Visualisasi Data Inflow dan Outflow Di Sumatera Selatan
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 Selatan 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 Selatan setiap periode
plot(datainflow$Tahun,datainflow$`Sumatera Selatan`,type = "l", col= "steelblue")
2.Visualisasi Prediksi Data outflow Uang Kartal Sumatera Selatan setiap periode
plot(dataoutflow$Tahun,dataoutflow$`Sumatera Selatan`,type = "l", col= "red")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sumatera Selatan Setiap Periode
plot(datainflow$Tahun,datainflow$`Sumatera Selatan`,type = "l", col= "steelblue")
lines(dataoutflow$Tahun,dataoutflow$`Sumatera Selatan`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sumatera Selatan 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$`Sumatera Selatan`, type = "l", col = "green")
lines(dataoutflowperbulan$`Sumatera Selatan`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))
SumateraSelatantimeseries <- datainflowperbulan$`Sumatera Selatan`
plot.ts(SumateraSelatantimeseries , type = "l", col = "green")
logSumateraSelatan <- log(datainflowperbulan$`Sumatera Selatan`)
plot.ts(logSumateraSelatan)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Sumatera Selatan
SumateraSelataninflowtimeseries <- ts(datainflowperbulan$`Sumatera Selatan`, frequency=12, start=c(2011,1))
SumateraSelataninflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 456.4919 363.4010 661.9322 355.8931 655.5655 445.7856 533.9543
## 2012 847.7118 557.6804 842.2304 433.0244 852.8930 791.5389 572.3589
## 2013 1241.6259 583.4391 369.7243 594.3824 606.8414 451.6471 436.7965
## 2014 1290.5744 697.7210 539.5584 601.5915 496.8451 761.1483 292.8094
## 2015 1373.5722 467.9884 507.1713 316.2315 646.5083 769.0185 2218.0190
## 2016 1724.9435 757.5883 698.3839 580.2943 975.7450 820.2649 3028.2880
## 2017 1247.4278 649.6430 715.8801 816.9179 919.5968 497.3588 3480.9746
## 2018 1386.1845 640.7069 770.9428 855.5950 824.7960 2888.6614 1826.9664
## 2019 1729.8884 787.8768 710.2534 1126.8944 967.7749 3393.1168 1151.6163
## 2020 2054.0128 914.8161 680.3686 665.8459 1019.4927 1432.5599 1025.4901
## 2021 1825.6471 768.0047 782.0909 1035.3983 1947.4074 1072.3469 963.1947
## Aug Sep Oct Nov Dec
## 2011 744.7032 1712.5480 561.2395 979.5828 349.2438
## 2012 1688.2790 825.1074 638.0242 767.6416 309.4864
## 2013 2094.8131 390.1139 941.0992 631.0574 305.7795
## 2014 2458.0650 693.6043 957.7864 685.5728 562.6037
## 2015 1058.1097 827.1355 979.8054 933.4509 700.1903
## 2016 969.6394 966.3684 828.4733 743.3749 658.3444
## 2017 1085.7413 1042.5878 1027.7669 931.9672 659.4133
## 2018 1187.7423 1109.3445 995.0373 993.8724 786.6507
## 2019 1222.7634 1014.0402 1049.8722 925.4907 732.1411
## 2020 933.0685 922.3794 619.9658 929.7524 558.5297
## 2021 711.8520SumateraSelatanoutflowtimeseries <- ts(dataoutflowperbulan$`Sumatera Selatan`, frequency=12, start=c(2011,1))
SumateraSelatanoutflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 665.0204 1327.9789 784.5960 1664.4175 1013.5138 1173.7556 1295.7944
## 2012 585.9661 1172.7767 1566.9525 1156.5310 1230.6371 1367.3602 1140.7971
## 2013 313.1714 522.0305 822.0748 370.0617 820.6871 914.3388 2150.1090
## 2014 681.3193 728.3859 1107.6390 1145.9336 970.1190 1138.7692 3375.7872
## 2015 303.5811 730.8745 947.5489 1528.0217 971.6950 1253.9235 3292.4818
## 2016 291.6833 754.0870 998.3022 1389.9930 1557.6113 3449.6370 1082.9331
## 2017 846.0081 1142.9021 1533.7630 1016.7147 1223.4515 4005.3341 441.6113
## 2018 577.3886 1086.3337 1585.9543 1302.7789 2209.9709 3882.2670 783.2563
## 2019 539.7334 1120.4360 1655.8566 2078.5155 4646.7802 448.9773 1286.7151
## 2020 516.0607 1086.5070 1851.9668 1261.4710 2590.3465 607.8210 1445.3980
## 2021 338.7335 1037.2551 1384.9886 2483.6447 2709.4180 1017.8561 