Visualisasi Data Inflow dan Outflow Uang Kartal di Sulawesi 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 Sulawesi 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 Sulawesi Barat setiap periode
plot(datainflow$Keterangan,datainflow$`Sulawesi Barat`,type = "l", col= "steelblue")
2.Visualisasi Prediksi Data outflow Uang Kartal Sulawesi Barat setiap periode
plot(dataoutflow$Keterangan,dataoutflow$`Sulawesi Barat`,type = "l", col= "red")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sulawesi Barat Setiap Periode
plot(datainflow$Keterangan,datainflow$`Sulawesi Barat`,type = "l", col= "steelblue")
lines(dataoutflow$Keterangan,dataoutflow$`Sulawesi Barat`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sulawesi 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$`Sulawesi Barat`, type = "l", col = "green")
lines(dataoutflowperbulan$`Sulawesi Barat`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))
Sulawesibarattimeseries <- datainflowperbulan$`Sulawesi Barat`
plot.ts(Sulawesibarattimeseries , type = "l", col = "green")
logSulawesiBarat <- log(datainflowperbulan$`Sulawesi Barat`)
plot.ts(logSulawesiBarat)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Sulawesi Barat
SulawesiBaratinflowtimeseries <- ts(datainflowperbulan$`Sulawesi Barat`, frequency=12, start=c(2011,1))
SulawesiBaratinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 NA NA NA NA NA NA
## 2012 NA NA NA NA NA NA
## 2013 NA NA NA NA NA NA
## 2014 NA NA NA NA NA NA
## 2015 NA NA NA NA NA NA
## 2016 77.595428 64.101689 18.748180 12.258352 8.705777 18.447796
## 2017 120.053332 79.685491 86.532548 54.133096 28.137053 49.040998
## 2018 127.244502 51.775206 57.938918 58.573614 41.243495 57.522559
## 2019 76.833814 29.953971 33.556790 30.108758 36.980986 62.625995
## 2020 122.384359 30.292156 15.326717 20.885047 18.402881 40.432542
## 2021 28.332570 9.319300 13.086396 31.672670 102.201162 27.274000
## Jul Aug Sep Oct Nov Dec
## 2011 NA NA NA NA NA NA
## 2012 NA NA NA NA NA NA
## 2013 NA NA NA NA NA NA
## 2014 NA NA NA NA NA NA
## 2015 NA NA NA 13.701551 6.191400 29.353053
## 2016 96.550392 49.820083 47.543647 59.958986 37.485351 44.899721
## 2017 129.267763 40.592610 43.896701 60.195531 33.448904 20.609082
## 2018 26.956823 37.810652 46.816296 44.682094 37.646507 18.233551
## 2019 38.654603 63.401570 55.206423 43.582463 46.267430 24.758847
## 2020 16.719525 17.667817 11.774420 11.720843 13.701609 9.442328
## 2021 35.099550 18.351060SulawesiBaratoutflowtimeseries <- ts(dataoutflowperbulan$`Sulawesi Barat`, frequency=12, start=c(2011,1))
SulawesiBaratoutflowtimeseries## 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 19.90250 17.32711 99.23076 138.83358 193.29539 371.53878 32.27097
## 2017 14.29429 115.92768 125.99148 212.53633 174.84550 440.90400 178.80223
## 2018 101.30289 150.83527 212.32664 336.47224 328.16741 534.88122 321.46516
## 2019 92.48808 146.63696 175.08767 339.97890 538.88879 61.22788 211.31975
## 2020 152.06847 163.32592 203.27818 227.28349 406.92020 166.04479 202.29854
## 2021 66.17178 119.53930 221.76242 342.99771 465.56940 276.96649 293.84955
## 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 46.90420 177.36203 422.81740
## 2016 103.73641 167.54213 71.51592 90.11001 208.70877
## 2017 187.20346 105.87504 132.31761 271.81236 543.26785
## 2018 357.64045 149.54061 194.70929 225.27841 437.63070
## 2019 190.85261 152.56301 126.63727 226.12018 487.38639
## 2020 222.39968 163.24144 193.04839 350.95351 470.19210
## 2021 291.95972plot.ts(SulawesiBaratinflowtimeseries)
plot.ts(SulawesiBaratoutflowtimeseries)
SulawesiBaratintimeseriescomponents <- decompose(SulawesiBaratinflowtimeseries)
SulawesiBaratintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 50.2469745 -3.7431838 0.3665722 -10.0657577 -18.6095718 0.4137056
