018 -1217.537318 7914.117948
## 2019 -1217.537318 7914.117948
## 2020 -1217.537318 7914.117948
## 2021plot(Sumateraintimeseriescomponents$seasonal,type = "l", col = "tomato")
lines(Sumateraouttimeseriescomponents$seasonal,col="cyan")
legend("top",c("Inflow","Outflow"),fill=c("tomato","cyan"))
plot(Sumateraintimeseriescomponents$trend,type = "l", col = "tomato")
lines(Sumateraouttimeseriescomponents$trend,col="cyan")
legend("top",c("Inflow","Outflow"),fill=c("tomato","cyan"))
plot(Sumateraintimeseriescomponents$random ,type = "l", col = "tomato")
lines(Sumateraouttimeseriescomponents$random,col="cyan")
legend("top",c("Inflow","Outflow"),fill=c("tomato","cyan"))
plot(Sumateraintimeseriescomponents$figure ,type = "l", col = "tomato")
lines(Sumateraouttimeseriescomponents$figure,col="cyan")
legend("top",c("Inflow","Outflow"),fill=c("tomato","cyan"))
REFERENSI:
https://rpubs.com/suhartono-uinmaliki/861286
https://www.bi.go.id/id/fungsi-utama/sistem-pembayaran/pengelolaan-rupiah/default.aspx