Visualisasi Data Inflow dan Outflow Uang Kartal di Papua Barat
Nurhisam Ari Kusnaini, Dosen Pengampu : Prof. Dr. Suhartono, M.kom
2022-02-21
UIN Maulana Malik Ibrahim Malang Teknik Informatika
Pengertian Inflow dan Outflow 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 adalah contoh penerapan visualisasi prediksi data Inflow-Outflow Uang Kartal di Papua Barat menggunakan bahasa pemrograman R.
library(readxl)datainflow <- read_excel(path = "data1.xlsx")## New names:
## * `` -> ...2datainflow## # A tibble: 12 x 13
## Keterangan ...2 Sulampua `Sulawesi Utara` `Sulawesi Tengah` `Sulawesi Sela~`
## <dbl> <lgl> <dbl> <dbl> <dbl> <dbl>
## 1 NA NA NA NA NA NA
## 2 2011 NA 25056. 5671. 1563. 10593.
## 3 2012 NA 31011. 6635. 1885. 13702.
## 4 2013 NA 63774. 21646. 1520. 17770.
## 5 2014 NA 41607. 7374. 3000. 19384.
## 6 2015 NA 40309. 6286. 2593. 19583.
## 7 2016 NA 45737. 7266. 2665. 21043.
## 8 2017 NA 44126. 7044. 2806. 18803.
## 9 2018 NA 52672. 7781. 3701. 21894.
## 10 2019 NA 60202. 7809. 4042. 24749.
## 11 2020 NA 52812. 6324. 3052. 21551.
## 12 2021 NA 45714. 4671. 2453. 18335.
## # ... with 7 more variables: `Sulawesi Tenggara` <dbl>, `Sulawesi Barat` <dbl>,
## # Gorontalo <dbl>, `Maluku Utara` <dbl>, Maluku <dbl>, Papua <dbl>,
## # `Papua Barat` <dbl>library (readxl)
dataoutflow <- read_excel(path = "data3.xlsx")
dataoutflow## # A tibble: 11 x 12
## Keterangan Sulampua `Sulawesi Utara` `Sulawesi Tengah` `Sulawesi Selatan`
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 36449. 6606. 4017. 8967.
## 2 2012 43623. 6375. 4458. 11873.
## 3 2013 64181. 22740. 4544. 11485.
## 4 2014 48231. 7207. 5696. 15645.
## 5 2015 53153. 7202. 5310. 16236.
## 6 2016 53145. 7707. 4962. 15494.
## 7 2017 56297. 8421. 5226. 15159.
## 8 2018 60935. 7605. 5578. 16779.
## 9 2019 60723. 7367. 5531. 18089.
## 10 2020 64828. 7437. 4674. 20503.
## 11 2021 33806. 3050. 2763. 12017.
## # ... with 7 more variables: `Sulawesi Tenggara` <dbl>, `Sulawesi Barat` <dbl>,
## # Gorontalo <dbl>, `Maluku Utara` <dbl>, Maluku <dbl>, Papua <dbl>,
## # `Papua Barat` <dbl>1.Visualisasi Prediksi Data Inflow Uang Kartal Papua Barat setiap periode
plot(datainflow$Keterangan,datainflow$`Papua Barat`,type = "l", col= "steelblue")
2.Visualisasi Prediksi Data outflow Uang Kartal Papua Barat setiap periode
plot(dataoutflow$Keterangan,dataoutflow$`Papua Barat`,type = "l", col= "red")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Papua Barat Setiap
plot(datainflow$Keterangan,datainflow$`Papua Barat`,type = "l", col= "steelblue")
lines(dataoutflow$Keterangan,dataoutflow$`Papua Barat`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Papua Barat Setiap Bulan
library(readxl)
datainflowperbulan <- read_excel(path = "data2.xlsx")## New names:
