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 Nusa Tenggara Timur setiap periode

plot(datainflow$Keterangan,datainflow$`Nusa Tenggar Timur`,type = "l", col= "steelblue")
## Warning: Unknown or uninitialised column: `Nusa Tenggar Timur`.

2. Visualisasi Prediksi Data outflow Uang Kartal Nusa Tenggara Timur setiap periode

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

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

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

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

NusaTenggaraTimurtimeseries <- datainflowperbulan$`Nusa Tenggara Timur`
plot.ts(NusaTenggaraTimurtimeseries , type = "l", col = "steelblue")

logNusaTenggaraTimur <- log(datainflowperbulan$`Nusa Tenggara Timur`)
plot.ts(logNusaTenggaraTimur)

5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Nusa Tenggara TImur

NusaTenggaraTimurinflowtimeseries <- ts(datainflowperbulan$`Nusa Tenggara Timur`, frequency=12, start=c(2011,1))
NusaTenggaraTimurinflowtimeseries
##             Jan        Feb        Mar        Apr        May        Jun
## 2011  354.26063  107.55653  211.94511  153.74315  166.54539  134.87637
## 2012  520.21320  400.40009  210.34596  188.73924  154.61893   96.60459
## 2013  801.40881  468.14313  370.60575  319.01870  440.42204  379.88675
## 2014  764.94069  352.59317  254.29539  317.80736  211.53872  208.86926
## 2015 1003.57525  462.49939  337.90267  165.65073  164.58269  161.86011
## 2016  938.19332  517.69098  376.42180  260.96682  269.40487  207.53591
## 2017 1120.63101  519.97228  448.63576  346.27257  308.70103  193.27340
## 2018 1703.23171  600.88294  406.47359  394.92594  321.63133  641.26316
## 2019 1591.43285  639.34846  469.80295  459.24921  409.80388  688.03614
## 2020 1753.58413  856.39178  571.16203  430.82793  224.02301  500.45272
## 2021 1911.27295  681.14882  799.98672  421.79964  543.41196  466.39242
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011  133.65718  143.06224  239.39248  168.54496  181.67542  129.89078
## 2012  220.52653  274.83472  181.93113  182.31296  189.99917  114.43033
## 2013  453.22273 1605.77412  157.53141  215.82414  123.90755   86.28982
## 2014  157.17584  359.11464  250.53633  218.97225  225.62940  190.89560
## 2015  396.62382  229.43914  203.60812  160.91339  185.58750  178.98394
## 2016  450.85509  239.97087  253.41094  207.34061  298.38504  189.80322
## 2017  459.94020  426.49634  380.90154  461.89635  444.68785  340.19532
## 2018  427.31607  335.76062  354.67206  394.08679  364.50655  171.24512
## 2019  423.59825  495.38520  559.15997  562.55908  452.12778  329.26325
## 2020  431.96778  370.51267  507.26668  274.42710  502.16352  234.46300
## 2021  370.53350  303.60562
NusaTenggaraTimuroutflowtimeseries <- ts(dataoutflowperbulan$`Nusa Tenggara Timur`, frequency=12, start=c(2011,1))
NusaTenggaraTimuroutflowtimeseries
##             Jan        Feb        Mar        Apr        May        Jun
## 2011   51.91510   87.59781  136.01796  201.56510  236.52224  273.29844
## 2012   61.53800   86.31504  138.95965  292.04098  266.13409  573.74643
## 2013  219.09854  511.19482  811.83687  658.48083 1184.00468 1245.06907
## 2014   43.76414   83.99365  194.29954  225.19582  226.74739  369.03233
## 2015   92.78045   73.75020  188.91345  322.08068  213.12940  391.00065
## 2016   51.36679  132.18025  149.51999  274.86825  391.98383 1016.82533
## 2017   82.92896   92.31245  174.88381  288.56236  457.64791 1458.41157
## 2018   95.04104  159.98378  270.43119  383.49064  685.81478 1561.66962
## 2019   52.43695  175.90384  225.37134  835.24248 1403.74156  168.86768
## 2020  125.58805  163.95628  301.79361  414.75707  992.36116  513.04723
## 2021   27.95339   70.78477  234.56960  454.31546  913.96864  659.14639
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011  470.99277  400.35784  174.68559  312.78472  305.78182 1041.59536
## 2012  429.29977  452.29231  293.65808  324.02743  318.01724 1023.57425
## 2013 3250.58324 1359.65636  363.71952  310.32495  335.76712 1274.44525
## 2014  851.35807  156.68564  335.74561  436.57324  500.99421 1243.54670
## 2015  857.88037  383.10786  416.53492  488.09038  527.26013 1575.02774
## 2016  534.49221  397.30898  411.35176  470.21969  423.45521 1398.77162
## 2017  488.35871  473.05707  512.95657  534.98492  657.83076 2346.63703
## 2018  517.10551  567.90837  412.25448  510.72320  405.03786 1985.69075
## 2019  942.50254  565.29566  201.41433  232.50391  575.14916 2359.14106
## 2020  625.02056  737.37943  647.27467  909.56877  770.29300 2154.93862
## 2021  466.15882  645.29648
plot.ts(NusaTenggaraTimurinflowtimeseries)

plot.ts(NusaTenggaraTimuroutflowtimeseries)

