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 Selatan setiap periode

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

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

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

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

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

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

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

logKalimantanSelatan <- log(datainflowperbulan$`Kalimantan Selatan`)
plot.ts(logKalimantanSelatan)

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

KalimantanSelataninflowtimeseries <- ts(datainflowperbulan$`Kalimantan Selatan`, frequency=12, start=c(2011,1))
KalimantanSelataninflowtimeseries
##            Jan       Feb       Mar       Apr       May       Jun       Jul
## 2011  435.9231  233.8043  499.1192  248.6236  364.1507  377.7424  420.8614
## 2012 1017.1029  608.9991  502.1998  501.0070  532.5363  436.0847  746.8760
## 2013  385.6956  152.3648  125.1875  206.8840  241.2505  135.5587  101.2874
## 2014 1193.4702  861.6307  610.4029  649.3939  577.0691  654.3690  347.4679
## 2015 1308.3016  767.8238  573.1010  654.6699  655.6017  717.3519 1168.0129
## 2016 1593.7013  811.7151  648.9879  600.8379  675.1654  460.3656 1721.9711
## 2017 1633.8799  784.1231  924.8374  796.4793 1040.5450  527.4119 2185.9746
## 2018 1833.8394  905.8473  816.1826  971.0926  918.5270 1682.4910 1645.6342
## 2019 2024.0948  940.1100  847.3525 1042.6128  854.3608 2298.3272 1287.2827
## 2020 2201.9387 1037.8126  748.4777  650.9558  518.9551 1606.9733  869.4647
## 2021 1899.2428 1204.1107 1051.9514  741.4424 1492.9044 1309.8859  993.5095
##            Aug       Sep       Oct       Nov       Dec
## 2011  268.3844 1200.0811  453.6909  557.9203  308.8282
## 2012  815.5548  819.9235  443.2733  584.2660  303.1428
## 2013  644.3126  587.7729  697.8160  591.7549  355.9105
## 2014 1923.4446  848.8139  832.9910  690.0823  424.7200
## 2015 1072.7254  637.9209  755.9631  746.8524  500.1152
## 2016  839.9792  925.0320  841.6619  762.6505  927.4038
## 2017 1053.9624 1011.0828  948.0112  865.3617  643.7222
## 2018 1099.2699 1133.8923 1016.7645  909.7523  670.4089
## 2019 1210.2265 1140.6141 1280.9129 1049.5325  486.2793
## 2020  784.0645 1152.7185  626.4177 1254.8229  300.2835
## 2021  962.0976
KalimantanSelatanoutflowtimeseries <- ts(dataoutflowperbulan$`Kalimantan Selatan`, frequency=12, start=c(2011,1))
KalimantanSelatanoutflowtimeseries
##             Jan        Feb        Mar        Apr        May        Jun
## 2011   30.92676  217.58423  331.32386  349.56174  338.01996  442.51594
## 2012   78.01497  251.83107  418.87169  320.11813  425.22442  617.59797
## 2013   53.28767  173.17133  229.16928  159.83154  564.48615  283.19700
## 2014  205.72818  297.56403  516.77520  462.29009  382.51735  459.45448
## 2015  145.86699  280.42443  375.57112  524.22672  390.50958  765.86225
## 2016  119.98024  321.23994  292.72978  483.17704  707.14356 1745.73473
## 2017  220.74825  470.94490  677.64512  803.43306  894.74852 2335.48989
## 2018  119.70985  411.30635  858.73024  569.53375 1311.73154 1745.23677
## 2019  156.83052  402.14036  735.27418 1026.22248 2307.99246  152.81024
## 2020  224.29025  440.09690  646.45724  823.77876 1415.65499  193.26948
## 2021  190.78066  225.12346  535.54389 1199.73199 1406.42748  434.94900
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011  448.01446 1334.56541   92.18066  396.73570  385.87217  758.37575
## 2012  445.67986 1000.58272  261.36103  475.96294  392.40476  892.19571
## 2013  651.41614  444.49077  318.61750  503.10060  633.13997 1032.20846
## 2014 1415.42457  330.56652  349.73945  507.07185  479.52777  858.19636
## 2015 1404.93564  267.48039  458.42324  540.49000  665.82226  935.00464
## 2016  600.40955  310.76644  397.14109  478.93165  692.11655 1274.95161
## 2017  274.67225  722.23003  431.55757  688.71465  823.43364 1200.64251
## 2018  343.85731  554.01268  415.87756  561.26903  631.11928  953.91478
## 2019  825.93749  641.39639  462.88459  710.51746  804.34719 1001.84450
## 2020  682.30480  352.21086  694.37750  939.31635  760.47623 1049.55259
## 2021  730.85906  468.65727
plot.ts(KalimantanSelataninflowtimeseries)

plot.ts(KalimantanSelatanoutflowtimeseries)

