Visualisasi dan Prediksi Data Inflow dan Outflow Uang Kartal pada Daerah Kepulauan Bangka Belitung
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
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2Data Inflow
datainflowkelasC <- read_excel(path = "C:/Users/ASUS PC/Documents/BUKU NOVA/RStudio/RMarkdown/kelascup.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 <- read_excel(path = "C:/Users/ASUS PC/Documents/BUKU NOVA/RStudio/RMarkdown/kelasout.xlsx")
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 Kep.Bangka Belitung
datainflowkelasC$`Kep. Bangka Belitung`## [1] 0.00000 0.00000 0.00000 13.70908 1176.58339 1544.20832
## [7] 1163.50693 1517.42196 3265.10361 2562.09436 1259.21009plot(datainflowkelasC$`Kep. Bangka Belitung`, type = "l", col = "red")
Visualisasi dan Prediksi Outflow di Daerah Kep.Bangka Belitung
dataoutflowkelasB$`Kep. Bangka Belitung`## [1] 0.0000 0.0000 0.0000 322.0841 2004.7861 2683.7941 2750.0952
## [8] 2737.6852 4167.0982 3898.5512 3492.5938plot(dataoutflowkelasB$`Kep. Bangka Belitung`, type = "l", col = "blue")
Visualisasi dan Prediksi Inflow-Outflow di Daerah Kep.Bangka Belitung Setiap Periode
plot(datainflowkelasC$`Kep. Bangka Belitung`, type = "l", col = "red")
lines(dataoutflowkelasB$`Kep. Bangka Belitung`, type = "l", 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 Kep.Bangka Belitung pada Setiap Bulan
plot(datainflowperbulan$`Kep. Bangka Belitung`, type = "l", col = "red")
lines(dataoutflowperbulan$`Kep. Bangka Belitung`,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
Time Series dan Log pada daerah Kep.Bangka Belitung
Kep.BangkaBelitungtimeseries <- datainflowperbulan$`Kep. Bangka Belitung`
plot.ts(Kep.BangkaBelitungtimeseries , type = "l", col = "green")
logKep.BangkaBelitung <- log(datainflowperbulan$`Kep. Bangka Belitung`)
plot.ts(logKep.BangkaBelitung)
library(TTR)## Warning: package 'TTR' was built under R version 4.1.2SumateraBaratSMA3 <- SMA(datainflowperbulan$`Sumatera Barat` ,n=3)
plot.ts(SumateraBaratSMA3 )
library("TTR")
Kep.BangkaBelitungSMA3 <- SMA(datainflowperbulan$`Kep. Bangka Belitung` ,n=8)
plot.ts(Kep.BangkaBelitungSMA3 )
Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Daerah Kep.Bangka Belitung
Inflow TimeSeries
Kep.BangkaBelitunginflowtimeseries <- ts(datainflowperbulan$`Kep. Bangka Belitung`, frequency=12, start=c(2011,1))
Kep.BangkaBelitunginflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2012 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2013 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2014 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2015 187.639957 33.147896 114.451087 78.250445 30.909939 92.942037
## 2016 340.792357 166.304876 85.640498 49.255930 114.320505 75.598373
## 2017 201.271938 110.302500 58.276958 28.704962 81.210205 41.263221
## 2018 306.662249 54.452683 55.611933 69.022908 54.486720 283.237018
## 2019 309.413941 301.696986 213.049668 247.041834 190.930447 725.701711
## 2020 458.198210 275.503586 153.561979 238.759146 253.398617 471.739983
## 2021 381.984491 92.951196 93.798544 91.820055 362.027685 84.895245
## Jul Aug Sep Oct Nov Dec
## 2011 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2012 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2013 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2014 0.000000 0.000000 0.000000 0.000000 8.583950 5.125127
## 2015 