Visualisasi Data Inflow dan Outflow Uang Kartal di Sulawesi 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 Sulawesi 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 Sulawesi Utara setiap periode
plot(datainflow$Keterangan,datainflow$`Sulawesi Utara`,type = "l", col= "red")
2.Visualisasi Prediksi Data outflow Uang Kartal Sulawesi Utara setiap periode
plot(dataoutflow$Keterangan,dataoutflow$`Sulawesi Utara`,type = "l", col= "green")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sulawesi Utara Setiap Periode
plot(datainflow$Keterangan,datainflow$`Sulawesi Utara`,type = "l", col= "red")
lines(dataoutflow$Keterangan,dataoutflow$`Sulawesi Utara`,col="green")
legend("top",c("Inflow","Outflow"),fill=c("red","green"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sulawesi 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$`Sulawesi Utara`, type = "l", col = "grey")
lines(dataoutflowperbulan$`Sulawesi Utara`,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("grey","blue"))
SulawesiUtaratimeseries <- datainflowperbulan$`Sulawesi Utara`
plot.ts(SulawesiUtaratimeseries , type = "l", col = "green")
logSulawesiUtara <- log(datainflowperbulan$`Sulawesi Utara`)
plot.ts(logSulawesiUtara)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Sulawesi Utara
SulawesiUtarainflowtimeseries <- ts(datainflowperbulan$`Sulawesi Utara`, frequency=12, start=c(2011,1))
SulawesiUtarainflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 860.7385 352.9041 414.9908 342.3528 379.4670 413.1758 480.2016
## 2012 1176.8558 523.6859 442.3320 376.7808 472.9361 436.3540 579.0293
## 2013 3866.5529 2045.1677 1591.0424 1538.8475 1729.1925 1664.8949 1412.6565
## 2014 1433.2824 607.4864 417.2395 601.4891 450.9658 559.0841 456.7986
## 2015 1564.4203 386.3612 372.0492 338.9239 391.7780 358.8130 1030.3345
## 2016 1649.7976 485.0502 353.9875 262.3391 473.8163 293.9829 1537.4737
## 2017 1651.3760 373.8373 375.4976 372.8282 351.9809 238.3377 1430.8788
## 2018 1926.1388 585.1342 399.4248 463.7705 539.3969 1045.9901 718.8949
## 2019 1813.8063 486.5071 417.5471 592.9503 570.9121 1551.6171 495.5567
## 2020 1772.6813 466.6830 289.3445 189.6372 298.2579 385.1628 247.9884
## 2021 1549.7558 488.7697 489.8684 310.2974 806.2857 510.1142 227.0602
## Aug Sep Oct Nov Dec
## 2011 414.8234 885.8144 422.9711 410.7011 292.8600
## 2012 837.1944 494.6133 402.0490 522.3170 370.4932
## 2013 5765.6093 437.5279 548.3990 530.6355 515.6329
## 2014 1302.7009 474.9873 420.9767 297.4887 351.9993
## 2015 444.2401 318.2717 358.9832 318.9779 403.1356
## 2016 434.2419 484.8547 394.0971 412.3486 483.6072
## 2017 557.4328 518.0903 434.3193 432.6734 307.0269
## 2018 419.3425 385.3481 450.3463 421.3295 426.0345
## 2019 385.5376 344.5586 536.7020 351.7624 261.6977
## 2020 342.4577 391.1442 826.0752 861.1173 253.6835
## 2021 289.1124SulawesiUtaraoutflowtimeseries <- ts(dataoutflowperbulan$`Sulawesi Utara`, frequency=12, start=c(2011,1))
SulawesiUtaraoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 244.217165 260.045859 351.805036 460.367592 474.222931 459.370674
## 2012 196.144420 282.586694 434.090852 527.717470 497.397112 797.291166
## 2013 585.265646 1537.569958 2605.811168 1412.572360 2964.619679 2431.821671
## 2014 16.409412 46.219356 247.753169 680.609195 553.004630 606.698769
## 2015 69.519317 271.288630 349.798232 630.368545 377.198542 395.827936
## 2016 37.312641 316.976614 340.576821 391.748992 608.551484 1463.838638
## 2017 43.052058 295.869476 424.080221 526.101370 560.182861 1859.738396
## 2018 118.041675 335.185255 731.777026 326.048400 753.635600 1448.171635
## 2019 45.741979 292.563444 496.109307 877.391274 1722.133985 137.636006
## 2020 65.143403 219.552409 333.994797 604.235532 691.227480 126.770693
## 2021 8.451208 130.471043 272.150058 584.501716 1168.091066 380.867926
## Jul Aug Sep Oct Nov Dec
## 2011 621.885006 985.119155 211.519479 545.324194 588.413447 1404.059346
## 2012 614.019626 975.948548 308.979313 365.883199 375.237139 999.750873
## 2013 5169.302578 3692.219093 329.271404 348.433562 411.462702 1251.274472
## 2014 1483.336097 326.598849 597.373574 514.414503 550.532571 1584.124973
## 2015 1474.582262 390.203861 500.558534 439.988713 709.381934 1593.502796
## 2016 692.891305 553.832599 543.825390 621.979022 609.849521 1526.020467
## 2017 238.870994 615.272440 513.858351 473.648557 949.443083 1920.808361
## 2018 233.748952 313.927111 243.826454 450.846092 565.334647 2084.264783
## 2019 597.609098 338.222218 257.648157 298.099294 559.586661 1744.697624
## 2020 425.908314 503.433905 403.172820 1254.260157 1098.606123 1710.624413
## 2021 262.606942 243.229178plot.ts(SulawesiUtarainflowtimeseries)
plot.ts(SulawesiUtaraoutflowtimeseries)
