Visualisasi Data Inflow dan Outflow Uang Kartal di Maluku Utara
Nurhisam Ari Kusnaini, Dosen Pengampu : Prof. Dr. Suhartono, M.kom
2022-02-21
UIN Maulana Malik Ibrahim Malang Teknik Informatika
Pengertian Inflow dan Outflow 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 adalah contoh penerapan visualisasi prediksi data Inflow-Outflow Uang Kartal di Maluku Utara menggunakan bahasa pemrograman R.
library(readxl)datainflow <- read_excel(path = "data1.xlsx")## New names:
## * `` -> ...2datainflow## # A tibble: 12 x 13
## Keterangan ...2 Sulampua `Sulawesi Utara` `Sulawesi Tengah` `Sulawesi Sela~`
## <dbl> <lgl> <dbl> <dbl> <dbl> <dbl>
## 1 NA NA NA NA NA NA
## 2 2011 NA 25056. 5671. 1563. 10593.
## 3 2012 NA 31011. 6635. 1885. 13702.
## 4 2013 NA 63774. 21646. 1520. 17770.
## 5 2014 NA 41607. 7374. 3000. 19384.
## 6 2015 NA 40309. 6286. 2593. 19583.
## 7 2016 NA 45737. 7266. 2665. 21043.
## 8 2017 NA 44126. 7044. 2806. 18803.
## 9 2018 NA 52672. 7781. 3701. 21894.
## 10 2019 NA 60202. 7809. 4042. 24749.
## 11 2020 NA 52812. 6324. 3052. 21551.
## 12 2021 NA 45714. 4671. 2453. 18335.
## # ... with 7 more variables: `Sulawesi Tenggara` <dbl>, `Sulawesi Barat` <dbl>,
## # Gorontalo <dbl>, `Maluku Utara` <dbl>, Maluku <dbl>, Papua <dbl>,
## # `Papua Barat` <dbl>library (readxl)
dataoutflow <- read_excel(path = "data3.xlsx")
dataoutflow## # A tibble: 11 x 12
## Keterangan Sulampua `Sulawesi Utara` `Sulawesi Tengah` `Sulawesi Selatan`
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011 36449. 6606. 4017. 8967.
## 2 2012 43623. 6375. 4458. 11873.
## 3 2013 64181. 22740. 4544. 11485.
## 4 2014 48231. 7207. 5696. 15645.
## 5 2015 53153. 7202. 5310. 16236.
## 6 2016 53145. 7707. 4962. 15494.
## 7 2017 56297. 8421. 5226. 15159.
## 8 2018 60935. 7605. 5578. 16779.
## 9 2019 60723. 7367. 5531. 18089.
## 10 2020 64828. 7437. 4674. 20503.
## 11 2021 33806. 3050. 2763. 12017.
## # ... with 7 more variables: `Sulawesi Tenggara` <dbl>, `Sulawesi Barat` <dbl>,
## # Gorontalo <dbl>, `Maluku Utara` <dbl>, Maluku <dbl>, Papua <dbl>,
## # `Papua Barat` <dbl>1.Visualisasi Prediksi Data Inflow Uang Kartal Maluku Utara setiap periode
plot(datainflow$Keterangan,datainflow$`Maluku Utara`,type = "l", col= "red")
2.Visualisasi Prediksi Data outflow Uang Kartal Maluku Utara setiap periode
plot(dataoutflow$Keterangan,dataoutflow$`Maluku Utara`,type = "l", col= "green")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Maluku Utara Setiap Periode
plot(datainflow$Keterangan,datainflow$`Maluku Utara`,type = "l", col= "red")
lines(dataoutflow$Keterangan,dataoutflow$`Maluku Utara`,col="green")
legend("top",c("Inflow","Outflow"),fill=c("red","green"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Maluku Utara Setiap Bulan
library(readxl)
datainflowperbulan <- read_excel(path = "data2.xlsx")## New names:
