Visualisasi Data Inflow dan Outflow Di Nusa Tenggara Barat
Rafi Ronalda Purnama Hakim,Prof.Dr.Suhartono,M.KOM
2/20/2022
Universitas : UIN MAULANA MALIK IBRAHIM MALANG
Jurusan : Teknik Informatika
Pengertian Inflow dan Outflow pada Uang Kartal
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. Ini juga mencakup transfer aset dan kewajiban antara sesama dan non-residen perusahaan, jika orang tua pengendali utama adalah penduduk. Investasi langsung keluar juga disebut investasi langsung di luar negeri.
library(readxl)datainflow <- read_excel(path = "InflowTahun.xlsx")## New names:
## * `` -> ...2datainflow## # A tibble: 12 x 12
## Keterangan ...2 `Bali Nusra` Bali `Nusa Tenggara Barat` `Nusa Tenggara T~`
## <dbl> <lgl> <dbl> <dbl> <dbl> <dbl>
## 1 NA NA NA NA NA NA
## 2 2011 NA 10322. 6394. 1803. 2125.
## 3 2012 NA 14613. 8202. 3676. 2735.
## 4 2013 NA 17512. 5066. 7024. 5422.
## 5 2014 NA 20807. 11590. 5704. 3512.
## 6 2015 NA 23008. 13072. 6285. 3651.
## 7 2016 NA 30965. 17914. 8842. 4210.
## 8 2017 NA 30797. 16962. 8383. 5452.
## 9 2018 NA 33866. 18610. 9140. 6116.
## 10 2019 NA 38116. 21422. 9614. 7080.
## 11 2020 NA 29400. 14735. 8007. 6657.
## 12 2021 NA 18892. 7505. 5888. 5498.
## # ... with 6 more variables: Kalimantan <dbl>, `Kalimantan Barat` <dbl>,
## # `Kalimantan Tengah` <dbl>, `Kalimantan Selatan` <dbl>,
## # `Kalimantan Timur` <dbl>, `Kalimantan Utara` <dbl>library (readxl)dataoutflow <- read_excel(path = "OutflowTahun.xlsx")
dataoutflow## # A tibble: 11 x 10
## Tahun Bali `Nusa Tenggara Ba~` `Nusa Tenggara~` Kalimantan `Kalimantan Ba~`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 8912. 3819. 3693. 29535. 5221.
## 2 2012 10782. 4379. 4260. 33444. 5698.
## 3 2013 7248. 10628. 11524. 44929. 6011.
## 4 2014 13104. 5620. 4668. 38772. 6764.
## 5 2015 14471. 6728. 5530. 41945. 8486.
## 6 2016 18140. 8149. 5652. 42179. 9402.
## 7 2017 17822. 8770. 7569. 50404. 11132.
## 8 2018 20434. 9271. 7555. 53989. 12278.
## 9 2019 20654. 10288. 7738. 57579. 13768.
## 10 2020 14323. 8546. 8356. 52060. 13501.
## 11 2021 6531. 5222. 3472. 30291. 6958.
## # ... with 4 more variables: `Kalimantan Tengah` <dbl>,
## # `Kalimantan Selatan` <dbl>, `Kalimantan Timur` <dbl>,
## # `Kalimantan Utara` <dbl>dataoutflow## # A tibble: 11 x 10
## Tahun Bali `Nusa Tenggara Ba~` `Nusa Tenggara~` Kalimantan `Kalimantan Ba~`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 8912. 3819. 3693. 29535. 5221.
