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 Timur setiap periode

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

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

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

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

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

4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Kalimantan Timur 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$`Kalimantan Timur`, type = "l", col = "steelblue")
lines(dataoutflowperbulan$`Kalimantan Timur`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))

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

logKalimantanTimur <- log(datainflowperbulan$`Kalimantan Timur`)
plot.ts(logKalimantanTimur)

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

KalimantanTimurinflowtimeseries <- ts(datainflowperbulan$`Kalimantan Timur`, frequency=12, start=c(2011,1))
KalimantanTimurinflowtimeseries
##            Jan       Feb       Mar       Apr       May       Jun       Jul
## 2011  386.8959  156.0572  327.5300  227.3215  259.4598  241.0772  209.0709
## 2012  813.4070  500.6319  350.5007  374.4735  419.0225  314.0501  404.2772
## 2013 1481.9062  748.0774  702.4110  603.9930  566.9668  577.5010  535.8822
## 2014 1324.2797  769.0908  609.2509  715.5751  480.4685  572.7389  317.5474
## 2015 1787.6819  846.2909  774.7626  654.3524  571.1234  613.4639 1590.8039
## 2016 1446.7594  957.0854  783.1885  678.5723  876.6097  536.9560 2079.3739
## 2017 1354.7299  891.1119  817.4353  726.6774  793.2949  355.3978 2179.7522
## 2018 1280.5636  789.4093  764.6919  881.9106  843.8006 2424.6150 1234.0321
## 2019 1516.7540  936.3101  861.4164 1115.6047  773.2035 2978.5227 1220.8372
## 2020 1631.3523  909.6022  646.9720  716.2299  807.6215 1658.0662  870.7932
## 2021 1943.7774  958.3480  905.2772  852.9136 1746.3150 1007.1073  786.9856
##            Aug       Sep       Oct       Nov       Dec
## 2011  234.6546 1380.6184  296.2488  379.3212  194.9991
## 2012 1084.6745  579.8222  248.0938  371.1188  283.1429
## 2013 3112.1327  519.8297  494.8781  470.7488  300.4810
## 2014 1872.0443  611.8721  671.9655  576.6264  414.1527
## 2015  758.9619  618.2369  576.8371  466.6612  386.8918
## 2016  886.1318  775.5502  609.4078  665.2391  608.3375
## 2017  921.8936  902.6810  770.3086  709.0181  510.3760
## 2018  891.9402  833.8232  976.6633  743.6479  639.7601
## 2019 1108.3736  975.5682 1035.1930  861.6003  607.9922
## 2020  786.0973  859.7558  578.4327  780.0292  367.3676
## 2021  713.3462
KalimantanTimuroutflowtimeseries <- ts(dataoutflowperbulan$`Kalimantan Timur`, frequency=12, start=c(2011,1))
KalimantanTimuroutflowtimeseries
##            Jan       Feb       Mar       Apr       May       Jun       Jul
## 2011  105.9584  183.1731  709.0119  861.9188  850.8895  893.8170  978.8524
## 2012  141.2637  447.3704  871.3136  950.4211 1144.6693 1172.7716 1263.9854
## 2013  458.9434  592.9759 1098.7873  975.5426 1260.1516 1273.3499 3482.4698
## 2014  476.5567  490.5591 1245.1557 1120.4022 1199.6649 1162.3492 3927.0262
## 2015  295.5754  623.2929  973.2470 1348.7651 1142.1460 1678.3206 3409.1998
## 2016  264.0031  749.6513  735.6124 1139.1516 1410.0316 3186.7023 1144.4938
## 2017  689.1725  862.2065 1291.8852 1238.8607 1352.2792 3801.5073  465.3724
## 2018  334.8694  925.5199 1365.6305 1244.3834 2516.1295 3018.9876  909.1610
## 2019  575.9478  978.4527 1253.5146 1618.2094 4740.3074  328.8598 1280.6878
## 2020  575.0570  770.9608 1165.8999 1376.6102 2521.2020  396.3374 1099.4440
## 2021  117.3321  751.8656  870.9665 1952.6925 2309.5234 1037.9313 1119.2996
##            Aug       Sep       Oct       Nov       Dec
## 2011 2853.7373  258.1347  962.8278 1111.9943 2567.1792
## 2012 2345.2061  420.0458 1324.9692 1410.1386 2933.5597
## 2013 1668.2297  964.4201 1417.6193 1661.6678 3596.7990
## 2014  224.3035 1069.6359 1667.7380 1287.4784 3526.7232
## 2015  408.7450 1046.2819 1227.4938 1492.9202 2868.4423
## 2016  871.6023 1270.6361  979.0426 1258.9000 2211.3448
## 2017 1418.9076  874.2134 1055.7755 1577.8654 1896.5708
## 2018 1369.8084  988.0312 1232.0343 1567.7647 2251.9540
## 2019 1439.7870 1067.5958 1207.9468 1591.9049 2513.1615
## 2020  873.2943  818.7630 1534.5310 1295.0360 2566.1166
## 2021  950.6085
plot.ts(KalimantanTimurinflowtimeseries)

plot.ts(KalimantanTimuroutflowtimeseries)

