Visualisasi Data Inflow dan Outflow Uang Kartal di Maluku
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 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 setiap periode
plot(datainflow$Keterangan,datainflow$`Maluku`,type = "l", col= "steelblue")
2.Visualisasi Prediksi Data outflow Uang Kartal Maluku setiap periode
plot(dataoutflow$Keterangan,dataoutflow$`Maluku`,type = "l", col= "red")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Maluku Setiap Periode
plot(datainflow$Keterangan,datainflow$`Maluku`,type = "l", col= "steelblue")
lines(dataoutflow$Keterangan,dataoutflow$`Maluku`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Maluku 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$`Maluku`, type = "l", col = "green")
lines(dataoutflowperbulan$`Maluku`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))
Malukutimeseries <- datainflowperbulan$`Maluku`
plot.ts(Malukutimeseries , type = "l", col = "green")
logMaluku <- log(datainflowperbulan$`Maluku`)
plot.ts(logMaluku)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Maluku
Malukuinflowtimeseries <- ts(datainflowperbulan$`Maluku`, frequency=12, start=c(2011,1))
Malukuinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 173.46915 70.63217 94.57877 97.52975 84.13177 130.97905
## 2012 315.72399 151.66574 113.00384 72.19651 56.04100 54.38012
## 2013 695.27374 348.06096 325.55503 450.60594 400.16117 421.20997
## 2014 423.83645 200.94569 96.22735 125.18203 94.98721 109.19298
## 2015 499.54972 229.16958 155.42673 93.44224 114.99787 77.48475
## 2016 473.54889 225.61107 187.11682 125.33421 141.47431 150.83220
## 2017 571.86280 168.63121 199.78422 144.11651 165.87234 90.57580
## 2018 726.88094 357.50953 224.88505 205.12539 211.56560 425.48352
## 2019 937.78961 296.48541 252.74304 163.88780 302.55014 601.76651
## 2020 851.06124 396.17545 289.23273 178.95458 151.26336 235.72380
## 2021 978.47905 422.95995 213.65558 199.06807 441.23702 208.70063
## Jul Aug Sep Oct Nov Dec
## 2011 99.35938 90.13565 243.06215 70.70926 62.60944 55.56591
## 2012 141.04389 86.41643 52.27125 27.44608 51.54123 25.32715
## 2013 395.26477 1113.56465 73.85404 38.67432 50.79921 27.88505
## 2014 50.57730 320.34903 150.79841 72.47475 78.08260 58.34591
## 2015 309.01228 104.96799 64.31078 42.91157 63.08052 35.16150
## 2016 410.73268 133.94504 167.40458 82.57896 100.58709 167.98696
## 2017 305.01296 261.47775 194.90558 173.82550 99.32666 108.88923
## 2018 314.94567 211.63632 180.39173 158.12325 125.53073 67.81591
## 2019 352.19157 286.45405 256.56616 223.26937 227.60450 154.83102
## 2020 143.54599 159.62922 179.49377 142.80347 149.72071 31.76341
## 2021 147.89048 182.65354Malukuoutflowtimeseries <- ts(dataoutflowperbulan$`Maluku`, frequency=12, start=c(2011,1))
Malukuoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 4.336825 47.198505 99.733621 171.306380 195.212975 129.584818
## 2012 7.141083 102.910498 194.320186 214.131311 205.778895 281.382638
## 2013 84.152453 247.206655 296.327795 220.634582 404.654339 484.381607
## 2014 4.616670 41.720219 114.725590 194.215507 188.719909 212.123576
## 2015 8.195562 66.885442 121.201143 126.778596 209.000120 250.806017
## 2016 23.758867 78.699690 35.853997 164.481528 214.735765 593.487422
## 2017 10.870840 111.368306 76.024964 169.205914 137.783105 846.269255
## 2018 32.603608 19.121820 74.942050 210.588667 316.683995 881.570932
## 2019 7.553912 129.788225 96.193375 347.227782 870.700565 10.146299
## 2020 18.372503 90.760656 247.182767 252.684506 599.806100 152.497996
## 2021 3.739239 36.003264 121.725942 345.788540 656.278267 253.329150
## Jul Aug Sep Oct Nov Dec
## 2011 227.799959 379.645791 71.801200 176.344615 178.695202 670.725459
## 2012 287.513947 214.945774 139.027165 302.795906 216.112085 523.662477
## 2013 1330.374506 325.410470 196.736296 279.869375 221.887658 703.131711
## 2014 483.830955 116.386131 164.453666 226.139847 261.001668 853.049618
## 2015 559.026798 139.627927 233.337438 329.470763 285.284358 793.693604
## 2016 297.320323 163.235820 303.420592 258.474148 299.973745 875.681588
## 2017 114.472734 310.233788 195.472688 194.643194 317.116812 1187.691182
## 2018 73.764495 258.987644 189.101140 214.265063 270.239583 882.156572
## 2019 359.262239 280.167477 192.305913 252.011053 317.438023 1208.502131
## 2020 197.880026 304.072441 174.338165 261.762021 400.893364 1023.324627
## 2021 191.034978 197.748747plot.ts(Malukuinflowtimeseries)
plot.ts(Malukuoutflowtimeseries)
