Visualisasi dan Prediksi Data Inflow dan Outflow Uang Kartal pada Daerah Jambi
Nova Rahma Yunida P
2/20/2022
Dosen Pengempu : Prof. Dr. Suhartono, M.Kom
UIN Maulana Malik Ibrahim Malang - Teknik Informatika
Data Beberapa Wilayah di Sumatera
Data Inflow
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2datainflowkelasC <- read_excel(path = "C:/Users/ASUS PC/Documents/BUKU NOVA/RStudio/RMarkdown/kelascup.xlsx")
dataoutflowkelasB <- read_excel(path = "C:/Users/ASUS PC/Documents/BUKU NOVA/RStudio/RMarkdown/kelasout.xlsx")
datainflowkelasC## # 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
dataoutflowkelasB## # 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>Visualisasi dan Prediksi Inflow di Daerah Jambi
datainflowkelasC$Jambi## [1] 1867.621 2138.463 3046.784 5169.097 4978.134 4398.161 4403.638 5656.590
## [9] 6486.166 5628.402 4979.747plot(datainflowkelasC$Jambi, type = "l", col = "red")
Visualisasi dan Prediksi Outflow di Daerah Jambi
dataoutflowkelasB$Jambi## [1] 5216.694 5013.235 6302.359 8361.178 8324.512 7773.966 8433.893 8459.397
## [9] 9204.041 8949.968 6046.364plot(dataoutflowkelasB$Jambi, type = "l", col = "blue")
Visualisasi dan Prediksi Inflow-Outflow di Daerah Jambi Setiap Periode
plot(datainflowkelasC$Jambi, type = "l", col = "red")
lines.default(dataoutflowkelasB$Jambi, col = "blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
Data pada Beberapa Wilayah di Sumatera Setiap Bulan
Data Inflow PerBulan
datainflowperbulan <- read_excel(path = "C:/Users/ASUS PC/Documents/BUKU NOVA/RStudio/RMarkdown/InflowperBulan.xlsx")
dataoutflowperbulan <- read_excel(path = "C:/Users/ASUS PC/Documents/BUKU NOVA/RStudio/RMarkdown/OutflowperBulan.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 PerBulan
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 di Daerah Jambi pada Setiap Bulan
plot(datainflowperbulan$Jambi, type = "l", col = "red")
lines(dataoutflowperbulan$Jambi,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
Time Series dan Log pada Daerah Jambi
Jambitimeseries <- datainflowperbulan$Jambi
plot.ts(Jambitimeseries , type = "l", col = "green")
logJambi <- log(datainflowperbulan$Jambi)
plot.ts(logJambi)
library(TTR)## Warning: package 'TTR' was built under R version 4.1.2JambiSMA3 <- SMA(datainflowperbulan$Jambi ,n=3)
plot.ts(JambiSMA3 )
library("TTR")
JambiSMA3 <- SMA(datainflowperbulan$Jambi ,n=8)
plot.ts(JambiSMA3 )
Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Daerah Jambi
Inflow TimeSeries
Jambiinflowtimeseries <- ts(datainflowperbulan$Jambi, frequency=12, start=c(2011,1))
Jambiinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 48.21238 39.91336 202.77581 76.36759 102.29337 80.38363
## 2012 214.78357 185.06614 118.25569 112.18712 176.73267 131.65442
## 2013 440.25724 250.16557 156.40296 131.70444 80.43460 90.88444
## 2014 648.84622 443.17728 218.60749 372.98546 277.49781 326.07002
## 2015 800.91577 310.67803 334.27000 339.99797 285.21811 266.80514
## 2016 723.86727 399.44327 227.89071 207.32596 294.89205 265.25147
## 2017 436.71704 349.18620 374.44420 291.87853 265.93193 109.35945
## 2018 850.92308 423.79251 432.57396 284.21732 331.44473 943.33760
## 2019 928.32921 508.44605 501.71263 395.87576 375.81227 1377.08370
## 2020 929.25223 453.21208 375.57835 488.00832 366.02264 926.36280
## 2021 1319.31010 533.89020 481.47669 442.30053 954.47189 568.16022
## Jul Aug Sep Oct Nov Dec
## 2011 118.45074 91.88117 618.33464 137.23519 238.83742 112.93547
## 2012 178.67562 446.70847 180.60249 96.89252 190.29249 106.61224
## 2013 150.73569 696.17818 239.01380 381.11280 240.84581 189.04884
## 2014 228.38825 1336.65537 383.31015 366.82210 328.60113 238.13597
## 2015 1033.05014 473.13670 295.54859 329.75416 266.79923 241.96031
## 2016 1069.41796 211.81993 325.26906 251.99989 234.81316 186.17002
## 2017 1008.96424 331.35488 369.25742 288.45059 300.80490 277.28824
## 2018 555.66909 452.09732 390.12811 409.82051 356.98477 225.60052
## 2019 517.64046 582.60662 370.00861 477.26284 302.21112 149.17703
## 2020 418.88012 362.62433 363.94528 290.43227 404.08403 249.99980
## 2021 337.72947 342.40788plot.ts(Jambiinflowtimeseries)
Outflow TimeSeries
Jambioutflowtimeseries <- ts(dataoutflowperbulan$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(Jambioutflowtimeseries)
Visualisasi Prediksi Data Inflow-Outflow Time Series Komponen Uang Kartal di Daerah Jambi
Inflow Time Series Komponen
Jambiintimeseriescomponents <- decompose(Jambiinflowtimeseries)
Jambiintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2012 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2013 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2014 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2015 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2016 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2017 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2018 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2019 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2020 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## 2021 331.44588 -14.12609 -81.49621 -93.86719 -114.25191 104.66208
## Jul Aug Sep Oct Nov Dec
## 2011 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2012 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2013 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2014 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2015 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2016 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2017 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2018 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2019 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2020 156.97755 120.14214 -28.04183 -81.29134 -102.91768 -197.23539
## 2021 156.97755 120.14214Outflow Time Series Komponen
Jambiouttimeseriescomponents <- decompose(Jambioutflowtimeseries)
Jambiouttimeseriescomponents$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 -48.40994Grafik TimeSeries Jambi
plot(Jambiintimeseriescomponents$seasonal,type = "l", col = "red")
lines(Jambiouttimeseriescomponents$seasonal,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
plot(Jambiintimeseriescomponents$trend,type = "l", col = "red")
lines(Jambiouttimeseriescomponents$trend, col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
plot(Jambiintimeseriescomponents$random ,type = "l", col = "red")
lines(Jambiouttimeseriescomponents$random,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
plot(Jambiintimeseriescomponents$figure ,type = "l", col = "red")
lines(Jambiouttimeseriescomponents$figure,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))