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:
## * `` -> ...2
datainflow
## # 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 Kalimantan Utara setiap periode

plot(datainflow$Keterangan,datainflow$`Kalimantan Utara`,type = "l", col= "steelblue")

2. Visualisasi Prediksi Data outflow Uang Kartal Kalimantan Utara setiap periode

plot(dataoutflow$Tahun,dataoutflow$`Kalimantan Utara`,type = "l", col= "red")

3. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Kalimantan Utara Setiap Periode

plot(datainflow$Keterangan,datainflow$`Kalimantan Utara`,type = "l", col= "steelblue")
lines(dataoutflow$Tahun,dataoutflow$`Kalimantan Utara`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("green","steelblue"))

4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Kalimantan Utara Setiap Bulan

library(readxl)
datainflowperbulan <- read_excel(path = "InflowBulan.xlsx")
## New names:
## * `` -> ...2
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 <- 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$`Kalimantan Utara`, type = "l", col = "steelblue")
lines(dataoutflowperbulan$`Kalimantan Utara`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))

KalimantanUtaratimeseries <- datainflowperbulan$`Kalimantan Utara`
plot.ts(KalimantanUtaratimeseries , type = "l", col = "steelblue")

logKalimantanUtara <- log(datainflowperbulan$`Kalimantan Utara`)
plot.ts(logKalimantanUtara)

5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Kalimantan Utara

KalimantanUtarainflowtimeseries <- ts(datainflowperbulan$`Kalimantan Utara`, frequency=12, start=c(2011,1))
KalimantanUtarainflowtimeseries
##            Jan       Feb       Mar       Apr       May       Jun       Jul
## 2011   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
## 2012   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
## 2013   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
## 2014   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
## 2015   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
## 2016   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000   0.00000
## 2017   0.00000   0.00000   0.00000   0.00000  17.96158  13.75579  95.91541
## 2018 174.11300  64.84647  70.92668  52.60056  46.62695 187.39100 122.33860
## 2019 162.42994  62.96498  44.90811  59.05614  44.72298 286.68414  67.74340
## 2020 285.54912 107.13178  72.02208  32.30300  94.25600 179.66090 103.53773
## 2021 409.60604 170.38744 146.70954 148.67880 286.59420 204.94956 163.06608
##            Aug       Sep       Oct       Nov       Dec
## 2011   0.00000   0.00000   0.00000   0.00000   0.00000
## 2012   0.00000   0.00000   0.00000   0.00000   0.00000
## 2013   0.00000   0.00000   0.00000   0.00000   0.00000
## 2014   0.00000   0.00000   0.00000   0.00000   0.00000
## 2015   0.00000   0.00000   0.00000   0.00000   0.00000
## 2016   0.00000   0.00000   0.00000   0.00000   0.00000
## 2017  30.00940  56.64839  46.61159  52.03657  28.46708
## 2018  51.54898  41.82854  36.46858  44.38170  23.59408
## 2019  82.03860 170.07789 179.30134 175.09017 137.20712
## 2020 124.19961 123.39072  61.11577 135.42532  43.31592
## 2021 140.69873
KalimantanUtaraoutflowtimeseries <- ts(dataoutflowperbulan$`Kalimantan Utara`, frequency=12, start=c(2011,1))
KalimantanUtaraoutflowtimeseries
##             Jan        Feb        Mar        Apr        May        Jun
## 2011   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
## 2012   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
## 2013   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
## 2014   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
## 2015   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
## 2016   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
## 2017   0.000000   0.000000   0.000000   0.000000   5.119011 246.156049
## 2018  21.008828  60.034740 138.021099 153.807490 259.968737 569.053524
## 2019  77.085773  78.330023 106.801568 248.832003 613.235229  29.209710
## 2020  95.351840 106.628715 173.244679 277.200492 383.823708 104.266497
## 2021 112.712430 124.489211 204.121347 342.138231 416.753104 202.968069
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
## 2012   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
## 2013   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
## 2014   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
## 2015   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
## 2016   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000
## 2017  18.682123 154.628033 114.952701 199.262928 205.099541 563.191658
## 2018 104.045686 163.751548 114.203237 190.138457 239.338260 457.640197
## 2019 169.896454 186.931547 253.272651 296.507943 382.803226 652.686582
## 2020 210.914225 226.039764 193.439557 264.907849 234.822526 555.828067
## 2021 289.305556 267.027266
plot.ts(KalimantanUtarainflowtimeseries)

plot.ts(KalimantanUtaraoutflowtimeseries)

