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 setiap periode

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

2. Visualisasi Prediksi Data outflow Uang Kartal Kalimantan setiap periode

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

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

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

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

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

logKalimantan <- log(datainflowperbulan$Kalimantan)
plot.ts(logKalimantan)

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

Kalimantaninflowtimeseries <- ts(datainflowperbulan$Kalimantan, frequency=12, start=c(2011,1))
Kalimantaninflowtimeseries
##             Jan        Feb        Mar        Apr        May        Jun
## 2011   982.0676   486.4215  1150.0744   612.1185   886.9024   849.9228
## 2012  2584.7855  1591.0769  1190.8039  1257.5387  1222.9938   980.4479
## 2013  5930.9533  3034.3545  2824.3131  3078.7353  2700.6872  2416.3717
## 2014  3758.4997  2271.3511  1704.3943  1851.5859  1490.9773  1782.9483
## 2015  4922.8092  2224.6906  2207.6379  1883.6853  1769.5275  1968.0280
## 2016  4644.7033  2824.5500  2371.9574  1868.7632  2227.6024  1436.9881
## 2017  4565.2125  2648.0854  2567.2427  2306.1718  2651.5982  1220.9444
## 2018  5424.8078  2599.8996  2494.4800  2776.3594  2533.9331  6739.4262
## 2019  6198.7438  3050.6054  2926.8587  3287.4523  2304.5627  8725.8993
## 2020  6471.4469  3406.5754  2314.9900  2269.9825  2511.8455  5253.3087
## 2021  7298.6883  3575.8581  3272.7532  2543.6912  5590.1432  3638.7721
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011   853.1505   647.7339  3684.7031  1075.5043  1335.1661   708.2715
## 2012  1521.9471  2413.1364  1882.6851   921.9712  1317.3116   689.9743
## 2013  2265.4579 10364.2659  1427.0732  1554.3614  1339.1711   761.9364
## 2014   790.2439  5723.7791  2130.0210  2181.2845  1653.7917  1040.5228
## 2015  4639.2567  2697.4141  2013.4138  2009.1588  1718.8270  1372.4382
## 2016  6049.1492  2581.9038  2509.4511  2106.4707  2097.8652  2127.3564
## 2017  6655.7608  2888.5383  2869.5967  2628.8515  2370.9517  1746.4700
## 2018  4501.8949  2963.4548  3176.0797  3202.8480  2680.2176  2063.6640
## 2019  3904.5939  3686.1159  3535.9565  3637.1874  3117.5200  1782.6214
## 2020  2852.9725  2838.4324  3109.9978  1908.7549  3112.1009  1149.4244
## 2021  2637.2677  2814.6656
Kalimantanoutflowtimeseries <- ts(dataoutflowperbulan$Kalimantan, frequency=12, start=c(2011,1))
Kalimantanoutflowtimeseries
##             Jan        Feb        Mar        Apr        May        Jun
## 2011   582.2242   859.0178  1570.2218  2337.0253  2019.1669  2439.1742
## 2012   834.9211  1409.7713  2125.2689  2378.9988  2628.5642  2954.4279
## 2013  1035.3895  1808.5302  2731.7646  2153.8350  3810.3190  3437.1400
## 2014   948.7785  1379.0510  2511.3898  2746.2057  2713.3284  2831.2588
## 2015   686.6159  1783.9609  2295.2388  3361.3209  3013.7731  4325.7830
## 2016   818.7089  1925.1134  2081.6134  3036.3231  3907.9318  8764.1853
## 2017  1552.3718  2485.4757  3262.4454  3459.7874  4157.8382 10832.3521
## 2018   817.5598  2849.7438  3927.9008  3676.1021  7041.7074  9637.7045
## 2019  1235.8249  2929.3173  3734.6734  5220.7579 13438.4430   856.1754
## 2020  1568.3579  2566.7644  3898.8501  5176.8640  7565.9636  1546.9692
## 2021   618.8893  2150.0993  3163.2461  6284.5272  7612.7585  3140.4786
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011  2543.5871  6258.0677   671.3997  2174.8158  2631.5086  5448.2917
## 2012  2834.8268  5112.4519  1065.1376  3006.8743  2832.1058  6260.9022
## 2013  8871.1225  4572.3990  2069.9639  3150.6647  3557.6844  7730.3153
## 2014  8509.6390  1072.7505  2499.2401  3501.1177  2882.1789  7177.1445
## 2015  7950.5142  1569.0496  2818.2509  3058.4110  3782.5731  7299.3565
## 2016  2815.3950  2328.2286  3181.5704  2887.3519  3873.3004  6559.2052
## 2017  1396.5417  4158.7154  2746.5025  3688.0760  4964.1545  7699.4466
## 2018  2665.5558  4053.4685  3024.6391  3960.9865  4382.4883  7951.2099
## 2019  4475.0908  4230.7453  3322.1716  4185.1525  4989.9148  8960.7725
## 2020  4100.4353  3203.6999  3570.6942  5348.0481  4276.5758  9237.0826
## 2021  4112.9850  3207.9518
plot.ts(Kalimantaninflowtimeseries)

plot.ts(Kalimantanoutflowtimeseries)

