Visualisasi dan Prediksi Data Inflow dan Outflow Uang kartal di Sumatera Selatan Menggunakan RStudio
Nurul Izzah, Prof. Dr. Suhartono, M.Kom
2 Maret 2022
Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang
Jurusan : Teknik Informatika
Data Inflow dan Outflow Wilayah di Pulau Sumatera
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2Data Inflow
datainflow <- read_excel(path = "C:/Users/User/Documents/SEMESTER 2/LINEAR ALGEBRA/inflowsumatera.xlsx")
datainflow## # A tibble: 11 x 12
## Keterangan Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 57900. 2308. 23238. 9385. 3012.
## 2 2012 65911. 2620. 25981. 11192. 4447.
## 3 2013 98369. 36337. 18120. 14056. 8933.
## 4 2014 86024. 4567. 30503. 14103. 6358.
## 5 2015 86549. 4710. 30254. 13309. 7156.
## 6 2016 97764. 5775. 34427. 14078. 8211.
## 7 2017 103748. 5514. 35617. 15312. 8553.
## 8 2018 117495. 5799. 41769. 15058. 10730.
## 9 2019 133762. 7509. 47112. 14750. 10915.
## 10 2020 109345. 6641. 36609. 10696. 9148.
## 11 2021 89270. 3702. 31840. 10748. 7769.
## # ... with 6 more variables: Kep. Riau <dbl>, Jambi <dbl>,
## # Sumatera Selatan <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # Kep. Bangka Belitung <dbl>Data Outflow
dataoutflow <- read_excel(path = "C:/Users/User/Documents/SEMESTER 2/LINEAR ALGEBRA/outflowsumatera.xlsx")
dataoutflow## # A tibble: 11 x 12
## Keterangan Sumatera Aceh `Sumatera Utara` `Sumatera Barat` Riau
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 80092. 6338. 22176. 5300. 12434.
## 2 2012 85235. 6378. 22495. 6434. 13014.
## 3 2013 103288. 23278. 19235. 6511. 15460.
## 4 2014 102338. 8630. 26391. 7060. 15158.
## 5 2015 109186. 9637. 27877. 7471. 15789.
## 6 2016 121992. 11311. 31959. 9198. 17645.
## 7 2017 133606. 11760. 35243. 10754. 18128.
## 8 2018 135676. 11450. 36908. 8447. 17926.
## 9 2019 153484. 13087. 44051. 9465. 19277.
## 10 2020 140589. 12874. 39758. 8763. 19139.
## 11 2021 86627. 5770. 23453. 5941. 12631.
## # ... with 6 more variables: Kep. Riau <dbl>, Jambi <dbl>,
## # Sumatera Selatan <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## # Kep. Bangka Belitung <dbl>1. Visualisasi Prediksi Data Inflow Uang Kartal Sumatera Selatan pada Setiap Periode
datainsumatera <- datainflow$`Sumatera Selatan`
plot(datainsumatera,type = "l", col= "blue")
2. Visualisasi Prediksi Data Outflow Uang Kartal Sumatera Selatan pada Setiap Periode
dataoutsuamtera <- dataoutflow$`Sumatera Selatan`
plot(dataoutsuamtera, type = "l", col = "red")
3. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sumatera Selatan Setiap Periode
plot(datainflow$`Sumatera Selatan`, type = "l", col = "blue")
lines(dataoutflow$`Sumatera Selatan`, type = "l", col = "red")
legend("top",c("Inflow","Outflow"),fill=c("blue","red"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sumatera Selatan Setiap Bulan
Data Inflow Setiap Bulan
datainflowperbulan <- read_excel(path = "C:/Users/User/Documents/SEMESTER 2/LINEAR ALGEBRA/inflowsumateraperbulan.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>Data Outflow Setiap Bulan
dataoutflowperbulan <- read_excel(path = "C:/Users/User/Documents/SEMESTER 2/LINEAR ALGEBRA/outflowsumateraperbulan.xlsx")
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>Visualisasi dan Prediksi Inflow-Outflow
plot(datainflowperbulan$`Sumatera Selatan`, type = "l", col = "purple")
lines(dataoutflowperbulan$`Sumatera Selatan`,col="pink")
legend("top",c("Inflow","Outflow"),fill=c("purple","pink"))
5. Time Series dan Log di Sumatera Selatan
sumsetimeseries <- datainflowperbulan$`Sumatera Selatan`
plot.ts(sumsetimeseries , type = "l", col = "maroon")
logsumse <- log(datainflowperbulan$`Sumatera Selatan`)
plot.ts(logsumse)
6. Visualisasi dan Prediksi Data Inflow-Outflow Time Series Uang Kartal di Sumatera Selatan
Data Inflow
sumseinflowtimeseries <- ts(datainflowperbulan$`Sumatera Selatan`, frequency=12, start=c(2011,1))
sumseinflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 456.4919 363.4010 661.9322 355.8931 655.5655 445.7856 533.9543