SulawesiUtaraintimeseriescomponents <- decompose(SulawesiUtarainflowtimeseries)
SulawesiUtaraintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 1130.30823 -63.71168 -201.87809 -192.66504 -136.43097 1.10959
## 2012 1130.30823 -63.71168 -201.87809 -192.66504 -136.43097 1.10959
## 2013 1130.30823 -63.71168 -201.87809 -192.66504 -136.43097 1.10959
## 2014 1130.30823 -63.71168 -201.87809 -192.66504 -136.43097 1.10959
## 2015 1130.30823 -63.71168 -201.87809 -192.66504 -136.43097 1.10959
## 2016 1130.30823 -63.71168 -201.87809 -192.66504 -136.43097 1.10959
## 2017 1130.30823 -63.71168 -201.87809 -192.66504 -136.43097 1.10959
## 2018 1130.30823 -63.71168 -201.87809 -192.66504 -136.43097 1.10959
## 2019 1130.30823 -63.71168 -201.87809 -192.66504 -136.43097 1.10959
## 2020 1130.30823 -63.71168 -201.87809 -192.66504 -136.43097 1.10959
## 2021 1130.30823 -63.71168 -201.87809 -192.66504 -136.43097 1.10959
## Jul Aug Sep Oct Nov Dec
## 2011 136.49266 384.43243 -233.28270 -227.49029 -252.69188 -344.19227
## 2012 136.49266 384.43243 -233.28270 -227.49029 -252.69188 -344.19227
## 2013 136.49266 384.43243 -233.28270 -227.49029 -252.69188 -344.19227
## 2014 136.49266 384.43243 -233.28270 -227.49029 -252.69188 -344.19227
## 2015 136.49266 384.43243 -233.28270 -227.49029 -252.69188 -344.19227
## 2016 136.49266 384.43243 -233.28270 -227.49029 -252.69188 -344.19227
## 2017 136.49266 384.43243 -233.28270 -227.49029 -252.69188 -344.19227
## 2018 136.49266 384.43243 -233.28270 -227.49029 -252.69188 -344.19227
## 2019 136.49266 384.43243 -233.28270 -227.49029 -252.69188 -344.19227
## 2020 136.49266 384.43243 -233.28270 -227.49029 -252.69188 -344.19227
## 2021 136.49266 384.43243SulawesiUtaraouttimeseriescomponents <- decompose(SulawesiUtaraoutflowtimeseries)
SulawesiUtaraouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun Jul
## 2011 -628.0147 -369.1064 -88.8158 -91.5628 208.4773 264.6787 410.6321
## 2012 -628.0147 -369.1064 -88.8158 -91.5628 208.4773 264.6787 410.6321
## 2013 -628.0147 -369.1064 -88.8158 -91.5628 208.4773 264.6787 410.6321
## 2014 -628.0147 -369.1064 -88.8158 -91.5628 208.4773 264.6787 410.6321
## 2015 -628.0147 -369.1064 -88.8158 -91.5628 208.4773 264.6787 410.6321
## 2016 -628.0147 -369.1064 -88.8158 -91.5628 208.4773 264.6787 410.6321
## 2017 -628.0147 -369.1064 -88.8158 -91.5628 208.4773 264.6787 410.6321
## 2018 -628.0147 -369.1064 -88.8158 -91.5628 208.4773 264.6787 410.6321
## 2019 -628.0147 -369.1064 -88.8158 -91.5628 208.4773 264.6787 410.6321
## 2020 -628.0147 -369.1064 -88.8158 -91.5628 208.4773 264.6787 410.6321
## 2021 -628.0147 -369.1064 -88.8158 -91.5628 208.4773 264.6787 410.6321
## Aug Sep Oct Nov Dec
## 2011 126.4167 -351.1859 -211.0869 -103.9982 833.5658
## 2012 126.4167 -351.1859 -211.0869 -103.9982 833.5658
## 2013 126.4167 -351.1859 -211.0869 -103.9982 833.5658
## 2014 126.4167 -351.1859 -211.0869 -103.9982 833.5658
## 2015 126.4167 -351.1859 -211.0869 -103.9982 833.5658
## 2016 126.4167 -351.1859 -211.0869 -103.9982 833.5658
## 2017 126.4167 -351.1859 -211.0869 -103.9982 833.5658
## 2018 126.4167 -351.1859 -211.0869 -103.9982 833.5658
## 2019 126.4167 -351.1859 -211.0869 -103.9982 833.5658
## 2020 126.4167 -351.1859 -211.0869 -103.9982 833.5658
## 2021 126.4167plot(SulawesiUtaraintimeseriescomponents$seasonal,type = "l", col = "yellow")
lines(SulawesiUtaraouttimeseriescomponents$seasonal,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(SulawesiUtaraintimeseriescomponents$trend,type = "l", col = "yellow")
lines(SulawesiUtaraouttimeseriescomponents$trend,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(SulawesiUtaraintimeseriescomponents$random ,type = "l", col = "yellow")
lines(SulawesiUtaraouttimeseriescomponents$random,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(SulawesiUtaraintimeseriescomponents$figure ,type = "l", col = "yellow")
lines(SulawesiUtaraouttimeseriescomponents$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