Universitas : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Jurusan : Teknik Informatika

Inflow - Outflow

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.

contoh penerapan visualisasi prediksi data Inflow-Outflow Uang Kartal di Sumatera menggunakan bahasa pemrograman R.

library(readxl)
## Warning: package 'readxl' was built under R version 4.1.2
datainflow <- read_excel(path = "inflowTahunan.xlsx")
datainflow
## # A tibble: 11 x 12
##    Tahun Sumatera   Aceh `Sumatera Utara` `Sumatera Barat`   Riau `Kep. Riau`
##    <dbl>    <dbl>  <dbl>            <dbl>            <dbl>  <dbl>       <dbl>
##  1  2011   57900.  2308.           23238.            9385.  3012.       1426.
##  2  2012   65911.  2620.           25981.           11192.  4447.       2236.
##  3  2013   98369. 36337.           18120.           14056.  8933.       3378.
##  4  2014   86024.  4567.           30503.           14103.  6358.       2563.
##  5  2015   86549.  4710.           30254.           13309.  7156.       3218.
##  6  2016   97764.  5775.           34427.           14078.  8211.       4317.
##  7  2017  103748.  5514.           35617.           15312.  8553.       4412.
##  8  2018  117495.  5799.           41769.           15058. 10730.       5134.
##  9  2019  133762.  7509.           47112.           14750. 10915.       6077.
## 10  2020  109345.  6641.           36609.           10696.  9148.       6175.
## 11  2021   89270.  3702.           31840.           10748.  7769.       5009.
## # ... with 5 more variables: Jambi <dbl>, Sumatera Selatan <dbl>,
## #   Bengkulu <dbl>, Lampung <dbl>, Kep. Bangka Bellitung <dbl>
library (readxl)
dataoutflow <- read_excel(path = "outflowTahunan.xlsx")
dataoutflow
## # A tibble: 11 x 12
##    Tahun Sumatera   Aceh `Sumatera Utara` `Sumatera Barat`   Riau `Kep. Riau`
##    <dbl>    <dbl>  <dbl>            <dbl>            <dbl>  <dbl>       <dbl>
##  1  2011   80092.  6338.           22176.            5300. 12434.       5819.
##  2  2012   85235.  6378.           22495.            6434. 13014.       6966.
##  3  2013  103288. 23278.           19235.            6511. 15460.       8747.
##  4  2014  102338.  8630.           26391.            7060. 15158.      10122.
##  5  2015  109186.  9637.           27877.            7471. 15789.       9803.
##  6  2016  121992. 11311.           31959.            9198. 17645.      10068.
##  7  2017  133606. 11760.           35243.           10754. 18128.      10749.
##  8  2018  135676. 11450.           36908.            8447. 17926.      12597.
##  9  2019  153484. 13087.           44051.            9465. 19277.      12644.
## 10  2020  140589. 12874.           39758.            8763. 19139.       8461.
## 11  2021   86627.  5770.           23453.            5941. 12631.       5128.
## # ... with 5 more variables: Jambi <dbl>, Sumatera Selatan <dbl>,
## #   Bengkulu <dbl>, Lampung <dbl>, Kep. Bangka Bellitung <dbl>

1.Visualisasi Prediksi Data Inflow Uang Kartal Sumatera setiap periode

plot(datainflow$Tahun,datainflow$`Sumatera`,type = "l", col= "steelblue")