291.012814 36.621340 68.667292 128.247156 99.552693 15.140736
## 2016 449.106283 28.370724 133.889548 41.285099 25.777118 33.867005
## 2017 358.088421 58.774292 93.159671 41.415730 81.863243 9.175790
## 2018 192.789235 125.814197 83.299065 149.917111 76.593131 65.535710
## 2019 268.101638 294.219489 238.370988 181.142442 176.158614 119.275850
## 2020 179.706885 151.857426 129.829668 78.321303 126.068162 45.149398
## 2021 68.830158 82.902713plot.ts(Kep.BangkaBelitunginflowtimeseries)
Outflow TimeSeries
Kep.BangkaBelitungoutflowtimeseries <- ts(dataoutflowperbulan$`Kep. Bangka Belitung`, frequency=12, start=c(2011,1))
Kep.BangkaBelitungoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2012 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2013 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2014 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2015 7.212591 100.722316 88.242902 119.734077 108.562288 141.922856
## 2016 62.511019 74.673600 73.366989 137.419284 187.254674 593.621546
## 2017 99.053103 107.190457 154.892432 219.087363 237.040453 685.029330
## 2018 45.294821 141.372517 163.027297 210.937666 447.089850 545.359260
## 2019 213.935303 151.205232 311.646991 381.732526 1149.153997 78.513581
## 2020 125.859564 120.611445 347.011532 292.171852 672.792344 28.320067
## 2021 38.406031 155.960921 473.816286 677.957705 731.426761 423.292681
## Jul Aug Sep Oct Nov Dec
## 2011 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2012 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2013 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## 2014 0.000000 0.000000 0.000000 0.000000 20.158561 301.925528
## 2015 461.459620 247.654498 145.109007 62.885310 180.503866 340.776774
## 2016 209.427034 276.657814 271.424057 153.904316 279.301232 364.232562
## 2017 31.018723 219.679759 53.315563 209.534771 226.055188 508.198084
## 2018 168.913022 197.022225 115.023134 225.510779 245.110586 233.024001
## 2019 354.451123 295.839540 171.788886 243.712926 269.138995 545.979144
## 2020 320.609406 136.901740 297.384179 471.479842 258.876760 826.532469
## 2021 494.593107 497.140310plot.ts(Kep.BangkaBelitungoutflowtimeseries)
Visualisasi Prediksi Data Inflow-Outflow Time Series Komponen Uang Kartal di Daerah Kep.Bangka Belitung
Inflow Time Series Komponen
Kep.BangkaBelitungintimeseriescomponents <- decompose(Kep.BangkaBelitunginflowtimeseries)
Kep.BangkaBelitungintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 115.0550050 -0.7375573 -25.9024253 -23.4835202 -22.8496336 83.6050158
## 2012 115.0550050 -0.7375573 -25.9024253 -23.4835202 -22.8496336 83.6050158
## 2013 115.0550050 -0.7375573 -25.9024253 -23.4835202 -22.8496336 83.6050158
## 2014 115.0550050 -0.7375573 -25.9024253 -23.4835202 -22.8496336 83.6050158
## 2015 115.0550050 -0.7375573 -25.9024253 -23.4835202 -22.8496336 83.6050158
## 2016 115.0550050 -0.7375573 -25.9024253 -23.4835202 -22.8496336 83.6050158
## 2017 115.0550050 -0.7375573 -25.9024253 -23.4835202 -22.8496336 83.6050158
## 2018 115.0550050 -0.7375573 -25.9024253 -23.4835202 -22.8496336 83.6050158
## 2019 115.0550050 -0.7375573 -25.9024253 -23.4835202 -22.8496336 83.6050158
## 2020 115.0550050 -0.7375573 -25.9024253 -23.4835202 -22.8496336 83.6050158
## 2021 115.0550050 -0.7375573 -25.9024253 -23.4835202 -22.8496336 83.6050158
## Jul Aug Sep Oct Nov Dec
## 2011 78.2633854 -28.0302942 -23.6525417 -37.1146917 -41.5789170 -73.5738251
## 2012 78.2633854 -28.0302942 -23.6525417 -37.1146917 -41.5789170 -73.5738251
## 2013 78.2633854 -28.0302942 -23.6525417 -37.1146917 -41.5789170 -73.5738251
## 2014 78.2633854 -28.0302942 -23.6525417 -37.1146917 -41.5789170 -73.5738251
## 2015 78.2633854 -28.0302942 -23.6525417 -37.1146917 -41.5789170 -73.5738251
## 2016 78.2633854 -28.0302942 -23.6525417 -37.1146917 -41.5789170 -73.5738251
## 2017 78.2633854 -28.0302942 -23.6525417 -37.1146917 -41.5789170 -73.5738251
## 2018 78.2633854 -28.0302942 -23.6525417 -37.1146917 -41.5789170 -73.5738251
## 2019 78.2633854 -28.0302942 -23.6525417 -37.1146917 -41.5789170 -73.5738251
## 2020 78.2633854 -28.0302942 -23.6525417 -37.1146917 -41.5789170 -73.5738251
## 2021 78.2633854 -28.0302942Outflow Time Series Komponen
Kep.BangkaBelitungouttimeseriescomponents <- decompose(Kep.BangkaBelitungoutflowtimeseries)
Kep.BangkaBelitungouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -116.451749 -94.637566 -27.708893 -6.502379 150.206433 64.167283
## 2012 -116.451749 -94.637566 -27.708893 -6.502379 150.206433 64.167283
## 2013 -116.451749 -94.637566 -27.708893 -6.502379 150.206433 64.167283
## 2014 -116.451749 -94.637566 -27.708893 -6.502379 150.206433 64.167283
## 2015 -116.451749 -94.637566 -27.708893 -6.502379 150.206433 64.167283
## 2016 -116.451749 -94.637566 -27.708893 -6.502379 150.206433 64.167283
## 2017 -116.451749 -94.637566 -27.708893 -6.502379 150.206433 64.167283
## 2018 -116.451749 -94.637566 -27.708893 -6.502379 150.206433 64.167283
## 2019 -116.451749 -94.637566 -27.708893 -6.502379 150.206433 64.167283
## 2020 -116.451749 -94.637566 -27.708893 -6.502379 150.206433 64.167283
## 2021 -116.451749 -94.637566 -27.708893 -6.502379 150.206433 64.167283
## Jul Aug Sep Oct Nov Dec
## 2011 1.650183 -16.372014 -50.967161 -24.467907 -19.128618 140.212388
## 2012 1.650183 -16.372014 -50.967161 -24.467907 -19.128618 140.212388
## 2013 1.650183 -16.372014 -50.967161 -24.467907 -19.128618 140.212388
## 2014 1.650183 -16.372014 -50.967161 -24.467907 -19.128618 140.212388
## 2015 1.650183 -16.372014 -50.967161 -24.467907 -19.128618 140.212388
## 2016 1.650183 -16.372014 -50.967161 -24.467907 -19.128618 140.212388
## 2017 1.650183 -16.372014 -50.967161 -24.467907 -19.128618 140.212388
## 2018 1.650183 -16.372014 -50.967161 -24.467907 -19.128618 140.212388
## 2019 1.650183 -16.372014 -50.967161 -24.467907 -19.128618 140.212388
## 2020 1.650183 -16.372014 -50.967161 -24.467907 -19.128618 140.212388
## 2021 1.650183 -16.372014Grafik TimeSeries Kep.Bangka Belitung
plot(Kep.BangkaBelitungintimeseriescomponents$seasonal,type = "l", col = "red")
lines(Kep.BangkaBelitungouttimeseriescomponents$seasonal,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
plot(Kep.BangkaBelitungintimeseriescomponents$trend,type = "l", col = "red")
lines(Kep.BangkaBelitungouttimeseriescomponents$trend,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
plot(Kep.BangkaBelitungintimeseriescomponents$random ,type = "l", col = "red")
lines(Kep.BangkaBelitungouttimeseriescomponents$random,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))
plot(Kep.BangkaBelitungintimeseriescomponents$figure ,type = "l", col = "red")
lines(Kep.BangkaBelitungouttimeseriescomponents$figure,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("red","blue"))