## 2 2012 10782. 4379. 4260. 33444. 5698.
## 3 2013 7248. 10628. 11524. 44929. 6011.
## 4 2014 13104. 5620. 4668. 38772. 6764.
## 5 2015 14471. 6728. 5530. 41945. 8486.
## 6 2016 18140. 8149. 5652. 42179. 9402.
## 7 2017 17822. 8770. 7569. 50404. 11132.
## 8 2018 20434. 9271. 7555. 53989. 12278.
## 9 2019 20654. 10288. 7738. 57579. 13768.
## 10 2020 14323. 8546. 8356. 52060. 13501.
## 11 2021 6531. 5222. 3472. 30291. 6958.
## # ... with 4 more variables: `Kalimantan Tengah` <dbl>,
## # `Kalimantan Selatan` <dbl>, `Kalimantan Timur` <dbl>,
## # `Kalimantan Utara` <dbl>1. Visualisasi Prediksi Data Inflow Uang Kartal Nusa Tenggara Barat setiap periode
plot(datainflow$Keterangan,datainflow$`Nusa Tenggara Barat`,type = "l", col= "steelblue")
2. Visualisasi Prediksi Data outflow Uang Kartal Nusa Tenggara Barat setiap periode
plot(dataoutflow$Tahun,dataoutflow$`Nusa Tenggara Barat`,type = "l", col= "red")
3. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Nusa Tenggara Barat Setiap Periode
plot(datainflow$Keterangan,datainflow$`Nusa Tenggara Barat`,type = "l", col= "steelblue")
lines(dataoutflow$Tahun,dataoutflow$`Nusa Tenggara Barat`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("green","steelblue"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Nusa Tenggara Barat Setiap Bulan
library(readxl)datainflowperbulan <- read_excel(path = "InflowBulan.xlsx")## New names:
## * `` -> ...2datainflowperbulan## # A tibble: 128 x 12
## Bulan ...2 `Bali Nusra` Bali `Nusa Tenggara Barat`
## <dttm> <lgl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 NA 912. 463. 93.8
## 2 2011-02-01 00:00:00 NA 591. 401. 82.1
## 3 2011-03-01 00:00:00 NA 869. 532. 125.
## 4 2011-04-01 00:00:00 NA 709. 431. 124.
## 5 2011-05-01 00:00:00 NA 754. 474. 113.
## 6 2011-06-01 00:00:00 NA 633. 393. 105.
## 7 2011-07-01 00:00:00 NA 856. 585. 137.
## 8 2011-08-01 00:00:00 NA 607. 328. 136.
## 9 2011-09-01 00:00:00 NA 1965. 1434. 292.
## 10 2011-10-01 00:00:00 NA 874. 522. 184.
## # ... with 118 more rows, and 7 more variables: `Nusa Tenggara Timur` <dbl>,
## # Kalimantan <dbl>, `Kalimantan Barat` <dbl>, `Kalimantan Tengah` <dbl>,
## # `Kalimantan Selatan` <dbl>, `Kalimantan Timur` <dbl>,
## # `Kalimantan Utara` <dbl>dataoutflowperbulan <- read_excel(path = "OutflowBulan.xlsx")
datainflowperbulan## # A tibble: 128 x 12
## Bulan ...2 `Bali Nusra` Bali `Nusa Tenggara Barat`
## <dttm> <lgl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 NA 912. 463. 93.8
## 2 2011-02-01 00:00:00 NA 591. 401. 82.1
## 3 2011-03-01 00:00:00 NA 869. 532. 125.
## 4 2011-04-01 00:00:00 NA 709. 431. 124.
## 5 2011-05-01 00:00:00 NA 754. 474. 113.
## 6 2011-06-01 00:00:00 NA 633. 393. 105.
## 7 2011-07-01 00:00:00 NA 856. 585. 137.
## 8 2011-08-01 00:00:00 NA 607. 328. 136.
## 9 2011-09-01 00:00:00 NA 1965. 1434. 292.
## 10 2011-10-01 00:00:00 NA 874. 522. 184.
## # ... with 118 more rows, and 7 more variables: `Nusa Tenggara Timur` <dbl>,
## # Kalimantan <dbl>, `Kalimantan Barat` <dbl>, `Kalimantan Tengah` <dbl>,
## # `Kalimantan Selatan` <dbl>, `Kalimantan Timur` <dbl>,
## # `Kalimantan Utara` <dbl>dataoutflowperbulan## # A tibble: 128 x 11
## Bulan `Bali Nusra` Bali `Nusa Tenggara Barat` `Nusa Tenggara~`
## <dttm> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 423. 177. 194. 51.9
## 2 2011-02-01 00:00:00 482. 353. 40.9 87.6
## 3 2011-03-01 00:00:00 989. 581. 273. 136.
## 4 2011-04-01 00:00:00 1207. 662. 343. 202.
## 5 2011-05-01 00:00:00 1168. 652. 279. 237.
## 6 2011-06-01 00:00:00 1476. 852. 351. 273.
## 7 2011-07-01 00:00:00 1536. 746. 319. 471.
## 8 2011-08-01 00:00:00 3084. 1888. 796. 400.
## 9 2011-09-01 00:00:00 926. 458. 293. 175.
## 10 2011-10-01 00:00:00 1321. 609. 399. 313.
## # ... with 118 more rows, and 6 more variables: Kalimantan <dbl>,
## # `Kalimantan Barat` <dbl>, `Kalimantan Tengah` <dbl>,
## # `Kalimantan Selatan` <dbl>, `Kalimantan Timur` <dbl>,
## # `Kalimantan Utara` <dbl>plot(datainflowperbulan$`Nusa Tenggara Barat`, type = "l", col = "steelblue")
lines(dataoutflowperbulan$`Nusa Tenggara Barat`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
NusaTenggaraBarattimeseries <- datainflowperbulan$`Nusa Tenggara Barat`
plot.ts(NusaTenggaraBarattimeseries , type = "l", col = "steelblue")
logNusaTenggaraBarat <- log(datainflowperbulan$`Nusa Tenggara Barat`)
plot.ts(logNusaTenggaraBarat)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Nusa Tenggara Barat
NusaTenggaraBaratinflowtimeseries <- ts(datainflowperbulan$`Nusa Tenggara Barat`, frequency=12, start=c(2011,1))
NusaTenggaraBaratinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 93.78277 82.11118 125.36647 124.05719 113.43034 105.25732
## 2012 550.63456 411.79246 244.91073 156.88303 263.80686 229.52634
## 2013 1113.47969 490.64519 357.22831 524.96469 378.32824 508.82852
## 2014 808.77394 531.51918 420.48442 354.48788 240.38495 414.09391
## 2015 962.09142 573.02164 450.75598 276.87117 375.99704 464.42822
## 2016 1123.09366 754.94601 676.78011 423.79516 486.91743 649.09273
## 2017 1108.70834 809.60080 644.85628 565.73828 617.42817 336.05230
## 2018 1351.53986 821.34930 444.08895 494.15633 532.57910 1549.84028
## 2019 1514.34160 804.55588 601.67056 549.64819 385.70710 1622.40191
## 2020 1840.24133 929.05494 598.86543 423.56340 394.49640 692.23920
## 2021 1609.07920 738.30793 543.76915 325.09127 881.09583 645.78836
## Jul Aug Sep Oct Nov Dec
## 2011 136.73890 136.40946 291.55112 183.60560 220.86671 189.44302
## 2012 389.53902 473.19342 215.60404 233.79147 347.46184 158.55025
## 2013 479.12111 1935.84521 263.64276 381.05598 370.11745 220.57360
## 2014 326.42429 986.00397 365.68409 437.38306 391.70022 427.42686
## 2015 1021.47948 599.02950 307.26066 462.71723 508.13725 283.23353
## 2016 1477.17878 742.81404 550.50921 574.05571 672.28981 710.26876
## 2017 1685.20463 622.68154 393.99464 558.70249 591.17135 449.34352
## 2018 1141.90311 826.88137 395.66821 530.38019 715.52211 336.12099
## 2019 805.56379 748.76408 608.81277 712.49160 750.21398 510.10587
## 2020 721.18810 567.66903 635.64928 317.05116 625.17567 261.94790
## 2021 452.45260 692.84977NusaTenggaraBaratoutflowtimeseries <- ts(dataoutflowperbulan$`Nusa Tenggara Barat`, frequency=12, start=c(2011,1))
NusaTenggaraBaratoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 193.55380 40.88791 272.56833 343.42689 279.02851 350.64136