2.Visualisasi Prediksi Data outflow Uang Kartal Sumatera setiap periode

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

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

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

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

library(readxl)
datainflowperbulan <- read_excel(path = "inflowbulanan.xlsx")
dataoutflowperbulan <- read_excel(path = "outflowbulanan.xlsx")
datainflowperbulan
## # A tibble: 128 x 12
##    keterangan          Sumatera  Aceh `Sumatera Utara` `Sumatera Barat`   Riau
##    <dttm>                 <dbl> <dbl>            <dbl>            <dbl>  <dbl>
##  1 2011-01-01 00:00:00    4164.  124.            2068.             545.   94.2
##  2 2011-02-01 00:00:00    3338.  115.            1826.             450.   96.4
##  3 2011-03-01 00:00:00    4878.  154.            2028.             849.  288. 
##  4 2011-04-01 00:00:00    3157.  122.            1429.             539.  160. 
##  5 2011-05-01 00:00:00    3821.  123.            1539.             692.  195. 
##  6 2011-06-01 00:00:00    3686.  151.            1637.             592.  101. 
##  7 2011-07-01 00:00:00    4370.  107.            1791.             800.  143. 
##  8 2011-08-01 00:00:00    3668.  184.            1256.             586.  134. 
##  9 2011-09-01 00:00:00   12875.  606.            4172.            2176. 1014. 
## 10 2011-10-01 00:00:00    4777.  158.            1941.             787.  341. 
## # ... with 118 more rows, and 6 more variables: Kep. Riau <dbl>, Jambi <dbl>,
## #   Sumatera Selatan <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## #   Kep. Bangka Belitung <dbl>
dataoutflowperbulan
## # A tibble: 128 x 12
##    keterangan          Sumatera  Aceh `Sumatera Utara` `Sumatera Barat`  Riau
##    <dttm>                 <dbl> <dbl>            <dbl>            <dbl> <dbl>
##  1 2011-01-01 00:00:00    3442.  350.             941.             307.  478.
##  2 2011-02-01 00:00:00    3989.  193.             990.             228.  400.
##  3 2011-03-01 00:00:00    4229.  230.            1209.             347.  621.
##  4 2011-04-01 00:00:00    6721.  529.            1653.             336. 1006.
##  5 2011-05-01 00:00:00    5787.  523.            1465.             328. 1000.
##  6 2011-06-01 00:00:00    7395.  406.            2167.             399. 1366.
##  7 2011-07-01 00:00:00    7154.  958.            1695.             449.  815.
##  8 2011-08-01 00:00:00   16043. 1046.            4104.            1376. 2729.
##  9 2011-09-01 00:00:00    1915.  124.             824.             148.  154.
## 10 2011-10-01 00:00:00    5174.  634.            1392.             299.  830.
## # ... with 118 more rows, and 6 more variables: Kep. Riau <dbl>, Jambi <dbl>,
## #   Sumatera Selatan <dbl>, Bengkulu <dbl>, Lampung <dbl>,
## #   Kep. Bangka Belitung <dbl>
plot(datainflowperbulan$`Sumatera`, type = "l", col = "green")
lines(dataoutflowperbulan$`Sumatera`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))

Sumateratimeseries <- datainflowperbulan$`Sumatera`
plot.ts(Sumateratimeseries , type = "l", col = "green")

logSumatera <- log(datainflowperbulan$`Sumatera`)
plot.ts(logSumatera)

