Visualisasi Data Inflow dan Outflow Di Lampung
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 Lampung 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 Lampung setiap periode
plot(datainflow$Tahun,datainflow$`Lampung`,type = "l", col= "steelblue")
2.Visualisasi Prediksi Data outflow Uang Kartal Lampung setiap periode
plot(dataoutflow$Tahun,dataoutflow$`Lampung`,type = "l", col= "red")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Lampung Setiap Periode
plot(datainflow$Tahun,datainflow$`Lampung`,type = "l", col= "steelblue")
lines(dataoutflow$Tahun,dataoutflow$`Lampung`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Lampung 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$`Lampung`, type = "l", col = "green")
lines(dataoutflowperbulan$`Lampung`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))
Lampungtimeseries <- datainflowperbulan$`Lampung`
plot.ts(Lampungtimeseries , type = "l", col = "green")
logLampung <- log(datainflowperbulan$`Lampung`)
plot.ts(logLampung)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Lampung
Lampunginflowtimeseries <- ts(datainflowperbulan$`Lampung`, frequency=12, start=c(2011,1))
Lampunginflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 621.71179 358.56622 550.36496 340.44445 402.03710 573.97617
## 2012 1054.26685 666.52412 517.06758 282.85569 344.24522 206.42495
## 2013 234.65931 117.20955 170.13195 75.87996 74.77127 36.67275
## 2014 1433.51885 725.39736 590.72966 568.85388 487.63656 605.40178
## 2015 1360.19086 508.30661 417.04559 277.84130 383.31675 415.35766
## 2016 1390.23556 804.10302 598.27040 555.00940 286.75963 158.32177
## 2017 1134.20195 690.26841 655.03228 794.03117 675.69061 531.31408
## 2018 1802.74124 949.48387 814.34998 689.44512 370.42214 2491.07250
## 2019 2147.18917 921.91017 900.51341 1104.01903 842.23750 3364.05676
## 2020 2551.78237 1446.11620 939.33048 955.09673 1276.19200 1889.39473
## 2021 2555.46285 1243.57068 936.61307 1164.78835 2166.96750 1237.16558
## Jul Aug Sep Oct Nov Dec
## 2011 656.24294 542.87169 1775.98512 623.85717 801.97986 442.08517
## 2012 412.76796 1054.75071 949.52907 542.34897 684.54376 253.90505
## 2013 44.55553 417.59331 503.81158 545.79969 811.39285 441.34723
## 2014 405.66825 2092.45192 643.26178 797.24338 708.30613 389.36846
## 2015 1428.15031 593.52078 619.52684 913.62659 703.11334 539.77191
## 2016 2223.80337 456.30742 715.20587 756.16663 791.67218 637.28563
## 2017 2604.58586 1140.22051 1078.63653 1103.29322 928.41949 742.39402
## 2018 1695.17386 1035.94201 1075.27013 999.38822 1026.78169 465.18491
## 2019 1323.56631 1497.04385 1400.45661 1499.49687 1110.19777 935.76984
## 2020 1067.22370 1128.19519 1159.70107 738.52056 1396.10786 609.86422
## 2021 636.42925 756.16900Lampungoutflowtimeseries <- ts(dataoutflowperbulan$`Lampung`, frequency=12, start=c(2011,1))
Lampungoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 171.73514 219.94503 342.64595 449.19497 435.48670 560.37199
## 2012 158.37385 143.61587 394.89727 507.72792 767.30148 655.28330
## 2013 22.45428 29.23682 110.38391 131.24521 202.68550 265.22837
## 2014 176.19089 461.64557 620.25400 823.10212 860.99213 627.46225
## 2015 79.28158 339.63124 533.63173 1128.60610 824.42210 1345.73686
## 2016 90.45391 366.34626 546.39793 569.14521 878.53762 3098.46776
## 2017 237.91153 511.86310 849.69294 966.64675 1462.29777 3500.09080
## 2018 318.72999 882.14666 1174.25565 998.17193 2665.43895 2743.98079
## 2019 404.72220 917.99585 1094.90498 1598.16522 4619.20707 177.59795
## 2020 456.44219 786.94826 1872.12587 872.29617 2180.29038 535.27930
## 2021 101.59299 535.40874 1170.44151 1897.92824 2151.89979 841.46432
## Jul Aug Sep Oct Nov Dec
## 2011 666.16768 1300.12070 85.77778 360.29523 363.14330 769.51399
## 2012 1070.05511 1224.03581 191.19995 311.82312 165.81554 785.51667
## 2013 716.58596 270.42444 682.06111 561.75263 495.94527 1083.01386
## 2014 2409.46995 269.11197 419.94532 498.62539 574.09363 598.25583
## 2015 2563.10561 767.93798 447.47874 410.26991 567.34463 938.38091
## 2016 500.52865 1026.05478 1034.08560 685.71598 788.86106 850.91234
## 2017 331.22315 1081.53768 589.27201 743.74975 1260.73121 1823.76710
## 2018 608.59506 813.89609 640.85045 760.05619 901.58457 1217.68173
## 2019 1207.01071 973.97942 907.99564 781.79512 1193.23921 1749.78046
## 2020 1538.36508 948.45298 1152.95654 1422.69234 897.20976 1210.34936
## 2021 1102.00325 249.35351plot.ts(Lampunginflowtimeseries)
plot.ts(Lampungoutflowtimeseries)
Lampungintimeseriescomponents <- decompose(Lampunginflowtimeseries)
Lampungintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2012 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2013 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2014 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2015 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2016 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2017 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2018 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2019 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2020 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## 2021 654.649730 -105.292355 -254.276223 -285.224912 -350.924198 196.297932
## Jul Aug Sep Oct Nov Dec
## 2011 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2012 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2013 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2014 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2015 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2016 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2017 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2018 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2019 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2020 320.071011 118.042127 108.993961 -36.214500 -2.725779 -363.396795
## 2021 320.071011 118.042127Lampungouttimeseriescomponents <- decompose(Lampungoutflowtimeseries)
Lampungouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2012 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2013 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2014 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2015 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2016 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2017 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2018 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2019 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2020 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## 2021 -687.44008 -392.00937 -70.57568 -36.15012 719.35511 546.83661
## Jul Aug Sep Oct Nov Dec
## 2011 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2012 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2013 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2014 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2015 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2016 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2017 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2018 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2019 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2020 308.82481 14.24713 -242.90932 -213.87961 -159.94847 213.64900
## 2021 308.82481 14.24713plot(Lampungintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(Lampungouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Lampungintimeseriescomponents$trend,type = "l", col = "orange")
lines(Lampungouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Lampungintimeseriescomponents$random ,type = "l", col = "orange")
lines(Lampungouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Lampungintimeseriescomponents$figure ,type = "l", col = "orange")
lines(Lampungouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))