Visualisasi Nilai Inflow dan Outflow Di Jambi
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 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 Jambi setiap periode
plot(datainflow$Tahun,datainflow$`Jambi`,type = "l", col= "steelblue")
2.Visualisasi Prediksi Data outflow Uang Kartal Jambi setiap periode
plot(dataoutflow$Tahun,dataoutflow$`Jambi`,type = "l", col= "red")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Jambi Setiap Periode
plot(datainflow$Tahun,datainflow$`Jambi`,type = "l", col= "steelblue")
lines(dataoutflow$Tahun,dataoutflow$`Jambi`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Jambi 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$`Jambi`, type = "l", col = "green")
lines(dataoutflowperbulan$`Jambi`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))
Jambitimeseries <- datainflowperbulan$`Jambi`
plot.ts(Jambitimeseries , type = "l", col = "green")
logJambi <- log(datainflowperbulan$`Jambi`)
plot.ts(logJambi)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Jambi
Jambiinflowtimeseries <- ts(datainflowperbulan$`Jambi`, frequency=12, start=c(2011,1))
Jambiinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 48.21238 39.91336 202.77581 76.36759 102.29337 80.38363
## 2012 214.78357 185.06614 118.25569 112.18712 176.73267 131.65442
## 2013 440.25724 250.16557 156.40296 131.70444 80.43460 90.88444
## 2014 648.84622 443.17728 218.60749 372.98546 277.49781 326.07002
## 2015 800.91577 310.67803 334.27000 339.99797 285.21811 266.80514
## 2016 723.86727 399.44327 227.89071 207.32596 294.89205 265.25147
## 2017 436.71704 349.18620 374.44420 291.87853 265.93193 109.35945
## 2018 850.92308 423.79251 432.57396 284.21732 331.44473 943.33760
## 2019 928.32921 508.44605 501.71263 395.87576 375.81227 1377.08370
## 2020 929.25223 453.21208 375.57835 488.00832 366.02264 926.36280
## 2021 1319.31010 533.89020 481.47669 442.30053 954.47189 568.16022
## Jul Aug Sep Oct Nov Dec
## 2011 118.45074 91.88117 618.33464 137.23519 238.83742 112.93547
## 2012 178.67562 446.70847 180.60249 96.89252 190.29249 106.61224
## 2013 150.73569 696.17818 239.01380 381.11280 240.84581 189.04884
## 2014 228.38825 1336.65537 383.31015 366.82210 328.60113 238.13597
## 2015 1033.05014 473.13670 295.54859 329.75416 266.79923 241.96031
## 2016 1069.41796 211.81993 325.26906 251.99989 234.81316 186.17002
## 2017 1008.96424 331.35488 369.25742 288.45059 300.80490 277.28824
## 2018 555.66909 452.09732 390.12811 409.82051 356.98477 225.60052
## 2019 517.64046 582.60662 370.00861 477.26284 302.21112 149.17703
## 2020 418.88012 362.62433 363.94528 290.43227 404.08403 249.99980
## 2021 337.72947 342.40788Jambioutflowtimeseries <- ts(dataoutflowperbulan$`Jambi`, frequency=12, start=c(2011,1))
Jambioutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 297.46348 280.08970 341.37188 474.26014 371.36905 540.43609
## 2012 133.61579 321.29557 315.41057 373.26078 441.58952 474.63459
## 2013 110.31731 184.50535 223.54744 235.42017 450.54670 349.51626
## 2014 351.35683 459.63127 637.62828 526.41165 683.34064 651.89272
## 2015 249.99472 486.10988 549.06994 721.86428 701.16932 931.14718
## 2016 229.69662 442.46621 487.32817 572.51965 587.13872 1610.89703
## 2017 394.17886 553.63581 500.03923 530.31764 570.86673 1961.91565
## 2018 275.03184 451.87980 498.71186 687.34280 1222.83919 1579.32715
## 2019 218.20233 534.52562 559.51510 895.65817 2018.12386 147.10847
## 2020 230.43948 421.99569 606.04929 713.68012 1262.75583 143.79548
## 2021 54.41456 487.87292 732.48101 1261.14201 1578.66374 642.31328
## Jul Aug Sep Oct Nov Dec
## 2011 428.10203 1056.05643 92.78528 295.39728 272.21261 767.15036
## 2012 330.20592 835.74847 221.85612 472.49384 299.07579 794.04754
## 2013 839.48154 339.88048 732.69193 819.24007 782.02490 1235.18658
## 2014 1929.38736 274.46904 553.86575 703.65271 588.68032 1000.86095
## 2015 1582.71912 395.76377 549.45261 479.75684 631.21748 1046.24662
## 2016 456.38157 430.25770 842.64910 521.69293 648.58138 944.35648
## 2017 212.49734 680.41258 470.55865 568.53590 820.95090 1169.98413
## 2018 391.43773 555.29629 475.32140 545.11918 735.03562 1042.05433
## 2019 717.81375 656.73797 617.28665 719.15618 727.75492 1392.15834
## 2020 633.64958 610.36918 689.06184 1124.09728 807.10093 1706.97368
## 2021 664.55917 624.91746plot.ts(Jambiinflowtimeseries)
plot.ts(Jambioutflowtimeseries)
Jambiintimeseriescomponents <- decompose(Jambiinflowtimeseries)
Jambiintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2012 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2013 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2014 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2015 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2016 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2017 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2018 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2019 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2020 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2021 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## Jul Aug Sep Oct Nov Dec
## 2011 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2012 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2013 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2014 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2015 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2016 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2017 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2018 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2019 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2020 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2021 156.97755 120.14214Jambiouttimeseriescomponents <- decompose(Jambioutflowtimeseries)
Jambiouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2012 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2013 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2014 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2015 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2016 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2017 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2018 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2019 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2020 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## 2021 -429.76166 -219.28351 -144.71768 -53.62864 238.04681 221.42655
## Jul Aug Sep Oct Nov Dec
## 2011 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2012 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2013 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2014 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2015 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2016 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2017 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2018 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2019 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2020 120.11152 -48.40994 -109.85159 -14.39860 -16.35840 456.82513
## 2021 120.11152 -48.40994plot(Jambiintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(Jambiouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Jambiintimeseriescomponents$trend,type = "l", col = "orange")
lines(Jambiouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Jambiintimeseriescomponents$random ,type = "l", col = "orange")
lines(Jambiouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Jambiintimeseriescomponents$figure ,type = "l", col = "orange")
lines(Jambiouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))