## * `` -> ...1
## * `` -> ...2dataoutflowperbulan <- read_excel(path = "data4.xlsx")## New names:
## * `` -> ...1datainflowperbulan## # A tibble: 128 x 13
## ...1 ...2 Sulampua `Sulawesi Utara` `Sulawesi Tengah`
## <dttm> <lgl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 NA 2584. 861. 167.
## 2 2011-02-01 00:00:00 NA 1504. 353. 46.1
## 3 2011-03-01 00:00:00 NA 2032. 415. 133.
## 4 2011-04-01 00:00:00 NA 1591. 342. 91.5
## 5 2011-05-01 00:00:00 NA 1704. 379. 106.
## 6 2011-06-01 00:00:00 NA 1795. 413. 77.0
## 7 2011-07-01 00:00:00 NA 1863. 480. 113.
## 8 2011-08-01 00:00:00 NA 1606. 415. 76.9
## 9 2011-09-01 00:00:00 NA 4967. 886. 446.
## 10 2011-10-01 00:00:00 NA 1918. 423. 113.
## # ... with 118 more rows, and 8 more variables: `Sulawesi Selatan` <dbl>,
## # `Sulawesi Tenggara` <dbl>, `Sulawesi Barat` <dbl>, Gorontalo <dbl>,
## # `Maluku Utara` <dbl>, Maluku <dbl>, Papua <dbl>, `Papua Barat` <dbl>dataoutflowperbulan## # A tibble: 128 x 12
## ...1 Sulampua `Sulawesi Utara` `Sulawesi Tengah`
## <dttm> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 966. 244. 83.5
## 2 2011-02-01 00:00:00 957. 260. 139.
## 3 2011-03-01 00:00:00 1982. 352. 189.
## 4 2011-04-01 00:00:00 2605. 460. 266.
## 5 2011-05-01 00:00:00 2559. 474. 317.
## 6 2011-06-01 00:00:00 2557. 459. 311.
## 7 2011-07-01 00:00:00 3087. 622. 351.
## 8 2011-08-01 00:00:00 6228. 985. 656.
## 9 2011-09-01 00:00:00 1234. 212. 105.
## 10 2011-10-01 00:00:00 2947. 545. 356.
## # ... with 118 more rows, and 8 more variables: `Sulawesi Selatan` <dbl>,
## # `Sulawesi Tenggara` <dbl>, `Sulawesi Barat` <dbl>, Gorontalo <dbl>,
## # `Maluku Utara` <dbl>, Maluku <dbl>, Papua <dbl>, `Papua Barat` <dbl>plot(datainflowperbulan$`Maluku Utara`, type = "l", col = "grey")
lines(dataoutflowperbulan$`Maluku Utara`,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("grey","blue"))
MalukuUtaratimeseries <- datainflowperbulan$`Maluku Utara`
plot.ts(MalukuUtaratimeseries , type = "l", col = "green")
logMalukuUtara <- log(datainflowperbulan$`Maluku Utara`)
plot.ts(logMalukuUtara)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Maluku Utara
MalukuUtarainflowtimeseries <- ts(datainflowperbulan$`Maluku Utara`, frequency=12, start=c(2011,1))
MalukuUtarainflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 62.10373 22.50867 54.15182 38.80835 42.43800 28.92824
## 2012 113.02124 87.03536 46.36464 37.50103 60.26457 27.92469
## 2013 1392.31787 859.44247 844.62455 971.67575 1070.50061 1064.86735
## 2014 211.65994 69.47643 43.75856 83.90539 47.38005 49.84697
## 2015 219.64695 59.93098 52.40471 50.82307 54.20264 56.75826
## 2016 200.20750 90.80773 60.75087 49.48764 49.66303 87.96881
## 2017 207.85401 92.73008 96.76078 88.68023 99.30188 61.50242
## 2018 355.57064 80.24984 109.06044 52.92882 76.11618 291.78714
## 2019 451.10744 129.89358 77.82786 104.17636 88.62895 458.73546
## 2020 399.90351 163.26113 118.89817 100.50598 119.77383 243.69075
## 2021 584.30152 157.58542 106.37244 93.98828 307.33331 192.89382
## Jul Aug Sep Oct Nov Dec
## 2011 24.01728 32.47994 160.69514 46.79988 45.26823 28.01422
## 2012 41.70865 69.51879 57.23457 22.76074 45.43771 23.79136
## 2013 1045.40166 2767.83065 91.56887 78.41379 69.13370 17.06019
## 2014 38.21002 198.75658 82.13432 