NusaTenggaraTimurintimeseriescomponents <- decompose(NusaTenggaraTimurinflowtimeseries)
NusaTenggaraTimurintimeseriescomponents$seasonal
##             Jan        Feb        Mar        Apr        May        Jun
## 2011  786.89249  124.29528  -26.97517  -91.17079 -135.22606  -73.52109
## 2012  786.89249  124.29528  -26.97517  -91.17079 -135.22606  -73.52109
## 2013  786.89249  124.29528  -26.97517  -91.17079 -135.22606  -73.52109
## 2014  786.89249  124.29528  -26.97517  -91.17079 -135.22606  -73.52109
## 2015  786.89249  124.29528  -26.97517  -91.17079 -135.22606  -73.52109
## 2016  786.89249  124.29528  -26.97517  -91.17079 -135.22606  -73.52109
## 2017  786.89249  124.29528  -26.97517  -91.17079 -135.22606  -73.52109
## 2018  786.89249  124.29528  -26.97517  -91.17079 -135.22606  -73.52109
## 2019  786.89249  124.29528  -26.97517  -91.17079 -135.22606  -73.52109
## 2020  786.89249  124.29528  -26.97517  -91.17079 -135.22606  -73.52109
## 2021  786.89249  124.29528  -26.97517  -91.17079 -135.22606  -73.52109
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011  -43.17575   40.49345 -103.54069 -131.26107 -121.76903 -225.04157
## 2012  -43.17575   40.49345 -103.54069 -131.26107 -121.76903 -225.04157
## 2013  -43.17575   40.49345 -103.54069 -131.26107 -121.76903 -225.04157
## 2014  -43.17575   40.49345 -103.54069 -131.26107 -121.76903 -225.04157
## 2015  -43.17575   40.49345 -103.54069 -131.26107 -121.76903 -225.04157
## 2016  -43.17575   40.49345 -103.54069 -131.26107 -121.76903 -225.04157
## 2017  -43.17575   40.49345 -103.54069 -131.26107 -121.76903 -225.04157
## 2018  -43.17575   40.49345 -103.54069 -131.26107 -121.76903 -225.04157
## 2019  -43.17575   40.49345 -103.54069 -131.26107 -121.76903 -225.04157
## 2020  -43.17575   40.49345 -103.54069 -131.26107 -121.76903 -225.04157
## 2021  -43.17575   40.49345
NusaTenggaraTimurouttimeseriescomponents <- decompose(NusaTenggaraTimuroutflowtimeseries)
NusaTenggaraTimurouttimeseriescomponents$seasonal
##             Jan        Feb        Mar        Apr        May        Jun
## 2011 -483.42723 -414.63979 -289.45441 -156.77080   74.63198  231.33878
## 2012 -483.42723 -414.63979 -289.45441 -156.77080   74.63198  231.33878
## 2013 -483.42723 -414.63979 -289.45441 -156.77080   74.63198  231.33878
## 2014 -483.42723 -414.63979 -289.45441 -156.77080   74.63198  231.33878
## 2015 -483.42723 -414.63979 -289.45441 -156.77080   74.63198  231.33878
## 2016 -483.42723 -414.63979 -289.45441 -156.77080   74.63198  231.33878
## 2017 -483.42723 -414.63979 -289.45441 -156.77080   74.63198  231.33878
## 2018 -483.42723 -414.63979 -289.45441 -156.77080   74.63198  231.33878
## 2019 -483.42723 -414.63979 -289.45441 -156.77080   74.63198  231.33878
## 2020 -483.42723 -414.63979 -289.45441 -156.77080   74.63198  231.33878
## 2021 -483.42723 -414.63979 -289.45441 -156.77080   74.63198  231.33878
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011  339.61073   -7.67380 -180.35978 -105.80297  -80.70026 1073.24754
## 2012  339.61073   -7.67380 -180.35978 -105.80297  -80.70026 1073.24754
## 2013  339.61073   -7.67380 -180.35978 -105.80297  -80.70026 1073.24754
## 2014  339.61073   -7.67380 -180.35978 -105.80297  -80.70026 1073.24754
## 2015  339.61073   -7.67380 -180.35978 -105.80297  -80.70026 1073.24754
## 2016  339.61073   -7.67380 -180.35978 -105.80297  -80.70026 1073.24754
## 2017  339.61073   -7.67380 -180.35978 -105.80297  -80.70026 1073.24754
## 2018  339.61073   -7.67380 -180.35978 -105.80297  -80.70026 1073.24754
## 2019  339.61073   -7.67380 -180.35978 -105.80297  -80.70026 1073.24754
## 2020  339.61073   -7.67380 -180.35978 -105.80297  -80.70026 1073.24754
## 2021  339.61073   -7.67380
plot(NusaTenggaraTimurintimeseriescomponents$seasonal,type = "l", col = "steelblue")
lines(NusaTenggaraTimurouttimeseriescomponents$seasonal,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))

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

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

plot(NusaTenggaraTimurintimeseriescomponents$figure ,type = "l", col = "green")
lines(NusaTenggaraTimurouttimeseriescomponents$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