Visualisasi Data Inflow dan Outflow Uang Kartal di Gorontalo
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 Gorontalo 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 Gorontalo setiap periode
plot(datainflow$Keterangan,datainflow$`Gorontalo`,type = "l", col= "red")
2.Visualisasi Prediksi Data outflow Uang Kartal Gorontalo setiap periode
plot(dataoutflow$Keterangan,dataoutflow$`Gorontalo`,type = "l", col= "green")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Gorontalo Setiap Periode
plot(datainflow$Keterangan,datainflow$`Gorontalo`,type = "l", col= "red")
lines(dataoutflow$Keterangan,dataoutflow$`Gorontalo`,col="green")
legend("top",c("Inflow","Outflow"),fill=c("red","green"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Gorontalo 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$`Gorontalo`, type = "l", col = "grey")
lines(dataoutflowperbulan$`Gorontalo`,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("grey","blue"))
Gorontalotimeseries <- datainflowperbulan$`Gorontalo`
plot.ts(Gorontalotimeseries , type = "l", col = "green")
logGorontalo <- log(datainflowperbulan$`Gorontalo`)
plot.ts(logGorontalo)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Gorontalo
Gorontaloinflowtimeseries <- ts(datainflowperbulan$`Gorontalo`, frequency=12, start=c(2011,1))
Gorontaloinflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 NA NA NA NA NA NA NA
## 2012 NA NA NA NA NA NA NA
## 2013 NA NA NA NA NA NA NA
## 2014 NA NA NA NA NA NA NA
## 2015 NA NA NA NA NA NA NA
## 2016 NA NA NA NA NA NA NA
## 2017 NA NA NA NA NA NA NA
## 2018 NA NA NA NA NA 363.96884 308.59300
## 2019 315.39492 99.86948 59.92529 116.25330 131.83335 449.41120 205.30602
## 2020 475.48479 148.29931 118.65741 111.77349 197.56291 257.79680 183.28000
## 2021 531.77920 115.75515 103.51660 114.97692 508.82020 185.82336 122.99964
## Aug Sep Oct Nov Dec
## 2011 NA NA NA NA NA
## 2012 NA NA NA NA NA
## 2013 NA NA NA NA NA
## 2014 NA NA NA NA NA
## 2015 NA NA NA NA NA
## 2016 NA NA NA NA NA
## 2017 NA NA NA NA NA
## 2018 97.99315 60.36057 90.46237 121.68511 44.55530
## 2019 144.82253 81.42395 127.72298 133.27950 117.42572
## 2020 179.70448 149.52429 125.16276 175.90879 103.93751
## 2021 86.41267Gorontalooutflowtimeseries <- ts(dataoutflowperbulan$`Gorontalo`, frequency=12, start=c(2011,1))
Gorontalooutflowtimeseries## 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 NA NA NA NA NA NA
## 2016 NA NA NA NA NA NA
## 2017 NA NA NA NA NA NA
## 2018 NA NA NA NA NA 417.250174
## 2019 2.912239 68.440611 203.897987 319.561389 598.558179 8.668225
## 2020 30.047071 150.389916 217.802882 221.803520 413.276028 29.184300
## 2021 3.165417 87.416155 251.058011 319.642871 545.983514 64.195415
## 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 NA NA
## 2015 NA NA NA NA NA NA
## 2016 NA NA NA NA NA NA
## 2017 NA NA NA NA NA NA
## 2018 16.083437 117.195856 79.278672 71.280672 22.652846 203.212534
## 2019 117.410600 44.348755 150.013896 86.298130 92.811908 258.125606
## 2020 170.884251 171.084087 252.290005 246.858686 135.567549 343.019459
## 2021 163.078600 59.657002plot.ts(Gorontaloinflowtimeseries)
plot.ts(Gorontalooutflowtimeseries)
Gorontalointimeseriescomponents <- decompose(Gorontaloinflowtimeseries)
Gorontalointimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 265.220733 -51.619066 -75.930442 -53.788896 -4.956746 181.582333