KalimantanSelatanintimeseriescomponents <- decompose(KalimantanSelataninflowtimeseries)
KalimantanSelatanintimeseriescomponents$seasonal
##             Jan        Feb        Mar        Apr        May        Jun
## 2011  635.61778  -71.33172 -215.18486 -184.96482 -195.64892   79.48896
## 2012  635.61778  -71.33172 -215.18486 -184.96482 -195.64892   79.48896
## 2013  635.61778  -71.33172 -215.18486 -184.96482 -195.64892   79.48896
## 2014  635.61778  -71.33172 -215.18486 -184.96482 -195.64892   79.48896
## 2015  635.61778  -71.33172 -215.18486 -184.96482 -195.64892   79.48896
## 2016  635.61778  -71.33172 -215.18486 -184.96482 -195.64892   79.48896
## 2017  635.61778  -71.33172 -215.18486 -184.96482 -195.64892   79.48896
## 2018  635.61778  -71.33172 -215.18486 -184.96482 -195.64892   79.48896
## 2019  635.61778  -71.33172 -215.18486 -184.96482 -195.64892   79.48896
## 2020  635.61778  -71.33172 -215.18486 -184.96482 -195.64892   79.48896
## 2021  635.61778  -71.33172 -215.18486 -184.96482 -195.64892   79.48896
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011  218.43137  130.00039   98.24674  -62.14508  -57.35231 -375.15753
## 2012  218.43137  130.00039   98.24674  -62.14508  -57.35231 -375.15753
## 2013  218.43137  130.00039   98.24674  -62.14508  -57.35231 -375.15753
## 2014  218.43137  130.00039   98.24674  -62.14508  -57.35231 -375.15753
## 2015  218.43137  130.00039   98.24674  -62.14508  -57.35231 -375.15753
## 2016  218.43137  130.00039   98.24674  -62.14508  -57.35231 -375.15753
## 2017  218.43137  130.00039   98.24674  -62.14508  -57.35231 -375.15753
## 2018  218.43137  130.00039   98.24674  -62.14508  -57.35231 -375.15753
## 2019  218.43137  130.00039   98.24674  -62.14508  -57.35231 -375.15753
## 2020  218.43137  130.00039   98.24674  -62.14508  -57.35231 -375.15753
## 2021  218.43137  130.00039
KalimantanSelatanouttimeseriescomponents <- decompose(KalimantanSelatanoutflowtimeseries)
KalimantanSelatanouttimeseriescomponents$seasonal
##              Jan         Feb         Mar         Apr         May         Jun
## 2011 -468.949094 -290.659144  -77.279644  -35.758681  318.594760  304.250690
## 2012 -468.949094 -290.659144  -77.279644  -35.758681  318.594760  304.250690
## 2013 -468.949094 -290.659144  -77.279644  -35.758681  318.594760  304.250690
## 2014 -468.949094 -290.659144  -77.279644  -35.758681  318.594760  304.250690
## 2015 -468.949094 -290.659144  -77.279644  -35.758681  318.594760  304.250690
## 2016 -468.949094 -290.659144  -77.279644  -35.758681  318.594760  304.250690
## 2017 -468.949094 -290.659144  -77.279644  -35.758681  318.594760  304.250690
## 2018 -468.949094 -290.659144  -77.279644  -35.758681  318.594760  304.250690
## 2019 -468.949094 -290.659144  -77.279644  -35.758681  318.594760  304.250690
## 2020 -468.949094 -290.659144  -77.279644  -35.758681  318.594760  304.250690
## 2021 -468.949094 -290.659144  -77.279644  -35.758681  318.594760  304.250690
##              Jul         Aug         Sep         Oct         Nov         Dec
## 2011  108.326682   -5.805776 -214.302308  -26.700598   11.920287  376.362827
## 2012  108.326682   -5.805776 -214.302308  -26.700598   11.920287  376.362827
## 2013  108.326682   -5.805776 -214.302308  -26.700598   11.920287  376.362827
## 2014  108.326682   -5.805776 -214.302308  -26.700598   11.920287  376.362827
## 2015  108.326682   -5.805776 -214.302308  -26.700598   11.920287  376.362827
## 2016  108.326682   -5.805776 -214.302308  -26.700598   11.920287  376.362827
## 2017  108.326682   -5.805776 -214.302308  -26.700598   11.920287  376.362827
## 2018  108.326682   -5.805776 -214.302308  -26.700598   11.920287  376.362827
## 2019  108.326682   -5.805776 -214.302308  -26.700598   11.920287  376.362827
## 2020  108.326682   -5.805776 -214.302308  -26.700598   11.920287  376.362827
## 2021  108.326682   -5.805776
plot(KalimantanSelatanintimeseriescomponents$seasonal,type = "l", col = "steelblue")
lines(KalimantanSelatanouttimeseriescomponents$seasonal,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))

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

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

plot(KalimantanSelatanintimeseriescomponents$figure ,type = "l", col = "green")
lines(KalimantanSelatanouttimeseriescomponents$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