Malukuintimeseriescomponents <- decompose(Malukuinflowtimeseries)
Malukuintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 419.22564 50.95875 -18.71564 -50.43618 -42.27682 16.06749
## 2012 419.22564 50.95875 -18.71564 -50.43618 -42.27682 16.06749
## 2013 419.22564 50.95875 -18.71564 -50.43618 -42.27682 16.06749
## 2014 419.22564 50.95875 -18.71564 -50.43618 -42.27682 16.06749
## 2015 419.22564 50.95875 -18.71564 -50.43618 -42.27682 16.06749
## 2016 419.22564 50.95875 -18.71564 -50.43618 -42.27682 16.06749
## 2017 419.22564 50.95875 -18.71564 -50.43618 -42.27682 16.06749
## 2018 419.22564 50.95875 -18.71564 -50.43618 -42.27682 16.06749
## 2019 419.22564 50.95875 -18.71564 -50.43618 -42.27682 16.06749
## 2020 419.22564 50.95875 -18.71564 -50.43618 -42.27682 16.06749
## 2021 419.22564 50.95875 -18.71564 -50.43618 -42.27682 16.06749
## Jul Aug Sep Oct Nov Dec
## 2011 35.94814 55.81487 -66.70109 -120.64451 -124.94891 -154.29175
## 2012 35.94814 55.81487 -66.70109 -120.64451 -124.94891 -154.29175
## 2013 35.94814 55.81487 -66.70109 -120.64451 -124.94891 -154.29175
## 2014 35.94814 55.81487 -66.70109 -120.64451 -124.94891 -154.29175
## 2015 35.94814 55.81487 -66.70109 -120.64451 -124.94891 -154.29175
## 2016 35.94814 55.81487 -66.70109 -120.64451 -124.94891 -154.29175
## 2017 35.94814 55.81487 -66.70109 -120.64451 -124.94891 -154.29175
## 2018 35.94814 55.81487 -66.70109 -120.64451 -124.94891 -154.29175
## 2019 35.94814 55.81487 -66.70109 -120.64451 -124.94891 -154.29175
## 2020 35.94814 55.81487 -66.70109 -120.64451 -124.94891 -154.29175
## 2021 35.94814 55.81487Malukuouttimeseriescomponents <- decompose(Malukuoutflowtimeseries)
Malukuouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -270.34323 -197.08614 -147.67431 -77.08041 60.15260 120.24738
## 2012 -270.34323 -197.08614 -147.67431 -77.08041 60.15260 120.24738
## 2013 -270.34323 -197.08614 -147.67431 -77.08041 60.15260 120.24738
## 2014 -270.34323 -197.08614 -147.67431 -77.08041 60.15260 120.24738
## 2015 -270.34323 -197.08614 -147.67431 -77.08041 60.15260 120.24738
## 2016 -270.34323 -197.08614 -147.67431 -77.08041 60.15260 120.24738
## 2017 -270.34323 -197.08614 -147.67431 -77.08041 60.15260 120.24738
## 2018 -270.34323 -197.08614 -147.67431 -77.08041 60.15260 120.24738
## 2019 -270.34323 -197.08614 -147.67431 -77.08041 60.15260 120.24738
## 2020 -270.34323 -197.08614 -147.67431 -77.08041 60.15260 120.24738
## 2021 -270.34323 -197.08614 -147.67431 -77.08041 60.15260 120.24738
## Jul Aug Sep Oct Nov Dec
## 2011 108.94262 -34.86151 -98.17840 -35.41887 -10.78034 582.08060
## 2012 108.94262 -34.86151 -98.17840 -35.41887 -10.78034 582.08060
## 2013 108.94262 -34.86151 -98.17840 -35.41887 -10.78034 582.08060
## 2014 108.94262 -34.86151 -98.17840 -35.41887 -10.78034 582.08060
## 2015 108.94262 -34.86151 -98.17840 -35.41887 -10.78034 582.08060
## 2016 108.94262 -34.86151 -98.17840 -35.41887 -10.78034 582.08060
## 2017 108.94262 -34.86151 -98.17840 -35.41887 -10.78034 582.08060
## 2018 108.94262 -34.86151 -98.17840 -35.41887 -10.78034 582.08060
## 2019 108.94262 -34.86151 -98.17840 -35.41887 -10.78034 582.08060
## 2020 108.94262 -34.86151 -98.17840 -35.41887 -10.78034 582.08060
## 2021 108.94262 -34.86151plot(Malukuintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(Malukuouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Malukuintimeseriescomponents$trend,type = "l", col = "orange")
lines(Malukuouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Malukuintimeseriescomponents$random ,type = "l", col = "orange")
lines(Malukuouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Malukuintimeseriescomponents$figure ,type = "l", col = "orange")
lines(Malukuouttimeseriescomponents$figure,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","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