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

Sumaterainflowtimeseries <- ts(datainflowperbulan$`Sumatera`, frequency=12, start=c(2011,1))
Sumaterainflowtimeseries
##            Jan       Feb       Mar       Apr       May       Jun       Jul
## 2011  4164.243  3337.607  4878.287  3156.548  3821.275  3686.394  4369.643
## 2012  7371.435  5443.242  5022.248  4102.575  5321.981  4064.952  5489.699
## 2013 13436.780  8035.017  7017.142  8267.947  7623.367  6961.815  7552.290
## 2014 11612.677  6964.701  5238.644  5988.730  4921.418  5591.202  3440.204
## 2015 12838.000  5173.687  5600.472  4954.255  5358.552  5936.657 15050.224
## 2016 13692.690  7760.507  6313.597  5254.795  6761.434  5066.314 20548.504
## 2017 12734.711  7752.686  7568.883  6638.224  7317.874  4071.240 21208.720
## 2018 16240.858  7668.179  7130.231  7628.992  5973.344 19402.076 14326.890
## 2019 17413.907  9281.546  8215.984  9406.456  7523.072 26667.739 11014.410
## 2020 19330.620 10365.349  7128.873  6536.998  7788.132 14946.781  8278.451
## 2021 21182.291  9983.745  8648.731  9095.626 16275.454 10211.629  6787.420
##            Aug       Sep       Oct       Nov       Dec
## 2011  3668.498 12874.594  4776.883  5669.993  3496.335
## 2012  9422.659  6813.338  4563.922  5452.494  2842.029
## 2013 19523.108  5265.619  6181.279  5347.888  3157.046
## 2014 19746.407  6305.927  6798.485  5515.775  3899.380
## 2015  8915.131  5710.106  6763.497  6087.162  4161.556
## 2016  6548.412  7498.570  6952.295  6098.330  5268.100
## 2017  8722.990  8250.898  7610.729  7122.755  4748.106
## 2018  9119.047  8886.660  8429.308  8078.990  4610.368
## 2019 10707.883  9462.332 10195.256  8492.726  5380.872
## 2020  8012.437  7559.106  5735.149  8462.623  5200.062
## 2021  7085.136
Sumateraoutflowtimeseries <- ts(dataoutflowperbulan$`Sumatera`, frequency=12, start=c(2011,1))
Sumateraoutflowtimeseries
##            Jan       Feb       Mar       Apr       May       Jun       Jul
## 2011  3441.614  3989.113  4228.628  6721.276  5787.181  7394.536  7154.223
## 2012  3200.178  4100.054  6605.179  6665.551  7147.179  8560.319  7711.993
## 2013  2221.436  4621.158  8219.574  4613.748  8423.251  7790.216 17485.108
## 2014  4289.908  4820.657  7088.166  8015.452  7757.313  8157.185 24722.650
## 2015  2036.392  5682.352  6300.508 10051.597  7592.788 12421.852 22934.645
## 2016  2804.053  4909.740  6985.628  8649.278 10859.812 28813.953  6455.632
## 2017  4855.706  6495.905  9234.822  9234.883 11638.176 29889.710  3252.637
## 2018  2424.451  7487.879 10455.312  9952.146 19165.027 25439.136  6324.910
## 2019  3735.569  7719.811 11089.472 15127.060 37664.505  2465.417 10575.813
## 2020  4693.754  6958.705 12667.832 11775.906 19644.928  3971.849 12710.177
## 2021  1990.678  6099.024  9638.351 19930.265 22004.413  7748.386 11650.666
##            Aug       Sep       Oct       Nov       Dec
## 2011 16042.967  1914.778  5173.616  5609.913 12634.335
## 2012 13610.489  3180.756  6273.015  5018.851 13161.070
## 2013 10207.967  6806.163  8014.259  8355.285 16529.555
## 2014  2377.454  6171.922  7655.389  7005.319 14276.798
## 2015  4668.410  6733.060  5783.016  8056.207 16924.980
## 2016  7937.744 10071.108  7571.519  9563.416 17369.845
## 2017 11015.435  6693.301  8559.331 12083.466 20652.339
## 2018 10042.081  7060.453  8155.825  9944.104 19225.025
## 2019 11780.050  8535.685  8868.257 12462.606 23460.023
## 2020  9744.450  9247.241 14431.727  9435.013 25307.224
## 2021  7565.509
plot.ts(Sumaterainflowtimeseries)

plot.ts(Sumateraoutflowtimeseries)

