Visualisasi Nilai Inflow dan Outflow Uang Kartal Di Bengkulu
Fauzil Hidayatullah, Prof.Dr.Suhartono,M.Kom
2/13/2022
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 Jambi 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 Bengkulu setiap periode
plot(datainflow$Tahun,datainflow$`Bengkulu`,type = "l", col= "steelblue")
2.Visualisasi Prediksi Data outflow Uang Kartal Bengkulu setiap periode
plot(dataoutflow$Tahun,dataoutflow$`Bengkulu`,type = "l", col= "red")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Bengkulu Setiap Periode
plot(datainflow$Tahun,datainflow$`Bengkulu`,type = "l", col= "steelblue")
lines(dataoutflow$Tahun,dataoutflow$`Bengkulu`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Bengkulu 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$`Bengkulu`, type = "l", col = "green")
lines(dataoutflowperbulan$`Bengkulu`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))
Bengkulutimeseries <- datainflowperbulan$`Bengkulu`
plot.ts(Bengkulutimeseries , type = "l", col = "green")
logBengkulu <- log(datainflowperbulan$`Bengkulu`)
plot.ts(logBengkulu)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Jambi
Bengkuluinflowtimeseries <- ts(datainflowperbulan$`Bengkulu`, frequency=12, start=c(2011,1))
Bengkuluinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 122.17640 42.56978 56.79831 27.06372 33.27979 25.84131
## 2012 229.63010 125.41615 65.93120 27.71178 17.46938 17.46938
## 2013 225.33676 240.39147 247.97928 232.80433 158.28819 99.59913
## 2014 708.02522 269.13089 173.04810 221.13003 102.52019 131.58252
## 2015 644.62293 221.83713 163.04665 105.55613 96.35064 84.34825
## 2016 702.39709 293.29774 185.31632 73.73894 119.25824 76.02947
## 2017 705.34454 296.38089 218.07302 108.20777 124.26259 38.37514
## 2018 885.45535 277.07756 207.05547 156.74029 120.71976 669.85657
## 2019 902.06334 384.59633 283.98631 340.23492 256.59610 1294.68991
## 2020 983.83714 517.87037 322.68228 295.68625 330.78731 594.49286
## 2021 1134.14469 507.34820 410.99660 309.79568 798.17998 293.65593
## Jul Aug Sep Oct Nov Dec
## 2011 98.70596 64.44523 430.67254 100.84602 111.67560 39.03351
## 2012 74.43659 207.95245 172.87088 104.67443 134.41372 23.27873
## 2013 135.59282 392.32979 166.69236 194.90184 165.05959 118.56169
## 2014 83.35252 899.76893 204.79900 245.78856 146.50267 75.86238
## 2015 662.75459 223.16428 168.84114 212.90720 127.31721 80.51677
## 2016 661.14587 110.45568 243.85150 175.18164 136.70141 111.48900
## 2017 919.91900 300.75244 296.76196 275.01659 201.18931 135.31315
## 2018 423.32742 286.78781 368.53402 286.96586 286.34575 181.12197
## 2019 381.33964 428.71096 432.36290 498.97557 330.91527 254.67978
## 2020 289.77418 409.26120 438.92378 281.96995 320.24937 185.53660
## 2021 350.87090 355.37500Bengkuluoutflowtimeseries <- ts(dataoutflowperbulan$`Bengkulu`, frequency=12, start=c(2011,1))
Bengkuluoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 43.00021 82.23542 143.53922 246.22066 202.80478 265.84634
## 2012 77.67069 136.45409 214.08931 230.04005 343.95133 343.95133
## 2013 150.23670 309.92998 431.93072 314.02314 742.58906 664.43864
## 2014 184.84757 233.07711 359.39862 524.14915 447.54582 377.69263
## 2015 103.40197 176.91637 236.82757 435.72702 510.20743 474.21976
## 2016 59.75611 134.50325 206.17499 355.34003 506.32330 1581.42961
## 2017 156.75645 191.46206 341.51406 410.43977 612.92546 1597.77779
## 2018 104.78294 200.91583 399.37190 498.39520 866.36789 1137.64484
## 2019 136.77104 354.05007 432.66657 755.79629 1646.68269 168.74806
## 2020 256.84547 331.85653 442.42736 531.24172 969.68490 209.58637
## 2021 95.04035 340.25426 457.19172 920.71828 1096.04779 629.30605
## Jul Aug Sep Oct Nov Dec
## 2011 263.31558 497.98805 73.97831 188.67118 175.22115 377.68102
## 2012 205.01716 360.89097 153.25346 209.32113 202.05658 482.63553
## 2013 1563.65149 783.20289 262.44591 260.53121 382.27823 624.35333
## 2014 949.04614 161.37331 247.44909 317.04213 292.98312 488.31758
## 2015 1085.06420 246.35914 274.35432 250.71305 309.02593 748.71687
## 2016 212.21523 567.18382 238.44064 187.43127 384.85065 729.08792
## 2017 110.49356 216.10078 248.63583 249.51486 472.84165 838.28091
## 2018 233.48894 261.42442 225.52806 344.89425 470.11011 752.32699
## 2019 653.94175 479.32908 380.83854 386.78029 650.26438 795.78060
## 2020 680.85829 483.37874 506.16610 625.26947 575.95459 950.75046
## 2021 676.14611 466.14904plot.ts(Bengkuluinflowtimeseries)
plot.ts(Bengkuluoutflowtimeseries)
Bengkuluintimeseriescomponents <- decompose(Bengkuluinflowtimeseries)
Bengkuluintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2012 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2013 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2014 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2015 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2016 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2017 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2018 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2019 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2020 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## 2021 415.814816 14.800879 -75.680615 -110.480486 -138.457757 46.586240
## Jul Aug Sep Oct Nov Dec
## 2011 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2012 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2013 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2014 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2015 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2016 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2017 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2018 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2019 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2020 99.818044 52.992951 9.648678 -47.713440 -93.764349 -173.564960
## 2021 99.818044 52.992951Bengkuluouttimeseriescomponents <- decompose(Bengkuluoutflowtimeseries)
Bengkuluouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2012 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2013 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2014 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2015 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2016 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2017 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2018 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2019 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2020 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## 2021 -316.47206 -209.72850 -93.99434 12.06698 296.09328 281.49728
## Jul Aug Sep Oct Nov Dec
## 2011 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2012 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2013 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2014 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2015 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2016 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2017 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2018 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2019 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2020 169.42047 -21.85756 -168.85362 -132.06305 -49.05355 232.94467
## 2021 169.42047 -21.85756plot(Bengkuluintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(Bengkuluouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Bengkuluintimeseriescomponents$trend,type = "l", col = "orange")
lines(Bengkuluouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Bengkuluintimeseriescomponents$random ,type = "l", col = "orange")
lines(Bengkuluouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Bengkuluintimeseriescomponents$figure ,type = "l", col = "orange")
lines(Bengkuluouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))