1575.3735
## Aug Sep Oct Nov Dec
## 2011 2621.9702 312.8544 903.8191 872.5556 1887.2803
## 2012 2552.5039 668.3767 1229.9850 806.9955 2121.1970
## 2013 1120.7014 1160.2111 1070.8040 1267.4096 2161.6112
## 2014 255.9222 760.6027 899.5059 1009.9874 1298.2534
## 2015 637.9153 689.0966 528.2202 1125.9204 1474.4510
## 2016 1108.8282 1044.8214 1044.6620 1210.0297 1823.2746
## 2017 1246.7486 781.9704 1137.4882 1686.9596 1918.1924
## 2018 1204.7220 1000.8916 973.8639 1416.3610 1907.2461
## 2019 1315.9151 889.2873 967.7377 1603.9596 2567.3698
## 2020 1494.1643 1371.2573 1979.2621 1571.4548 2533.1589
## 2021 888.5372plot.ts(SumateraSelataninflowtimeseries)
plot.ts(SumateraSelatanoutflowtimeseries)
SumateraSelatanintimeseriescomponents <- decompose(SumateraSelataninflowtimeseries)
SumateraSelatanintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2012 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2013 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2014 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2015 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2016 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2017 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2018 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2019 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2020 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2021 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## Jul Aug Sep Oct Nov Dec
## 2011 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2012 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2013 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2014 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2015 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2016 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2017 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2018 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2019 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2020 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2021 509.571884 389.746351SumateraSelatanouttimeseriescomponents <- decompose(SumateraSelatanoutflowtimeseries)
SumateraSelatanouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -840.21380 -395.36177 37.92850 -63.06564 481.08395 568.98365
## 2012 -840.21380 -395.36177 37.92850 -63.06564 481.08395 568.98365
## 2013 -840.21380 -395.36177 37.92850 -63.06564 481.08395 568.98365
## 2014 -840.21380 -395.36177 37.92850 -63.06564 481.08395 568.98365
## 2015 -840.21380 -395.36177 37.92850 -63.06564 481.08395 568.98365
## 2016 -840.21380 -395.36177 37.92850 -63.06564 481.08395 568.98365
## 2017 -840.21380 -395.36177 37.92850 -63.06564 481.08395 568.98365
## 2018 -840.21380 -395.36177 37.92850 -63.06564 481.08395 568.98365
## 2019 -840.21380 -395.36177 37.92850 -63.06564 481.08395 568.98365
## 2020 -840.21380 -395.36177 37.92850 -63.06564 481.08395 568.98365
## 2021 -840.21380 -395.36177 37.92850 -63.06564 481.08395 568.98365
## Jul Aug Sep Oct Nov Dec
## 2011 311.95619 40.97787 -448.31460 -248.63179 -75.48302 630.14046
## 2012 311.95619 40.97787 -448.31460 -248.63179 -75.48302 630.14046
## 2013 311.95619 40.97787 -448.31460 -248.63179 -75.48302 630.14046
## 2014 311.95619 40.97787 -448.31460 -248.63179 -75.48302 630.14046
## 2015 311.95619 40.97787 -448.31460 -248.63179 -75.48302 630.14046
## 2016 311.95619 40.97787 -448.31460 -248.63179 -75.48302 630.14046
## 2017 311.95619 40.97787 -448.31460 -248.63179 -75.48302 630.14046
## 2018 311.95619 40.97787 -448.31460 -248.63179 -75.48302 630.14046
## 2019 311.95619 40.97787 -448.31460 -248.63179 -75.48302 630.14046
## 2020 311.95619 40.97787 -448.31460 -248.63179 -75.48302 630.14046
## 2021 311.95619 40.97787plot(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"))