KalimantanTimurintimeseriescomponents <- decompose(KalimantanTimurinflowtimeseries)
KalimantanTimurintimeseriescomponents$seasonal
##             Jan        Feb        Mar        Apr        May        Jun
## 2011  594.35193  -37.57596 -156.71537 -138.19263 -178.60724  251.98410
## 2012  594.35193  -37.57596 -156.71537 -138.19263 -178.60724  251.98410
## 2013  594.35193  -37.57596 -156.71537 -138.19263 -178.60724  251.98410
## 2014  594.35193  -37.57596 -156.71537 -138.19263 -178.60724  251.98410
## 2015  594.35193  -37.57596 -156.71537 -138.19263 -178.60724  251.98410
## 2016  594.35193  -37.57596 -156.71537 -138.19263 -178.60724  251.98410
## 2017  594.35193  -37.57596 -156.71537 -138.19263 -178.60724  251.98410
## 2018  594.35193  -37.57596 -156.71537 -138.19263 -178.60724  251.98410
## 2019  594.35193  -37.57596 -156.71537 -138.19263 -178.60724  251.98410
## 2020  594.35193  -37.57596 -156.71537 -138.19263 -178.60724  251.98410
## 2021  594.35193  -37.57596 -156.71537 -138.19263 -178.60724  251.98410
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011  244.85041  336.47397  -29.19087 -214.17769 -246.38132 -426.81935
## 2012  244.85041  336.47397  -29.19087 -214.17769 -246.38132 -426.81935
## 2013  244.85041  336.47397  -29.19087 -214.17769 -246.38132 -426.81935
## 2014  244.85041  336.47397  -29.19087 -214.17769 -246.38132 -426.81935
## 2015  244.85041  336.47397  -29.19087 -214.17769 -246.38132 -426.81935
## 2016  244.85041  336.47397  -29.19087 -214.17769 -246.38132 -426.81935
## 2017  244.85041  336.47397  -29.19087 -214.17769 -246.38132 -426.81935
## 2018  244.85041  336.47397  -29.19087 -214.17769 -246.38132 -426.81935
## 2019  244.85041  336.47397  -29.19087 -214.17769 -246.38132 -426.81935
## 2020  244.85041  336.47397  -29.19087 -214.17769 -246.38132 -426.81935
## 2021  244.85041  336.47397
KalimantanTimurouttimeseriescomponents <- decompose(KalimantanTimuroutflowtimeseries)
KalimantanTimurouttimeseriescomponents$seasonal
##             Jan        Feb        Mar        Apr        May        Jun
## 2011 -994.70574 -660.94783 -273.49778 -166.37337  527.26967  385.60544
## 2012 -994.70574 -660.94783 -273.49778 -166.37337  527.26967  385.60544
## 2013 -994.70574 -660.94783 -273.49778 -166.37337  527.26967  385.60544
## 2014 -994.70574 -660.94783 -273.49778 -166.37337  527.26967  385.60544
## 2015 -994.70574 -660.94783 -273.49778 -166.37337  527.26967  385.60544
## 2016 -994.70574 -660.94783 -273.49778 -166.37337  527.26967  385.60544
## 2017 -994.70574 -660.94783 -273.49778 -166.37337  527.26967  385.60544
## 2018 -994.70574 -660.94783 -273.49778 -166.37337  527.26967  385.60544
## 2019 -994.70574 -660.94783 -273.49778 -166.37337  527.26967  385.60544
## 2020 -994.70574 -660.94783 -273.49778 -166.37337  527.26967  385.60544
## 2021 -994.70574 -660.94783 -273.49778 -166.37337  527.26967  385.60544
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011  437.65875  -13.46535 -486.09602 -108.09369   45.85300 1306.79293
## 2012  437.65875  -13.46535 -486.09602 -108.09369   45.85300 1306.79293
## 2013  437.65875  -13.46535 -486.09602 -108.09369   45.85300 1306.79293
## 2014  437.65875  -13.46535 -486.09602 -108.09369   45.85300 1306.79293
## 2015  437.65875  -13.46535 -486.09602 -108.09369   45.85300 1306.79293
## 2016  437.65875  -13.46535 -486.09602 -108.09369   45.85300 1306.79293
## 2017  437.65875  -13.46535 -486.09602 -108.09369   45.85300 1306.79293
## 2018  437.65875  -13.46535 -486.09602 -108.09369   45.85300 1306.79293
## 2019  437.65875  -13.46535 -486.09602 -108.09369   45.85300 1306.79293
## 2020  437.65875  -13.46535 -486.09602 -108.09369   45.85300 1306.79293
## 2021  437.65875  -13.46535
plot(KalimantanTimurintimeseriescomponents$seasonal,type = "l", col = "steelblue")
lines(KalimantanTimurouttimeseriescomponents$seasonal,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))

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

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

plot(KalimantanTimurintimeseriescomponents$figure ,type = "l", col = "green")
lines(KalimantanTimurouttimeseriescomponents$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