## 2012 50.2469745 -3.7431838 0.3665722 -10.0657577 -18.6095718 0.4137056
## 2013 50.2469745 -3.7431838 0.3665722 -10.0657577 -18.6095718 0.4137056
## 2014 50.2469745 -3.7431838 0.3665722 -10.0657577 -18.6095718 0.4137056
## 2015 50.2469745 -3.7431838 0.3665722 -10.0657577 -18.6095718 0.4137056
## 2016 50.2469745 -3.7431838 0.3665722 -10.0657577 -18.6095718 0.4137056
## 2017 50.2469745 -3.7431838 0.3665722 -10.0657577 -18.6095718 0.4137056
## 2018 50.2469745 -3.7431838 0.3665722 -10.0657577 -18.6095718 0.4137056
## 2019 50.2469745 -3.7431838 0.3665722 -10.0657577 -18.6095718 0.4137056
## 2020 50.2469745 -3.7431838 0.3665722 -10.0657577 -18.6095718 0.4137056
## 2021 50.2469745 -3.7431838 0.3665722 -10.0657577 -18.6095718 0.4137056
## Jul Aug Sep Oct Nov Dec
## 2011 17.0059952 -1.8982360 -2.2055834 0.6602980 -10.5986393 -21.5725736
## 2012 17.0059952 -1.8982360 -2.2055834 0.6602980 -10.5986393 -21.5725736
## 2013 17.0059952 -1.8982360 -2.2055834 0.6602980 -10.5986393 -21.5725736
## 2014 17.0059952 -1.8982360 -2.2055834 0.6602980 -10.5986393 -21.5725736
## 2015 17.0059952 -1.8982360 -2.2055834 0.6602980 -10.5986393 -21.5725736
## 2016 17.0059952 -1.8982360 -2.2055834 0.6602980 -10.5986393 -21.5725736
## 2017 17.0059952 -1.8982360 -2.2055834 0.6602980 -10.5986393 -21.5725736
## 2018 17.0059952 -1.8982360 -2.2055834 0.6602980 -10.5986393 -21.5725736
## 2019 17.0059952 -1.8982360 -2.2055834 0.6602980 -10.5986393 -21.5725736
## 2020 17.0059952 -1.8982360 -2.2055834 0.6602980 -10.5986393 -21.5725736
## 2021 17.0059952 -1.8982360SulawesiBaratouttimeseriescomponents <- decompose(SulawesiBaratoutflowtimeseries)
SulawesiBaratouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -143.463201 -93.223630 -45.911492 40.254380 114.992467 99.646958
## 2012 -143.463201 -93.223630 -45.911492 40.254380 114.992467 99.646958
## 2013 -143.463201 -93.223630 -45.911492 40.254380 114.992467 99.646958
## 2014 -143.463201 -93.223630 -45.911492 40.254380 114.992467 99.646958
## 2015 -143.463201 -93.223630 -45.911492 40.254380 114.992467 99.646958
## 2016 -143.463201 -93.223630 -45.911492 40.254380 114.992467 99.646958
## 2017 -143.463201 -93.223630 -45.911492 40.254380 114.992467 99.646958
## 2018 -143.463201 -93.223630 -45.911492 40.254380 114.992467 99.646958
## 2019 -143.463201 -93.223630 -45.911492 40.254380 114.992467 99.646958
## 2020 -143.463201 -93.223630 -45.911492 40.254380 114.992467 99.646958
## 2021 -143.463201 -93.223630 -45.911492 40.254380 114.992467 99.646958
## Jul Aug Sep Oct Nov Dec
## 2011 -26.821416 -4.923568 -71.410509 -78.239725 6.999157 202.100578
## 2012 -26.821416 -4.923568 -71.410509 -78.239725 6.999157 202.100578
## 2013 -26.821416 -4.923568 -71.410509 -78.239725 6.999157 202.100578
## 2014 -26.821416 -4.923568 -71.410509 -78.239725 6.999157 202.100578
## 2015 -26.821416 -4.923568 -71.410509 -78.239725 6.999157 202.100578
## 2016 -26.821416 -4.923568 -71.410509 -78.239725 6.999157 202.100578
## 2017 -26.821416 -4.923568 -71.410509 -78.239725 6.999157 202.100578
## 2018 -26.821416 -4.923568 -71.410509 -78.239725 6.999157 202.100578
## 2019 -26.821416 -4.923568 -71.410509 -78.239725 6.999157 202.100578
## 2020 -26.821416 -4.923568 -71.410509 -78.239725 6.999157 202.100578
## 2021 -26.821416 -4.923568plot(SulawesiBaratintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(SulawesiBaratouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(SulawesiBaratintimeseriescomponents$trend,type = "l", col = "orange")
lines(SulawesiBaratouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(SulawesiBaratintimeseriescomponents$random ,type = "l", col = "orange")
lines(SulawesiBaratouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(SulawesiBaratintimeseriescomponents$figure ,type = "l", col = "orange")
lines(SulawesiBaratouttimeseriescomponents$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