Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sumatera Barat
Heny Rimadana, Prof. Dr. Suhartono, M.Kom
2/19/2022
Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang
Fakultas : Sains dan Teknologi
Jurusan : Teknik Informatika
Mata Kuliah : Linier Algebra
Berikut contoh penerapan visualisasi prediksi data inflow & outflow pada sumatera barat dengan menggunakan pemerograman pada Bahasa R
library(readxl)
## Warning: package 'readxl' was built under R version 4.1.2
datainflow <- read_excel(path = "inflow sumatera.xlsx")
datainflow
## # 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 Bellitung` <dbl>
library(readxl)
dataoutflow <- read_excel(path = "outflow sumatera.xlsx")
dataoutflow
## # 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 Bellitung` <dbl>0.1 1.Visualisasi Prediksi Data Inflow Uang Kartal Sumatera Barat Setiap Periode
plot(datainflow$Tahun,datainflow$`Sumatera Barat`,type = "l", col= "steelblue")
0.2 2.Visualisasi Prediksi Data Outflow Uang Kartal di Sumatera Barat Setiap Periode
plot(dataoutflow$Tahun,dataoutflow$`Sumatera Barat`,type = "l", col= "red")
0.3 3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sumatera Barat Setiap Periode
plot(datainflow$Tahun,datainflow$`Sumatera Barat`,type = "l", col= "steelblue")
lines(dataoutflow$Tahun,dataoutflow$`Sumatera Barat`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
0.4 4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sumatera Barat Setiap Bulan
library(readxl)
datainflowperbulan <- read_excel(path = "inflowbulanan.xlsx")
dataoutflowperbulan <- read_excel(path = "outflowbulanan.xlsx")
datainflowperbulan
## # A tibble: 128 x 12
## Bulanan 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
## Bulanan 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$`Sumatera Barat`, type = "l", col = "red")
lines(dataoutflowperbulan$`Sumatera Barat`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("red","yellow"))
SumateraBarattimeseries <- datainflowperbulan$`Sumatera Barat`
plot.ts(SumateraBarattimeseries , type = "l", col = "green")
logSumateraBarat <- log(datainflowperbulan$`Sumatera Barat`)
plot.ts(logSumateraBarat)
0.5 5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Sumatera Barat
SumateraBaratinflowtimeseries <- ts(datainflowperbulan$`Sumatera Barat`, frequency=12, start=c(2011,1))
SumateraBaratinflowtimeseries
## Jan Feb Mar Apr May Jun Jul
## 2011 544.5248 450.0701 849.2939 539.1026 691.9377 592.4192 799.5802
## 2012 1130.4905 865.3519 854.9514 704.9590 885.0385 641.2570 1038.4298
## 2013 1776.9203 1112.8960 940.8829 994.6862 1107.1890 1086.4650 1303.0975
## 2014 1675.2029 1111.3808 924.0093 993.2328 762.4694 866.8874 675.1555
## 2015 1698.0899 904.5427 969.6610 836.3249 855.4427 1045.4934 2161.9387
## 2016 1751.8196 892.1499 904.6083 737.9714 919.1321 720.4721 2928.9035
## 2017 1850.5169 1143.2622 1287.3335 1037.7823 1173.4844 683.3602 2902.9224
## 2018 2037.4366 957.8346 732.3303 1043.6172 956.1836 2214.6015 2449.9422
## 2019 1890.0168 845.6557 917.9565 986.2518 810.4107 3290.2635 1379.9442
## 2020 1936.5593 867.9322 593.6931 586.1949 460.8289 1752.8809 720.9419
## 2021 2463.1456 1078.7217 996.1128 924.2523 2033.1787 1301.2214 934.1477
## Aug Sep Oct Nov Dec
## 2011 586.3581 2176.2413 787.3761 854.4358 513.2068
## 2012 1339.7732 1507.8169 789.7558 883.7977 550.4838
## 2013 2173.6578 1202.3046 933.7316 875.4979 548.6130
## 2014 3114.2115 1200.3284 1157.9625 931.1027 691.0219
## 2015 1729.1363 824.0283 995.3346 750.3287 538.4899
## 2016 1145.6062 1048.3006 1050.2491 1005.0248 973.9955
## 2017 1503.0438 1122.1439 1047.2614 883.3420 677.3816
## 2018 1185.0947 1199.5619 1008.1251 776.0709 497.4198
## 2019 1194.5156 1066.1918 1093.7082 771.6151 503.1632
## 2020 934.1740 842.2214 604.4694 893.2831 502.3578
## 2021 1017.1201
SumateraBaratoutflowtimeseries <- ts(dataoutflowperbulan$`Sumatera Barat`, frequency=12, start=c(2011,1))
SumateraBaratoutflowtimeseries
## Jan Feb Mar Apr May Jun
## 2011 306.70068 227.74199 347.23365 335.95990 327.77383 399.24039
## 2012 214.52616 252.76902 462.17950 577.54488 461.72280 623.94257
## 2013 245.10797 218.45108 398.34203 317.45463 461.02830 471.02622
## 2014 185.88126 273.86294 480.13567 452.26115 466.95347 548.54011
## 2015 124.28159 443.52843 443.34413 514.88579 503.17081 926.50648
## 2016 140.03323 351.99398 316.41743 604.36993 757.45169 2598.20471
## 2017 349.10531 710.49354 848.72339 860.68821 999.67421 3176.59985
## 2018 55.96053 302.53616 543.51806 570.24349 1461.73993 2601.75460
## 2019 75.55494 370.26231 613.28838 952.67623 3692.93346 50.39067
## 2020 102.48174 308.36325 782.28278 819.13541 2242.07887 34.07573
## 2021 86.54225 374.74081 559.24066 1554.62334 2167.68623 295.68386
## Jul Aug Sep Oct Nov Dec
## 2011 448.56438 