Prediksi Data Inflow-Outflow Uang kartal Di Jambi
Muizzul Azizah Oktavianing Putri
2/14/2022
Dosen Pengampu : Prof. Dr. Suhartono, M.Kom
Lembaga : Universitas Islam Negeri Maulana Malik Ibrahim Malang
Jurusan : Teknik Informatika
library(readxl)## Warning: package 'readxl' was built under R version 4.1.2datainflow <- read_excel(path = "inflowTahun.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 Belitung <dbl>library(readxl)
dataoutflow <- read_excel(path = "OutflowTahun.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 Belitung <dbl>datainflow$Jambi## [1] 1867.621 2138.463 3046.784 5169.097 4978.134 4398.161 4403.638 5656.590
## [9] 6486.166 5628.402 4979.747plot(datainflow$Jambi, type = "l", col = "red")
dataoutflow$Bengkulu## [1] 2560.502 2959.332 6489.611 4582.922 4851.534 5162.737 5446.743 5495.251
## [9] 6841.649 6564.020 4680.854plot(dataoutflow$Jambi, type = "l", col = "blue")
plot(datainflow$Jambi, type = "l", col = "red")
lines(dataoutflow$Jambi, type = "l", col = "blue")
library(readxl)
datainflowBulan <- read_excel(path = "inflowBulan.xlsx")
datainflowBulan## # 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>library(readxl)
dataoutflowBulan <- read_excel(path = "OutflowBulan.xlsx")
dataoutflowBulan## # A tibble: 128 x 12
## Bulan 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>datainflowBulan$Jambi## [1] 48.21238 39.91336 202.77581 76.36759 102.29337 80.38363
## [7] 118.45074 91.88117 618.33464 137.23519 238.83742 112.93547
## [13] 214.78357 185.06614 118.25569 112.18712 176.73267 131.65442
## [19] 178.67562 446.70847 180.60249 96.89252 190.29249 106.61224
## [25] 440.25724 250.16557 156.40296 131.70444 80.43460 90.88444
## [31] 150.73569 696.17818 239.01380 381.11280 240.84581 189.04884
## [37] 648.84622 443.17728 218.60749 372.98546 277.49781 326.07002
## [43] 228.38825 1336.65537 383.31015 366.82210 328.60113 238.13597
## [49] 800.91577 310.67803 334.27000 339.99797 285.21811 266.80514
## [55] 1033.05014 473.13670 295.54859 329.75416 266.79923 241.96031
## [61] 723.86727 399.44327 227.89071 207.32596 294.89205 265.25147
## [67] 1069.41796 211.81993 325.26906 251.99989 234.81316 186.17002
## [73] 436.71704 349.18620 374.44420 291.87853 265.93193 109.35945
## [79] 1008.96424 331.35488 369.25742 288.45059 300.80490 277.28824
## [85] 850.92308 423.79251 432.57396 284.21732 331.44473 943.33760
## [91] 555.66909 452.09732 390.12811 409.82051 356.98477 225.60052
## [97] 928.32921 508.44605 501.71263 395.87576 375.81227 1377.08370
## [103] 517.64046 582.60662 370.00861 477.26284 302.21112 149.17703
## [109] 929.25223 453.21208 375.57835 488.00832 366.02264 926.36280
## [115] 418.88012 362.62433 363.94528 290.43227 404.08403 249.99980
## [121] 1319.31010 533.89020 481.47669 442.30053 954.47189 568.16022
## [127] 337.72947 342.40788datainflowBulan <- datainflowBulan$`Jambi`
plot.ts(datainflowBulan , type = "l", col = "red")
dataoutflowBulan$Jambi## [1] 297.46348 280.08970 341.37188 474.26014 371.36905 540.43609
## [7] 428.10203 1056.05643 92.78528 295.39728 272.21261 767.15036
## [13] 133.61579 321.29557 315.41057 373.26078 441.58952 474.63459
## [19] 330.20592 835.74847 221.85612 472.49384 299.07579 794.04754
## [25] 110.31731 184.50535 223.54744 235.42017 450.54670 349.51626
## [31] 839.48154 339.88048 732.69193 819.24007 782.02490 1235.18658
## [37] 351.35683 459.63127 637.62828 526.41165 683.34064 651.89272
## [43] 1929.38736 274.46904 553.86575 703.65271 588.68032 1000.86095
## [49] 249.99472 486.10988 549.06994 721.86428 701.16932 931.14718
## [55] 1582.71912 395.76377 549.45261 479.75684 631.21748 1046.24662
## [61] 229.69662 442.46621 487.32817 572.51965 587.13872 1610.89703
## [67] 456.38157 430.25770 842.64910 521.69293 648.58138 944.35648
## [73] 394.17886 553.63581 500.03923 530.31764 570.86673 1961.91565
## [79] 212.49734 680.41258 470.55865 568.53590 820.95090 1169.98413
## [85] 275.03184 451.87980 498.71186 687.34280 1222.83919 1579.32715
## [91] 391.43773 555.29629 475.32140 545.11918 735.03562 1042.05433
## [97] 218.20233 534.52562 559.51510 895.65817 2018.12386 147.10847
## [103] 717.81375 656.73797 617.28665 719.15618 727.75492 1392.15834
## [109] 230.43948 421.99569 606.04929 713.68012 1262.75583 143.79548
## [115] 633.64958 610.36918 689.06184 1124.09728 807.10093 1706.97368
## [121] 54.41456 487.87292 732.48101 1261.14201 1578.66374 642.31328
## [127] 664.55917 624.91746dataoutflowBulan <- dataoutflowBulan$`Jambi`
plot.ts(dataoutflowBulan , type = "l", col = "blue")
plot(datainflow$`Jambi`, type = "l", col = "red")
lines(dataoutflow$`Jambi`, col="green")
legend("top",c("Inflow","Outflow"),fill=c("red","green"))