## 2012 265.220733 -51.619066 -75.930442 -53.788896 -4.956746 181.582333
## 2013 265.220733 -51.619066 -75.930442 -53.788896 -4.956746 181.582333
## 2014 265.220733 -51.619066 -75.930442 -53.788896 -4.956746 181.582333
## 2015 265.220733 -51.619066 -75.930442 -53.788896 -4.956746 181.582333
## 2016 265.220733 -51.619066 -75.930442 -53.788896 -4.956746 181.582333
## 2017 265.220733 -51.619066 -75.930442 -53.788896 -4.956746 181.582333
## 2018 265.220733 -51.619066 -75.930442 -53.788896 -4.956746 181.582333
## 2019 265.220733 -51.619066 -75.930442 -53.788896 -4.956746 181.582333
## 2020 265.220733 -51.619066 -75.930442 -53.788896 -4.956746 181.582333
## 2021 265.220733 -51.619066 -75.930442 -53.788896 -4.956746 181.582333
## Jul Aug Sep Oct Nov Dec
## 2011 16.526207 -20.342255 -68.370743 -58.283553 -37.959580 -92.077991
## 2012 16.526207 -20.342255 -68.370743 -58.283553 -37.959580 -92.077991
## 2013 16.526207 -20.342255 -68.370743 -58.283553 -37.959580 -92.077991
## 2014 16.526207 -20.342255 -68.370743 -58.283553 -37.959580 -92.077991
## 2015 16.526207 -20.342255 -68.370743 -58.283553 -37.959580 -92.077991
## 2016 16.526207 -20.342255 -68.370743 -58.283553 -37.959580 -92.077991
## 2017 16.526207 -20.342255 -68.370743 -58.283553 -37.959580 -92.077991
## 2018 16.526207 -20.342255 -68.370743 -58.283553 -37.959580 -92.077991
## 2019 16.526207 -20.342255 -68.370743 -58.283553 -37.959580 -92.077991
## 2020 16.526207 -20.342255 -68.370743 -58.283553 -37.959580 -92.077991
## 2021 16.526207 -20.342255Gorontaloouttimeseriescomponents <- decompose(Gorontalooutflowtimeseries)
Gorontaloouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -161.32502 -72.52681 50.26838 102.83812 332.06250 -160.19337
## 2012 -161.32502 -72.52681 50.26838 102.83812 332.06250 -160.19337
## 2013 -161.32502 -72.52681 50.26838 102.83812 332.06250 -160.19337
## 2014 -161.32502 -72.52681 50.26838 102.83812 332.06250 -160.19337
## 2015 -161.32502 -72.52681 50.26838 102.83812 332.06250 -160.19337
## 2016 -161.32502 -72.52681 50.26838 102.83812 332.06250 -160.19337
## 2017 -161.32502 -72.52681 50.26838 102.83812 332.06250 -160.19337
## 2018 -161.32502 -72.52681 50.26838 102.83812 332.06250 -160.19337
## 2019 -161.32502 -72.52681 50.26838 102.83812 332.06250 -160.19337
## 2020 -161.32502 -72.52681 50.26838 102.83812 332.06250 -160.19337
## 2021 -161.32502 -72.52681 50.26838 102.83812 332.06250 -160.19337
## Jul Aug Sep Oct Nov Dec
## 2011 -37.89013 -74.72173 17.33598 -18.22176 -69.51684 91.89067
## 2012 -37.89013 -74.72173 17.33598 -18.22176 -69.51684 91.89067
## 2013 -37.89013 -74.72173 17.33598 -18.22176 -69.51684 91.89067
## 2014 -37.89013 -74.72173 17.33598 -18.22176 -69.51684 91.89067
## 2015 -37.89013 -74.72173 17.33598 -18.22176 -69.51684 91.89067
## 2016 -37.89013 -74.72173 17.33598 -18.22176 -69.51684 91.89067
## 2017 -37.89013 -74.72173 17.33598 -18.22176 -69.51684 91.89067
## 2018 -37.89013 -74.72173 17.33598 -18.22176 -69.51684 91.89067
## 2019 -37.89013 -74.72173 17.33598 -18.22176 -69.51684 91.89067
## 2020 -37.89013 -74.72173 17.33598 -18.22176 -69.51684 91.89067
## 2021 -37.89013 -74.72173plot(Gorontalointimeseriescomponents$seasonal,type = "l", col = "yellow")
lines(Gorontaloouttimeseriescomponents$seasonal,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(Gorontalointimeseriescomponents$trend,type = "l", col = "yellow")
lines(Gorontaloouttimeseriescomponents$trend,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(Gorontalointimeseriescomponents$random ,type = "l", col = "yellow")
lines(Gorontaloouttimeseriescomponents$random,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(Gorontalointimeseriescomponents$figure ,type = "l", col = "yellow")
lines(Gorontaloouttimeseriescomponents$figure,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
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