## * `` -> ...1
## * `` -> ...2dataoutflowperbulan <- read_excel(path = "data4.xlsx")## New names:
## * `` -> ...1datainflowperbulan## # A tibble: 128 x 13
## ...1 ...2 Sulampua `Sulawesi Utara` `Sulawesi Tengah`
## <dttm> <lgl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 NA 2584. 861. 167.
## 2 2011-02-01 00:00:00 NA 1504. 353. 46.1
## 3 2011-03-01 00:00:00 NA 2032. 415. 133.
## 4 2011-04-01 00:00:00 NA 1591. 342. 91.5
## 5 2011-05-01 00:00:00 NA 1704. 379. 106.
## 6 2011-06-01 00:00:00 NA 1795. 413. 77.0
## 7 2011-07-01 00:00:00 NA 1863. 480. 113.
## 8 2011-08-01 00:00:00 NA 1606. 415. 76.9
## 9 2011-09-01 00:00:00 NA 4967. 886. 446.
## 10 2011-10-01 00:00:00 NA 1918. 423. 113.
## # ... with 118 more rows, and 8 more variables: `Sulawesi Selatan` <dbl>,
## # `Sulawesi Tenggara` <dbl>, `Sulawesi Barat` <dbl>, Gorontalo <dbl>,
## # `Maluku Utara` <dbl>, Maluku <dbl>, Papua <dbl>, `Papua Barat` <dbl>dataoutflowperbulan## # A tibble: 128 x 12
## ...1 Sulampua `Sulawesi Utara` `Sulawesi Tengah`
## <dttm> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 966. 244. 83.5
## 2 2011-02-01 00:00:00 957. 260. 139.
## 3 2011-03-01 00:00:00 1982. 352. 189.
## 4 2011-04-01 00:00:00 2605. 460. 266.
## 5 2011-05-01 00:00:00 2559. 474. 317.
## 6 2011-06-01 00:00:00 2557. 459. 311.
## 7 2011-07-01 00:00:00 3087. 622. 351.
## 8 2011-08-01 00:00:00 6228. 985. 656.
## 9 2011-09-01 00:00:00 1234. 212. 105.
## 10 2011-10-01 00:00:00 2947. 545. 356.
## # ... with 118 more rows, and 8 more variables: `Sulawesi Selatan` <dbl>,
## # `Sulawesi Tenggara` <dbl>, `Sulawesi Barat` <dbl>, Gorontalo <dbl>,
## # `Maluku Utara` <dbl>, Maluku <dbl>, Papua <dbl>, `Papua Barat` <dbl>plot(datainflowperbulan$`Papua Barat`, type = "l", col = "green")
lines(dataoutflowperbulan$`Papua Barat`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))
PapuaBarattimeseries <- datainflowperbulan$`Papua Barat`
plot.ts(PapuaBarattimeseries, type = "l", col = "green")
logPapuaBarat <- log(datainflowperbulan$`Papua Barat`)
plot.ts(logPapuaBarat)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Papua Barat
PapuaBaratinflowtimeseries <- ts(datainflowperbulan$`Papua Barat`, frequency=12, start=c(2011,1))
PapuaBaratinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 NA NA NA NA NA NA
## 2012 NA NA NA NA NA NA
## 2013 NA NA NA NA NA NA
## 2014 NA NA NA NA NA NA
## 2015 234.786161 77.202679 42.333115 8.531739 12.219021 12.529806
## 2016 366.038954 119.481566 39.598847 10.650048 41.709904 14.203983
## 2017 252.350244 82.093992 110.628400 31.598978 59.325703 34.892096
## 2018 461.626541 126.237713 71.878709 28.365943 54.797393 151.098208
## 2019 508.781789 115.513617 53.002715 73.311085 82.682636 122.104424
## 2020 859.964132 102.333336 62.777983 55.573356 37.626417 165.383949
## 2021 818.035135 256.093228 247.396020 147.267117 169.155966 89.344549
## Jul Aug Sep Oct Nov Dec
## 2011 NA NA NA NA NA NA
## 2012 NA NA NA NA NA NA
## 2013 NA NA NA NA NA NA
## 2014 NA NA NA