## 2012 847.7118 557.6804 842.2304 433.0244 852.8930 791.5389 572.3589
## 2013 1241.6259 583.4391 369.7243 594.3824 606.8414 451.6471 436.7965
## 2014 1290.5744 697.7210 539.5584 601.5915 496.8451 761.1483 292.8094
## 2015 1373.5722 467.9884 507.1713 316.2315 646.5083 769.0185 2218.0190
## 2016 1724.9435 757.5883 698.3839 580.2943 975.7450 820.2649 3028.2880
## 2017 1247.4278 649.6430 715.8801 816.9179 919.5968 497.3588 3480.9746
## 2018 1386.1845 640.7069 770.9428 855.5950 824.7960 2888.6614 1826.9664
## 2019 1729.8884 787.8768 710.2534 1126.8944 967.7749 3393.1168 1151.6163
## 2020 2054.0128 914.8161 680.3686 665.8459 1019.4927 1432.5599 1025.4901
## 2021 1825.6471 768.0047 782.0909 1035.3983 1947.4074 1072.3469 963.1947
## Aug Sep Oct Nov Dec
## 2011 744.7032 1712.5480 561.2395 979.5828 349.2438
## 2012 1688.2790 825.1074 638.0242 767.6416 309.4864
## 2013 2094.8131 390.1139 941.0992 631.0574 305.7795
## 2014 2458.0650 693.6043 957.7864 685.5728 562.6037
## 2015 1058.1097 827.1355 979.8054 933.4509 700.1903
## 2016 969.6394 966.3684 828.4733 743.3749 658.3444
## 2017 1085.7413 1042.5878 1027.7669 931.9672 659.4133
## 2018 1187.7423 1109.3445 995.0373 993.8724 786.6507
## 2019 1222.7634 1014.0402 1049.8722 925.4907 732.1411
## 2020 933.0685 922.3794 619.9658 929.7524 558.5297
## 2021 711.8520plot.ts(sumseinflowtimeseries)
Data Outflow
sumseoutflowtimeseries <- ts(dataoutflowperbulan$`Sumatera Selatan`, frequency=12, start=c(2011,1))
sumseoutflowtimeseries## 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(sumseoutflowtimeseries)
7. Visualisasi dan Prediksi Data Inflow-Outflow Time Series Komponen Uang Kartal di Sumatera Selatan
sumseintimeseriescomponents <- decompose(sumseinflowtimeseries)
sumseintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2012 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2013 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2014 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2015 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2016 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2017 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2018 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2019 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2020 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## 2021 491.487816 -299.776183 -327.111996 -306.363005 -159.769086 338.917294
## Jul Aug Sep Oct Nov Dec
## 2011 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2012 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2013 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2014 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2015 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2016 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2017 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2018 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2019 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2020 509.571884 389.746351 -6.409713 -100.157546 -116.102210 -414.033605
## 2021 509.571884 389.746351sumseouttimeseriescomponents <- decompose(sumseoutflowtimeseries)
sumseouttimeseriescomponents$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(sumseintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(sumseouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(sumseintimeseriescomponents$trend,type = "l", col = "orange")
lines(sumseouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(sumseintimeseriescomponents$random ,type = "l", col = "orange")
lines(sumseouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(sumseintimeseriescomponents$figure ,type = "l", col = "orange")
lines(sumseouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
Sumber :
https://www.bi.go.id/id/statistik/ekonomi-keuangan/ssp/indikator-pengedaran-uang.aspx
https://rpubs.com/suhartono-uinmaliki/861286
summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00