## 2012 274.80607 138.55052 270.81908 454.44237 290.99300 512.02921
## 2013 498.78734 820.48116 1020.66984 693.31859 1081.19483 876.62028
## 2014 215.66256 259.41935 358.25391 513.89463 601.15931 486.22217
## 2015 300.40186 270.34695 346.92749 783.92348 553.87002 620.72835
## 2016 378.55925 204.05382 458.25515 723.65922 805.05191 1781.28338
## 2017 252.45080 337.24735 710.17852 808.51428 1109.96304 1998.07503
## 2018 206.83369 398.28054 812.67400 898.95897 1370.61853 1794.47538
## 2019 121.33645 523.76697 710.95053 1467.19223 2777.37767 234.92824
## 2020 291.35553 303.87264 631.37940 931.78677 1720.36178 532.73791
## 2021 92.33143 168.91659 512.21525 1778.40357 1560.96801 323.56386
## Jul Aug Sep Oct Nov Dec
## 2011 319.46650 795.68413 292.95300 399.00086 228.58627 302.92930
## 2012 284.94519 639.09306 352.80700 448.71892 346.30646 365.62585
## 2013 2363.60890 1335.85715 503.83641 494.21692 347.26947 591.72633
## 2014 940.35037 340.44524 518.43573 557.55377 447.41898 380.69617
## 2015 1270.05493 366.60595 618.53536 468.11892 510.33991 618.05357
## 2016 542.60450 483.29275 767.97220 639.89517 673.35602 691.18240
## 2017 253.63960 837.52854 663.29932 398.54667 642.18566 758.30636
## 2018 363.05172 472.68561 701.58368 620.38579 718.15401 913.58197
## 2019 511.45829 574.14392 656.62323 815.34692 612.29106 1282.30868
## 2020 486.22795 668.97329 581.70800 1251.01957 400.11484 746.25085
## 2021 498.85536 286.36848plot.ts(NusaTenggaraBaratinflowtimeseries)
plot.ts(NusaTenggaraBaratoutflowtimeseries)
NusaTenggaraBaratintimeseriescomponents <- decompose(NusaTenggaraBaratinflowtimeseries)
NusaTenggaraBaratintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 591.59992 76.24692 -117.30139 -193.90475 -206.89023 100.99765
## 2012 591.59992 76.24692 -117.30139 -193.90475 -206.89023 100.99765
## 2013 591.59992 76.24692 -117.30139 -193.90475 -206.89023 100.99765
## 2014 591.59992 76.24692 -117.30139 -193.90475 -206.89023 100.99765
## 2015 591.59992 76.24692 -117.30139 -193.90475 -206.89023 100.99765
## 2016 591.59992 76.24692 -117.30139 -193.90475 -206.89023 100.99765
## 2017 591.59992 76.24692 -117.30139 -193.90475 -206.89023 100.99765
## 2018 591.59992 76.24692 -117.30139 -193.90475 -206.89023 100.99765
## 2019 591.59992 76.24692 -117.30139 -193.90475 -206.89023 100.99765
## 2020 591.59992 76.24692 -117.30139 -193.90475 -206.89023 100.99765
## 2021 591.59992 76.24692 -117.30139 -193.90475 -206.89023 100.99765
## Jul Aug Sep Oct Nov Dec
## 2011 240.99680 177.44396 -188.12502 -154.42024 -78.31430 -248.32933
## 2012 240.99680 177.44396 -188.12502 -154.42024 -78.31430 -248.32933
## 2013 240.99680 177.44396 -188.12502 -154.42024 -78.31430 -248.32933
## 2014 240.99680 177.44396 -188.12502 -154.42024 -78.31430 -248.32933
## 2015 240.99680 177.44396 -188.12502 -154.42024 -78.31430 -248.32933
## 2016 240.99680 177.44396 -188.12502 -154.42024 -78.31430 -248.32933
## 2017 240.99680 177.44396 -188.12502 -154.42024 -78.31430 -248.32933
## 2018 240.99680 177.44396 -188.12502 -154.42024 -78.31430 -248.32933
## 2019 240.99680 177.44396 -188.12502 -154.42024 -78.31430 -248.32933
## 2020 240.99680 177.44396 -188.12502 -154.42024 -78.31430 -248.32933
## 2021 240.99680 177.44396NusaTenggaraBaratouttimeseriescomponents <- decompose(NusaTenggaraBaratoutflowtimeseries)
NusaTenggaraBaratouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -398.703916 -318.088130 -65.725125 146.280474 478.752890 312.185235
## 2012 -398.703916 -318.088130 -65.725125 146.280474 478.752890 312.185235
## 2013 -398.703916 -318.088130 -65.725125 146.280474 478.752890 312.185235
## 2014 -398.703916 -318.088130 -65.725125 146.280474 478.752890 312.185235
## 2015 -398.703916 -318.088130 -65.725125 146.280474 478.752890 312.185235
## 2016 -398.703916 -318.088130 -65.725125 146.280474 478.752890 312.185235
## 2017 -398.703916 -318.088130 -65.725125 146.280474 478.752890 312.185235
## 2018 -398.703916 -318.088130 -65.725125 146.280474 478.752890 312.185235
## 2019 -398.703916 -318.088130 -65.725125 146.280474 478.752890 312.185235
## 2020 -398.703916 -318.088130 -65.725125 146.280474 478.752890 312.185235
## 2021 -398.703916 -318.088130 -65.725125 146.280474 478.752890 312.185235
## Jul Aug Sep Oct Nov Dec
## 2011 97.389362 15.167839 -72.019715 -35.492355 -163.490922 3.744364
## 2012 97.389362 15.167839 -72.019715 -35.492355 -163.490922 3.744364
## 2013 97.389362 15.167839 -72.019715 -35.492355 -163.490922 3.744364
## 2014 97.389362 15.167839 -72.019715 -35.492355 -163.490922 3.744364
## 2015 97.389362 15.167839 -72.019715 -35.492355 -163.490922 3.744364
## 2016 97.389362 15.167839 -72.019715 -35.492355 -163.490922 3.744364
## 2017 97.389362 15.167839 -72.019715 -35.492355 -163.490922 3.744364
## 2018 97.389362 15.167839 -72.019715 -35.492355 -163.490922 3.744364
## 2019 97.389362 15.167839 -72.019715 -35.492355 -163.490922 3.744364
## 2020 97.389362 15.167839 -72.019715 -35.492355 -163.490922 3.744364
## 2021 97.389362 15.167839plot(NusaTenggaraBaratintimeseriescomponents$seasonal,type = "l", col = "steelblue")
lines(NusaTenggaraBaratouttimeseriescomponents$seasonal,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
plot(NusaTenggaraBaratintimeseriescomponents$trend,type = "l", col = "green")
lines(NusaTenggaraBaratouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("green","grey"))
plot(NusaTenggaraBaratintimeseriescomponents$random ,type = "l", col = "green")
lines(NusaTenggaraBaratouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("green","grey"))
plot(NusaTenggaraBaratintimeseriescomponents$figure ,type = "l", col = "green")
lines(NusaTenggaraBaratouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("green","grey"))