Sumateraintimeseriescomponents <- decompose(Sumaterainflowtimeseries)
Sumateraintimeseriescomponents$seasonal
##             Jan        Feb        Mar        Apr        May        Jun
## 2011  6152.5962  -614.2449 -1728.6408 -1759.2576 -1797.7115  1972.5387
## 2012  6152.5962  -614.2449 -1728.6408 -1759.2576 -1797.7115  1972.5387
## 2013  6152.5962  -614.2449 -1728.6408 -1759.2576 -1797.7115  1972.5387
## 2014  6152.5962  -614.2449 -1728.6408 -1759.2576 -1797.7115  1972.5387
## 2015  6152.5962  -614.2449 -1728.6408 -1759.2576 -1797.7115  1972.5387
## 2016  6152.5962  -614.2449 -1728.6408 -1759.2576 -1797.7115  1972.5387
## 2017  6152.5962  -614.2449 -1728.6408 -1759.2576 -1797.7115  1972.5387
## 2018  6152.5962  -614.2449 -1728.6408 -1759.2576 -1797.7115  1972.5387
## 2019  6152.5962  -614.2449 -1728.6408 -1759.2576 -1797.7115  1972.5387
## 2020  6152.5962  -614.2449 -1728.6408 -1759.2576 -1797.7115  1972.5387
## 2021  6152.5962  -614.2449 -1728.6408 -1759.2576 -1797.7115  1972.5387
##             Jul        Aug        Sep        Oct        Nov        Dec
## 2011  3070.5444  2282.6974  -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2012  3070.5444  2282.6974  -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2013  3070.5444  2282.6974  -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2014  3070.5444  2282.6974  -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2015  3070.5444  2282.6974  -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2016  3070.5444  2282.6974  -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2017  3070.5444  2282.6974  -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2018  3070.5444  2282.6974  -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2019  3070.5444  2282.6974  -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2020  3070.5444  2282.6974  -336.6473 -1439.1383 -1683.5836 -4119.1528
## 2021  3070.5444  2282.6974
Sumateraouttimeseriescomponents <- decompose(Sumateraoutflowtimeseries)
Sumateraouttimeseriescomponents$seasonal
##               Jan          Feb          Mar          Apr          May
## 2011 -6834.998330 -4154.095027 -1069.366771  -541.827422  4487.308681
## 2012 -6834.998330 -4154.095027 -1069.366771  -541.827422  4487.308681
## 2013 -6834.998330 -4154.095027 -1069.366771  -541.827422  4487.308681
## 2014 -6834.998330 -4154.095027 -1069.366771  -541.827422  4487.308681
## 2015 -6834.998330 -4154.095027 -1069.366771  -541.827422  4487.308681
## 2016 -6834.998330 -4154.095027 -1069.366771  -541.827422  4487.308681
## 2017 -6834.998330 -4154.095027 -1069.366771  -541.827422  4487.308681
## 2018 -6834.998330 -4154.095027 -1069.366771  -541.827422  4487.308681
## 2019 -6834.998330 -4154.095027 -1069.366771  -541.827422  4487.308681
## 2020 -6834.998330 -4154.095027 -1069.366771  -541.827422  4487.308681
## 2021 -6834.998330 -4154.095027 -1069.366771  -541.827422  4487.308681
##               Jun          Jul          Aug          Sep          Oct
## 2011  4146.113221  2196.088359     3.268476 -3129.321269 -1799.750549
## 2012  4146.113221  2196.088359     3.268476 -3129.321269 -1799.750549
## 2013  4146.113221  2196.088359     3.268476 -3129.321269 -1799.750549
## 2014  4146.113221  2196.088359     3.268476 -3129.321269 -1799.750549
## 2015  4146.113221  2196.088359     3.268476 -3129.321269 -1799.750549
## 2016  4146.113221  2196.088359     3.268476 -3129.321269 -1799.750549
## 2017  4146.113221  2196.088359     3.268476 -3129.321269 -1799.750549
## 2018  4146.113221  2196.088359     3.268476 -3129.321269 -1799.750549
## 2019  4146.113221  2196.088359     3.268476 -3129.321269 -1799.750549
## 2020  4146.113221  2196.088359     3.268476 -3129.321269 -1799.750549
## 2021  4146.113221  2196.088359     3.268476                          
##               Nov          Dec
## 2011 -1217.537318  7914.117948
## 2012 -1217.537318  7914.117948
## 2013 -1217.537318  7914.117948
## 2014 -1217.537318  7914.117948
## 2015 -1217.537318  7914.117948
## 2016 -1217.537318  7914.117948
## 2017 -1217.537318  7914.117948
## 2018 -1217.537318  7914.117948
## 2019 -1217.537318  7914.117948
## 2020 -1217.537318  7914.117948
## 2021
plot(Sumateraintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(Sumateraouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))

plot(Sumateraintimeseriescomponents$trend,type = "l", col = "orange")
lines(Sumateraouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))

plot(Sumateraintimeseriescomponents$random ,type = "l", col = "orange")
lines(Sumateraouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))

plot(Sumateraintimeseriescomponents$figure ,type = "l", col = "orange")
lines(Sumateraouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))