Visualisasi Data Inflow dan Outflow Di Sumatera Barat
Fauzil Hidayatullah, Prof.Dr.Suhartono,M.Kom
2/20/2022
Universitas : Universitas Islam Negeri Maulana Malik Ibrahim Malang
Jurusan : Teknik Informatika
Inflow - Outflow
Inflow disebut investasi sebagai langsung dalam ekonomi pelaporan, termasuk semua kewajiban dan aset yang ditransfer antara perusahaan investasi langsung penduduk dan investor langsung mereka. Ini juga mencakup transfer aset dan kewajiban antara perusahaan yang bertempat tinggal dan yang tidak residen, jika orang tua pengendali utama adalah bukan penduduk.
Outflow disebut sebagai investasi langsung di luar negeri, termasuk aset dan kewajiban yang ditransfer antara investor langsung penduduk dan perusahaan investasi langsung mereka.
contoh penerapan visualisasi prediksi data Inflow-Outflow Uang Kartal di Sumatera Barat menggunakan bahasa pemrograman R.
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2datainflow <- read_excel(path = "inflowTahunan.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 = "outflowTahunan.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>1.Visualisasi Prediksi Data Inflow Uang Kartal Sumatera Barat setiap periode
plot(datainflow$Tahun,datainflow$`Sumatera Barat`,type = "l", col= "steelblue")
2.Visualisasi Prediksi Data outflow Uang Kartal Sumatera Barat setiap periode
plot(dataoutflow$Tahun,dataoutflow$`Sumatera Barat`,type = "l", col= "red")
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"))
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
## 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
## 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>plot(datainflowperbulan$`Sumatera Barat`, type = "l", col = "green")
lines(dataoutflowperbulan$`Sumatera Barat`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))
SumateraSelatantimeseries <- datainflowperbulan$`Sumatera Barat`
plot.ts(SumateraSelatantimeseries , type = "l", col = "green")
logSumateraSelatan <- log(datainflowperbulan$`Sumatera Barat`)
plot.ts(logSumateraSelatan)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Sumatera Barat
SumateraSelataninflowtimeseries <- ts(datainflowperbulan$`Sumatera Barat`, frequency=12, start=c(2011,1))
SumateraSelataninflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 544.5248 450.0701 849.2939 539.1026 691.9377 592.4192 799.5802
## 2012 1130.4905 865.3519 854.9514 704.9590 885.0385 641.2570 1038.4298
## 2013 1776.9203 1112.8960 940.8829 994.6862 1107.1890 1086.4650 1303.0975
## 2014 1675.2029 1111.3808 924.0093 993.2328 762.4694 866.8874 675.1555
## 2015 1698.0899 904.5427 969.6610 836.3249 855.4427 1045.4934 2161.9387
## 2016 1751.8196 892.1499 904.6083 737.9714 919.1321 720.4721 2928.9035
## 2017 1850.5169 1143.2622 1287.3335 1037.7823 1173.4844 683.3602 2902.9224
## 2018 2037.4366 957.8346 732.3303 1043.6172 956.1836 2214.6015 2449.9422
## 2019 1890.0168 845.6557 917.9565 986.2518 810.4107 3290.2635 1379.9442
## 2020 1936.5593 867.9322 593.6931 586.1949 460.8289 1752.8809 720.9419
## 2021 2463.1456 1078.7217 996.1128 924.2523 2033.1787 1301.2214 934.1477
## Aug Sep Oct Nov Dec
## 2011 586.3581 2176.2413 787.3761 854.4358 513.2068
## 2012 1339.7732 1507.8169 789.7558 883.7977 550.4838
## 2013 2173.6578 1202.3046 933.7316 875.4979 548.6130
## 2014 3114.2115 1200.3284 1157.9625 931.1027 691.0219
## 2015 1729.1363 824.0283 995.3346 750.3287 538.4899
## 2016 1145.6062 1048.3006 1050.2491 1005.0248 973.9955
## 2017 1503.0438 1122.1439 1047.2614 883.3420 677.3816
## 2018 1185.0947 1199.5619 1008.1251 776.0709 497.4198
## 2019 1194.5156 1066.1918 1093.7082 771.6151 503.1632
## 2020 934.1740 842.2214 604.4694 893.2831 502.3578
## 2021 1017.1201SumateraSelatanoutflowtimeseries <- ts(dataoutflowperbulan$`Sumatera Barat`, frequency=12, start=c(2011,1))
SumateraSelatanoutflowtimeseries## 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 217.18849plot.ts(SumateraSelataninflowtimeseries)
plot.ts(SumateraSelatanoutflowtimeseries)
SumateraSelatanintimeseriescomponents <- decompose(SumateraSelataninflowtimeseries)
SumateraSelatanintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2012 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2013 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2014 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2015 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2016 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2017 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2018 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2019 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2020 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## 2021 677.50012 -167.90248 -240.27096 -255.95989 -254.27532 231.31747
## Jul Aug Sep Oct Nov Dec
## 2011 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2012 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2013 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2014 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2015 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2016 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2017 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2018 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2019 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2020 527.87634 371.73426 96.85991 -177.47315 -269.01396 -540.39235
## 2021 527.87634 371.73426SumateraSelatanouttimeseriescomponents <- decompose(SumateraSelatanoutflowtimeseries)
SumateraSelatanouttimeseriescomponents$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 -44.52431plot(SumateraSelatanintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(SumateraSelatanouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(SumateraSelatanintimeseriescomponents$trend,type = "l", col = "orange")
lines(SumateraSelatanouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(SumateraSelatanintimeseriescomponents$random ,type = "l", col = "orange")
lines(SumateraSelatanouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(SumateraSelatanintimeseriescomponents$figure ,type = "l", col = "orange")
lines(SumateraSelatanouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))