64.21434 77.99877 38.41842
## 2015 237.59333 91.56407 58.97259 41.65076 47.63351 35.34494
## 2016 284.56284 90.85775 111.20567 96.70454 50.19685 86.70455
## 2017 209.10802 142.19811 143.92009 89.11779 72.18040 35.17707
## 2018 177.16853 108.46879 83.52319 78.58572 68.85937 48.16373
## 2019 119.04007 134.60441 100.06981 91.45382 113.59993 54.82074
## 2020 143.56000 152.20226 148.98405 105.47128 122.38243 56.98181
## 2021 151.50785 143.81800MalukuUtaraoutflowtimeseries <- ts(dataoutflowperbulan$`Maluku Utara`, frequency=12, start=c(2011,1))
MalukuUtaraoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 67.898967 39.076739 71.505420 108.030761 120.785748 103.445786
## 2012 46.222625 58.235535 92.804047 175.019579 156.685529 135.175914
## 2013 267.193407 790.517592 780.153948 559.469282 1038.415848 982.177013
## 2014 23.686751 75.749703 110.371277 127.414509 105.005835 180.129675
## 2015 14.607574 75.218048 119.977235 128.560930 170.027322 214.587436
## 2016 3.563644 51.995252 105.361498 124.356947 262.613050 508.764644
## 2017 42.708324 102.114026 174.000256 165.856286 299.036394 535.867738
## 2018 24.060536 73.654062 173.809492 194.107934 282.576804 586.110404
## 2019 26.516420 58.100606 117.396146 364.028553 652.085703 21.268600
## 2020 56.624914 73.084380 239.982806 383.778330 347.565756 117.342705
## 2021 11.219680 69.113371 265.819418 339.474669 571.546973 167.662814
## Jul Aug Sep Oct Nov Dec
## 2011 167.978644 277.980048 10.064785 114.978744 116.617350 432.592342
## 2012 176.665284 218.116570 52.191991 143.909345 86.160987 335.963059
## 2013 2228.699586 1146.719605 111.178567 185.968130 133.891448 353.765320
## 2014 389.092920 8.238407 107.333709 166.331465 153.798976 362.061397
## 2015 507.036406 60.158876 264.422613 116.730169 254.240558 471.465423
## 2016 118.405316 111.001222 227.058198 165.622251 220.966539 346.769901
## 2017 96.466463 155.472516 101.984563 181.050285 255.755029 641.933726
## 2018 109.099487 165.544115 105.526556 175.719286 156.629912 631.165421
## 2019 314.027763 188.767597 112.501926 164.514552 260.883164 703.725061
## 2020 246.984628 121.310717 205.186934 257.006808 235.616209 658.584330
## 2021 242.802497 155.369764plot.ts(MalukuUtarainflowtimeseries)
plot.ts(MalukuUtaraoutflowtimeseries)
MalukuUtaraintimeseriescomponents <- decompose(MalukuUtarainflowtimeseries)
MalukuUtaraintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 223.212890 -12.299989 -31.652553 -21.955109 -8.567358 66.191532
## 2012 223.212890 -12.299989 -31.652553 -21.955109 -8.567358 66.191532
## 2013 223.212890 -12.299989 -31.652553 -21.955109 -8.567358 66.191532
## 2014 223.212890 -12.299989 -31.652553 -21.955109 -8.567358 66.191532
## 2015 223.212890 -12.299989 -31.652553 -21.955109 -8.567358 66.191532
## 2016 223.212890 -12.299989 -31.652553 -21.955109 -8.567358 66.191532
## 2017 223.212890 -12.299989 -31.652553 -21.955109 -8.567358 66.191532
## 2018 223.212890 -12.299989 -31.652553 -21.955109 -8.567358 66.191532
## 2019 223.212890 -12.299989 -31.652553 -21.955109 -8.567358 66.191532
## 2020 223.212890 -12.299989 -31.652553 -21.955109 -8.567358 66.191532
## 2021 223.212890 -12.299989 -31.652553 -21.955109 -8.567358 66.191532
## Jul Aug Sep Oct Nov Dec
## 2011 49.992388 194.064839 -81.732872 -114.493940 -116.075762 -146.684067
## 2012 49.992388 194.064839 -81.732872 -114.493940 -116.075762 -146.684067
## 2013 49.992388 194.064839 -81.732872 -114.493940 -116.075762 -146.684067
## 2014 49.992388 194.064839 -81.732872 -114.493940 -116.075762 -146.684067
## 2015 49.992388 194.064839 -81.732872 -114.493940 -116.075762 -146.684067
## 2016 49.992388 194.064839 -81.732872 -114.493940 -116.075762 -146.684067
## 2017 49.992388 194.064839 -81.732872 -114.493940 -116.075762 -146.684067
## 2018 49.992388 194.064839 -81.732872 -114.493940 -116.075762 -146.684067
## 2019 49.992388 194.064839 -81.732872 -114.493940 -116.075762 -146.684067
## 2020 49.992388 194.064839 -81.732872 -114.493940 -116.075762 -146.684067
## 2021 49.992388 194.064839MalukuUtaraouttimeseriescomponents <- decompose(MalukuUtaraoutflowtimeseries)
MalukuUtaraouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -204.506239 -113.169259 -42.548857 -9.805777 110.254642 105.036560
## 2012 -204.506239 -113.169259 -42.548857 -9.805777 110.254642 105.036560
## 2013 -204.506239 -113.169259 -42.548857 -9.805777 110.254642 105.036560
## 2014 -204.506239 -113.169259 -42.548857 -9.805777 110.254642 105.036560
## 2015 -204.506239 -113.169259 -42.548857 -9.805777 110.254642 105.036560
## 2016 -204.506239 -113.169259 -42.548857 -9.805777 110.254642 105.036560
## 2017 -204.506239 -113.169259 -42.548857 -9.805777 110.254642 105.036560
## 2018 -204.506239 -113.169259 -42.548857 -9.805777 110.254642 105.036560
## 2019 -204.506239 -113.169259 -42.548857 -9.805777 110.254642 105.036560
## 2020 -204.506239 -113.169259 -42.548857 -9.805777 110.254642 105.036560
## 2021 -204.506239 -113.169259 -42.548857 -9.805777 110.254642 105.036560
## Jul Aug Sep Oct Nov Dec
## 2011 187.464398 -2.539273 -119.060050 -83.395922 -65.965530 238.235308
## 2012 187.464398 -2.539273 -119.060050 -83.395922 -65.965530 238.235308
## 2013 187.464398 -2.539273 -119.060050 -83.395922 -65.965530 238.235308
## 2014 187.464398 -2.539273 -119.060050 -83.395922 -65.965530 238.235308
## 2015 187.464398 -2.539273 -119.060050 -83.395922 -65.965530 238.235308
## 2016 187.464398 -2.539273 -119.060050 -83.395922 -65.965530 238.235308
## 2017 187.464398 -2.539273 -119.060050 -83.395922 -65.965530 238.235308
## 2018 187.464398 -2.539273 -119.060050 -83.395922 -65.965530 238.235308
## 2019 187.464398 -2.539273 -119.060050 -83.395922 -65.965530 238.235308
## 2020 187.464398 -2.539273 -119.060050 -83.395922 -65.965530 238.235308
## 2021 187.464398 -2.539273plot(MalukuUtaraintimeseriescomponents$seasonal,type = "l", col = "yellow")
lines(MalukuUtaraouttimeseriescomponents$seasonal,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(MalukuUtaraintimeseriescomponents$trend,type = "l", col = "yellow")
lines(MalukuUtaraouttimeseriescomponents$trend,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(MalukuUtaraintimeseriescomponents$random ,type = "l", col = "yellow")
lines(MalukuUtaraouttimeseriescomponents$random,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(MalukuUtaraintimeseriescomponents$figure ,type = "l", col = "yellow")
lines(MalukuUtaraouttimeseriescomponents$figure,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
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