1376.25990 147.70279 298.57216 349.75474 734.22520
## 2012 543.65577 1260.36359 163.22296 437.83317 405.63471 1030.89819
## 2013 1130.65362 773.18744 411.62158 536.88884 421.89894 1125.35118
## 2014 2100.82357 115.32964 393.25698 416.17580 555.13227 1071.69548
## 2015 2153.22221 161.12169 337.86600 346.21304 452.70749 1063.81167
## 2016 636.60428 298.35824 592.36023 470.20911 815.03093 1616.78339
## 2017 151.96773 583.16929 372.26254 511.67734 738.88167 1451.21128
## 2018 113.42245 401.53968 287.98036 398.91845 512.61803 1196.57690
## 2019 445.31828 672.32642 403.02094 428.11685 511.72653 1249.35115
## 2020 651.14472 565.58335 343.19704 792.57966 483.75028 1638.08473
## 2021 684.83394 217.18849
plot.ts(SumateraBaratinflowtimeseries)
plot.ts(SumateraBaratoutflowtimeseries)
SumateraBaratintimeseriescomponents <- decompose(SumateraBaratinflowtimeseries)
SumateraBaratintimeseriescomponents$seasonal
## Jan Feb Mar Apr May Jun
## 2011 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2012 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2013 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2014 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2015 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2016 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2017 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2018 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2019 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2020 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2021 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## Jul Aug Sep Oct Nov Dec
## 2011 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2012 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2013 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2014 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2015 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2016 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2017 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2018 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2019 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2020 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2021 527.87634 371.73426
SumateraBaratouttimeseriescomponents <- decompose(SumateraBaratoutflowtimeseries)
SumateraBaratouttimeseriescomponents$seasonal
## Jan Feb Mar Apr May Jun
## 2011 -535.28958 -328.69192 -132.69390 -49.10511 545.48678 538.93605
## 2012 -535.28958 -328.69192 -132.69390 -49.10511 545.48678 538.93605
## 2013 -535.28958 -328.69192 -132.69390 -49.10511 545.48678 538.93605
## 2014 -535.28958 -328.69192 -132.69390 -49.10511 545.48678 538.93605
## 2015 -535.28958 -328.69192 -132.69390 -49.10511 545.48678 538.93605
## 2016 -535.28958 -328.69192 -132.69390 -49.10511 545.48678 538.93605
## 2017 -535.28958 -328.69192 -132.69390 -49.10511 545.48678 538.93605
## 2018 -535.28958 -328.69192 -132.69390 -49.10511 545.48678 538.93605
## 2019 -535.28958 -328.69192 -132.69390 -49.10511 545.48678 538.93605
## 2020 -535.28958 -328.69192 -132.69390 -49.10511 545.48678 538.93605
## 2021 -535.28958 -328.69192 -132.69390 -49.10511 545.48678 538.93605
## Jul Aug Sep Oct Nov Dec
## 2011 171.98463 -44.52431 -321.49495 -208.98678 -160.73573 525.11481
## 2012 171.98463 -44.52431 -321.49495 -208.98678 -160.73573 525.11481
## 2013 171.98463 -44.52431 -321.49495 -208.98678 -160.73573 525.11481
## 2014 171.98463 -44.52431 -321.49495 -208.98678 -160.73573 525.11481
## 2015 171.98463 -44.52431 -321.49495 -208.98678 -160.73573 525.11481
## 2016 171.98463 -44.52431 -321.49495 -208.98678 -160.73573 525.11481
## 2017 171.98463 -44.52431 -321.49495 -208.98678 -160.73573 525.11481
## 2018 171.98463 -44.52431 -321.49495 -208.98678 -160.73573 525.11481
## 2019 171.98463 -44.52431 -321.49495 -208.98678 -160.73573 525.11481
## 2020 171.98463 -44.52431 -321.49495 -208.98678 -160.73573 525.11481
## 2021 171.98463 -44.52431
plot(SumateraBaratintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(SumateraBaratouttimeseriescomponents$seasonal,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("orange","blue"))
plot(SumateraBaratintimeseriescomponents$trend,type = "l", col = "orange")
lines(SumateraBaratouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(SumateraBaratintimeseriescomponents$random ,type = "l", col = "black")
lines(SumateraBaratouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("black","grey"))
plot(SumateraBaratintimeseriescomponents$figure ,type = "l", col = "orange")
lines(SumateraBaratouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
#Referensi
https://ejurnal.its.ac.id/index.php/sains_seni/article/download/12401/2433#:
https://www.bi.go.id/id/fungsi-utama/sistem-pembayaran/pengelolaan-rupiah/default.aspx