KalimantanUtaraintimeseriescomponents <- decompose(KalimantanUtarainflowtimeseries)
KalimantanUtaraintimeseriescomponents$seasonal
##             Jan        Feb        Mar        Apr        May        Jun
## 2011  57.015598  -6.886833 -14.207666 -19.939325 -14.226147  36.493493
## 2012  57.015598  -6.886833 -14.207666 -19.939325 -14.226147  36.493493
## 2013  57.015598  -6.886833 -14.207666 -19.939325 -14.226147  36.493493
## 2014  57.015598  -6.886833 -14.207666 -19.939325 -14.226147  36.493493
## 2015  57.015598  -6.886833 -14.207666 -19.939325 -14.226147  36.493493
## 2016  57.015598  -6.886833 -14.207666 -19.939325 -14.226147  36.493493
## 2017  57.015598  -6.886833 -14.207666 -19.939325 -14.226147  36.493493
## 2018  57.015598  -6.886833 -14.207666 -19.939325 -14.226147  36.493493
## 2019  57.015598  -6.886833 -14.207666 -19.939325 -14.226147  36.493493
## 2020  57.015598  -6.886833 -14.207666 -19.939325 -14.226147  36.493493
## 2021  57.015598  -6.886833 -14.207666 -19.939325 -14.226147  36.493493
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011   3.163097  -9.427396  -0.333739  -8.409349  -1.879339 -21.362394
## 2012   3.163097  -9.427396  -0.333739  -8.409349  -1.879339 -21.362394
## 2013   3.163097  -9.427396  -0.333739  -8.409349  -1.879339 -21.362394
## 2014   3.163097  -9.427396  -0.333739  -8.409349  -1.879339 -21.362394
## 2015   3.163097  -9.427396  -0.333739  -8.409349  -1.879339 -21.362394
## 2016   3.163097  -9.427396  -0.333739  -8.409349  -1.879339 -21.362394
## 2017   3.163097  -9.427396  -0.333739  -8.409349  -1.879339 -21.362394
## 2018   3.163097  -9.427396  -0.333739  -8.409349  -1.879339 -21.362394
## 2019   3.163097  -9.427396  -0.333739  -8.409349  -1.879339 -21.362394
## 2020   3.163097  -9.427396  -0.333739  -8.409349  -1.879339 -21.362394
## 2021   3.163097  -9.427396
KalimantanUtaraouttimeseriescomponents <- decompose(KalimantanUtaraoutflowtimeseries)
KalimantanUtaraouttimeseriescomponents$seasonal
##             Jan        Feb        Mar        Apr        May        Jun
## 2011 -64.346941 -60.332613 -34.109851  -7.145981  55.241197  16.751788
## 2012 -64.346941 -60.332613 -34.109851  -7.145981  55.241197  16.751788
## 2013 -64.346941 -60.332613 -34.109851  -7.145981  55.241197  16.751788
## 2014 -64.346941 -60.332613 -34.109851  -7.145981  55.241197  16.751788
## 2015 -64.346941 -60.332613 -34.109851  -7.145981  55.241197  16.751788
## 2016 -64.346941 -60.332613 -34.109851  -7.145981  55.241197  16.751788
## 2017 -64.346941 -60.332613 -34.109851  -7.145981  55.241197  16.751788
## 2018 -64.346941 -60.332613 -34.109851  -7.145981  55.241197  16.751788
## 2019 -64.346941 -60.332613 -34.109851  -7.145981  55.241197  16.751788
## 2020 -64.346941 -60.332613 -34.109851  -7.145981  55.241197  16.751788
## 2021 -64.346941 -60.332613 -34.109851  -7.145981  55.241197  16.751788
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011 -32.179988 -10.387088 -17.304573   7.914248  15.876839 130.022962
## 2012 -32.179988 -10.387088 -17.304573   7.914248  15.876839 130.022962
## 2013 -32.179988 -10.387088 -17.304573   7.914248  15.876839 130.022962
## 2014 -32.179988 -10.387088 -17.304573   7.914248  15.876839 130.022962
## 2015 -32.179988 -10.387088 -17.304573   7.914248  15.876839 130.022962
## 2016 -32.179988 -10.387088 -17.304573   7.914248  15.876839 130.022962
## 2017 -32.179988 -10.387088 -17.304573   7.914248  15.876839 130.022962
## 2018 -32.179988 -10.387088 -17.304573   7.914248  15.876839 130.022962
## 2019 -32.179988 -10.387088 -17.304573   7.914248  15.876839 130.022962
## 2020 -32.179988 -10.387088 -17.304573   7.914248  15.876839 130.022962
## 2021 -32.179988 -10.387088
plot(KalimantanUtaraintimeseriescomponents$seasonal,type = "l", col = "steelblue")
lines(KalimantanUtaraouttimeseriescomponents$seasonal,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))

plot(KalimantanUtaraintimeseriescomponents$trend,type = "l", col = "green")
lines(KalimantanUtaraouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("green","grey"))

plot(KalimantanUtaraintimeseriescomponents$random ,type = "l", col = "green")
lines(KalimantanUtaraouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("green","grey"))

plot(KalimantanUtaraintimeseriescomponents$figure ,type = "l", col = "green")
lines(KalimantanUtaraouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("green","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