NA 7.068639 4.660277
## 2015 59.670808 23.755918 10.202931 4.128693 15.386826 16.789910
## 2016 92.752189 39.378805 33.605915 15.055478 16.170374 29.016368
## 2017 101.944111 74.428508 60.027285 65.514362 28.426638 32.070661
## 2018 74.826884 41.771733 54.451811 38.712443 30.245983 19.341296
## 2019 66.865723 85.400600 115.352739 70.857158 62.548625 91.250729
## 2020 68.525162 80.156326 67.120563 28.793665 66.178614 40.454433
## 2021 110.897296 68.918083PapuaBaratoutflowtimeseries <- ts(dataoutflowperbulan$`Papua Barat`, frequency=12, start=c(2011,1))
PapuaBaratoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 NA NA NA NA NA NA
## 2012 NA NA NA NA NA NA
## 2013 NA NA NA NA NA NA
## 2014 NA NA NA NA NA NA
## 2015 2.767224 7.072923 24.798564 28.167208 55.250230 187.994953
## 2016 4.191762 14.133856 100.632664 48.171528 68.772119 260.278234
## 2017 8.142376 60.573964 84.218742 122.365413 112.860755 353.131057
## 2018 3.245099 32.495810 99.615551 174.081181 175.936383 334.822064
## 2019 2.807260 25.800347 172.503804 166.091680 342.092930 34.737358
## 2020 13.233726 86.879593 111.519662 218.850834 195.727540 44.899549
## 2021 4.470038 13.376282 70.283378 153.045616 203.613392 256.123665
## Jul Aug Sep Oct Nov Dec
## 2011 NA NA NA NA NA NA
## 2012 NA NA NA NA NA NA
## 2013 NA NA NA NA NA NA
## 2014 NA NA NA NA 17.210321 152.933962
## 2015 207.655769 26.356468 101.941446 174.807233 211.519620 870.275185
## 2016 127.667628 92.959929 196.022979 80.122501 150.225158 780.605152
## 2017 199.939051 98.039457 138.257536 102.072876 242.989312 1098.098839
## 2018 248.735522 266.922816 158.742263 187.260764 240.258633 1078.634152
## 2019 231.213770 109.885819 221.951256 233.287557 362.754221 1415.519360
## 2020 167.911591 160.036351 159.147310 121.654858 442.446051 1363.941709
## 2021 70.287839 86.299612plot.ts(PapuaBaratinflowtimeseries)
plot.ts(PapuaBaratoutflowtimeseries)
PapuaBaratintimeseriescomponents <- decompose(PapuaBaratinflowtimeseries)
PapuaBaratintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun Jul
## 2011 434.38233 22.87241 -30.85979 -59.21710 -42.15346 -7.50395 -17.74079
## 2012 434.38233 22.87241 -30.85979 -59.21710 -42.15346 -7.50395 -17.74079
## 2013 434.38233 22.87241 -30.85979 -59.21710 -42.15346 -7.50395 -17.74079
## 2014 434.38233 22.87241 -30.85979 -59.21710 -42.15346 -7.50395 -17.74079
## 2015 434.38233 22.87241 -30.85979 -59.21710 -42.15346 -7.50395 -17.74079
## 2016 434.38233 22.87241 -30.85979 -59.21710 -42.15346 -7.50395 -17.74079
## 2017 434.38233 22.87241 -30.85979 -59.21710 -42.15346 -7.50395 -17.74079
## 2018 434.38233 22.87241 -30.85979 -59.21710 -42.15346 -7.50395 -17.74079
## 2019 434.38233 22.87241 -30.85979 -59.21710 -42.15346 -7.50395 -17.74079
## 2020 434.38233 22.87241 -30.85979 -59.21710 -42.15346 -7.50395 -17.74079
## 2021 434.38233 22.87241 -30.85979 -59.21710 -42.15346 -7.50395 -17.74079
## Aug Sep Oct Nov Dec
## 2011 -42.98226 -46.33704 -68.34111 -71.07851 -71.04073
## 2012 -42.98226 -46.33704 -68.34111 -71.07851 -71.04073
## 2013 -42.98226 -46.33704 -68.34111 -71.07851 -71.04073
## 2014 -42.98226 -46.33704 -68.34111 -71.07851 -71.04073
## 2015 -42.98226 -46.33704 -68.34111 -71.07851 -71.04073
## 2016 -42.98226 -46.33704 -68.34111 -71.07851 -71.04073
## 2017 -42.98226 -46.33704 -68.34111 -71.07851 -71.04073
## 2018 -42.98226 -46.33704 -68.34111 -71.07851 -71.04073
## 2019 -42.98226 -46.33704 -68.34111 -71.07851 -71.04073
## 2020 -42.98226 -46.33704 -68.34111 -71.07851 -71.04073
## 2021 -42.98226PapuaBaratouttimeseriescomponents <- decompose(PapuaBaratoutflowtimeseries)
PapuaBaratouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -222.24794 -188.84867 -110.74805 -78.56779 -45.52017 -12.67910
## 2012 -222.24794 -188.84867 -110.74805 -78.56779 -45.52017 -12.67910
## 2013 -222.24794 -188.84867 -110.74805 -78.56779 -45.52017 -12.67910
## 2014 -222.24794 -188.84867 -110.74805 -78.56779 -45.52017 -12.67910
## 2015 -222.24794 -188.84867 -110.74805 -78.56779 -45.52017 -12.67910
## 2016 -222.24794 -188.84867 -110.74805 -78.56779 -45.52017 -12.67910
## 2017 -222.24794 -188.84867 -110.74805 -78.56779 -45.52017 -12.67910
## 2018 -222.24794 -188.84867 -110.74805 -78.56779 -45.52017 -12.67910
## 2019 -222.24794 -188.84867 -110.74805 -78.56779 -45.52017 -12.67910
## 2020 -222.24794 -188.84867 -110.74805 -78.56779 -45.52017 -12.67910
## 2021 -222.24794 -188.84867 -110.74805 -78.56779 -45.52017 -12.67910
## Jul Aug Sep Oct Nov Dec
## 2011 -26.55735 -98.10003 -61.48268 -75.47525 47.79177 872.43525
## 2012 -26.55735 -98.10003 -61.48268 -75.47525 47.79177 872.43525
## 2013 -26.55735 -98.10003 -61.48268 -75.47525 47.79177 872.43525
## 2014 -26.55735 -98.10003 -61.48268 -75.47525 47.79177 872.43525
## 2015 -26.55735 -98.10003 -61.48268 -75.47525 47.79177 872.43525
## 2016 -26.55735 -98.10003 -61.48268 -75.47525 47.79177 872.43525
## 2017 -26.55735 -98.10003 -61.48268 -75.47525 47.79177 872.43525
## 2018 -26.55735 -98.10003 -61.48268 -75.47525 47.79177 872.43525
## 2019 -26.55735 -98.10003 -61.48268 -75.47525 47.79177 872.43525
## 2020 -26.55735 -98.10003 -61.48268 -75.47525 47.79177 872.43525
## 2021 -26.55735 -98.10003plot(PapuaBaratintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(PapuaBaratouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(PapuaBaratintimeseriescomponents$trend,type = "l", col = "orange")
lines(PapuaBaratouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(PapuaBaratintimeseriescomponents$random ,type = "l", col = "orange")
lines(PapuaBaratouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(PapuaBaratintimeseriescomponents$figure ,type = "l", col = "orange")
lines(PapuaBaratouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
Daftar Pustaka
https://ejurnal.its.ac.id/index.php/sains_seni/article/download/12401/2433#
https://www.bi.go.id/id/statistik/ekonomi-keuangan/ssp/indikator-pengedaran-uang.aspx
https://www.bi.go.id/id/fungsi-utama/sistem-pembayaran/pengelolaan-rupiah/default.aspx8