Kalimantanintimeseriescomponents <- decompose(Kalimantaninflowtimeseries)
Kalimantanintimeseriescomponents$seasonal
##             Jan        Feb        Mar        Apr        May        Jun
## 2011  2355.5005  -118.3226  -498.2221  -501.9084  -643.6089   580.6372
## 2012  2355.5005  -118.3226  -498.2221  -501.9084  -643.6089   580.6372
## 2013  2355.5005  -118.3226  -498.2221  -501.9084  -643.6089   580.6372
## 2014  2355.5005  -118.3226  -498.2221  -501.9084  -643.6089   580.6372
## 2015  2355.5005  -118.3226  -498.2221  -501.9084  -643.6089   580.6372
## 2016  2355.5005  -118.3226  -498.2221  -501.9084  -643.6089   580.6372
## 2017  2355.5005  -118.3226  -498.2221  -501.9084  -643.6089   580.6372
## 2018  2355.5005  -118.3226  -498.2221  -501.9084  -643.6089   580.6372
## 2019  2355.5005  -118.3226  -498.2221  -501.9084  -643.6089   580.6372
## 2020  2355.5005  -118.3226  -498.2221  -501.9084  -643.6089   580.6372
## 2021  2355.5005  -118.3226  -498.2221  -501.9084  -643.6089   580.6372
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011   734.5961   972.4389   -95.8579  -624.0092  -700.0012 -1461.2426
## 2012   734.5961   972.4389   -95.8579  -624.0092  -700.0012 -1461.2426
## 2013   734.5961   972.4389   -95.8579  -624.0092  -700.0012 -1461.2426
## 2014   734.5961   972.4389   -95.8579  -624.0092  -700.0012 -1461.2426
## 2015   734.5961   972.4389   -95.8579  -624.0092  -700.0012 -1461.2426
## 2016   734.5961   972.4389   -95.8579  -624.0092  -700.0012 -1461.2426
## 2017   734.5961   972.4389   -95.8579  -624.0092  -700.0012 -1461.2426
## 2018   734.5961   972.4389   -95.8579  -624.0092  -700.0012 -1461.2426
## 2019   734.5961   972.4389   -95.8579  -624.0092  -700.0012 -1461.2426
## 2020   734.5961   972.4389   -95.8579  -624.0092  -700.0012 -1461.2426
## 2021   734.5961   972.4389
Kalimantanouttimeseriescomponents <- decompose(Kalimantanoutflowtimeseries)
Kalimantanouttimeseriescomponents$seasonal
##              Jan         Feb         Mar         Apr         May         Jun
## 2011 -2825.07684 -1701.86621  -813.75865  -326.19998  1547.90126  1179.20313
## 2012 -2825.07684 -1701.86621  -813.75865  -326.19998  1547.90126  1179.20313
## 2013 -2825.07684 -1701.86621  -813.75865  -326.19998  1547.90126  1179.20313
## 2014 -2825.07684 -1701.86621  -813.75865  -326.19998  1547.90126  1179.20313
## 2015 -2825.07684 -1701.86621  -813.75865  -326.19998  1547.90126  1179.20313
## 2016 -2825.07684 -1701.86621  -813.75865  -326.19998  1547.90126  1179.20313
## 2017 -2825.07684 -1701.86621  -813.75865  -326.19998  1547.90126  1179.20313
## 2018 -2825.07684 -1701.86621  -813.75865  -326.19998  1547.90126  1179.20313
## 2019 -2825.07684 -1701.86621  -813.75865  -326.19998  1547.90126  1179.20313
## 2020 -2825.07684 -1701.86621  -813.75865  -326.19998  1547.90126  1179.20313
## 2021 -2825.07684 -1701.86621  -813.75865  -326.19998  1547.90126  1179.20313
##              Jul         Aug         Sep         Oct         Nov         Dec
## 2011   895.53166   -70.31379 -1241.33156  -265.22422    16.11986  3605.01535
## 2012   895.53166   -70.31379 -1241.33156  -265.22422    16.11986  3605.01535
## 2013   895.53166   -70.31379 -1241.33156  -265.22422    16.11986  3605.01535
## 2014   895.53166   -70.31379 -1241.33156  -265.22422    16.11986  3605.01535
## 2015   895.53166   -70.31379 -1241.33156  -265.22422    16.11986  3605.01535
## 2016   895.53166   -70.31379 -1241.33156  -265.22422    16.11986  3605.01535
## 2017   895.53166   -70.31379 -1241.33156  -265.22422    16.11986  3605.01535
## 2018   895.53166   -70.31379 -1241.33156  -265.22422    16.11986  3605.01535
## 2019   895.53166   -70.31379 -1241.33156  -265.22422    16.11986  3605.01535
## 2020   895.53166   -70.31379 -1241.33156  -265.22422    16.11986  3605.01535
## 2021   895.53166   -70.31379
plot(Kalimantanintimeseriescomponents$seasonal,type = "l", col = "steelblue")
lines(Kalimantanouttimeseriescomponents$seasonal,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))

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

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

plot(Kalimantanintimeseriescomponents$figure ,type = "l", col = "green")
lines(Kalimantanouttimeseriescomponents$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