Visualisasi Data Inflow dan Outflow Uang Kartal di Sulawesi Selatan
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 Selatan 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 Selatan setiap periode
plot(datainflow$Keterangan,datainflow$`Sulawesi Selatan`,type = "l", col= "red")
2.Visualisasi Prediksi Data outflow Uang Kartal Sulawesi Selatan setiap periode
plot(dataoutflow$Keterangan,dataoutflow$`Sulawesi Selatan`,type = "l", col= "green")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sulawesi Selatan Setiap Periode
plot(datainflow$Keterangan,datainflow$`Sulawesi Selatan`,type = "l", col= "red")
lines(dataoutflow$Keterangan,dataoutflow$`Sulawesi Selatan`,col="green")
legend("top",c("Inflow","Outflow"),fill=c("red","green"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sulawesi Selatan 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$`Sulawesi Selatan`, type = "l", col = "grey")
lines(dataoutflowperbulan$`Sulawesi Selatan`,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("grey","blue"))
Sulawesiselatantimeseries <- datainflowperbulan$`Sulawesi Selatan`
plot.ts(Sulawesiselatantimeseries , type = "l", col = "green")
logSulawesiSelatan <- log(datainflowperbulan$`Sulawesi Selatan`)
plot.ts(logSulawesiSelatan)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Sulawesi Selatan
SulawesiSelataninflowtimeseries <- ts(datainflowperbulan$`Sulawesi Selatan`, frequency=12, start=c(2011,1))
SulawesiSelataninflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 737.2701 681.9942 910.0269 634.0787 697.3718 768.1180 742.7741
## 2012 1825.5671 1188.5240 857.6867 740.7358 1106.8802 906.3162 1168.7513
## 2013 2351.4793 1368.2980 1094.8123 1243.7820 1322.5633 1087.1937 1253.6472
## 2014 2732.2070 1503.5450 1039.9472 1315.0485 1264.3109 1572.9139 1109.0895
## 2015 3352.9496 1704.0220 1150.3860 1310.9590 1026.3442 1495.2299 2945.2499
## 2016 3002.2932 2041.5394 1291.9515 1111.7549 1267.5116 1264.3756 3935.5280
## 2017 2902.7937 1344.6431 876.9484 1336.9740 1605.1893 581.3572 3638.3386
## 2018 3539.6905 1393.2126 874.1457 1285.0432 1451.2186 3097.2785 2757.6692
## 2019 3493.4415 1808.0409 1188.0854 1464.0796 1705.3327 4474.1179 2360.2352
## 2020 4119.1319 2311.2332 1720.2440 809.5786 1135.8491 2606.6298 1359.0222
## 2021 4336.9419 2411.6856 1839.7368 1679.5252 2757.1762 2221.8949 1541.3612
## Aug Sep Oct Nov Dec
## 2011 647.3934 2324.1407 832.8167 1026.7175 590.7962
## 2012 1699.7772 1068.5728 988.4976 1354.5523 795.6488
## 2013 3010.8676 959.0225 1722.4662 1467.0511 888.6079
## 2014 3359.5185 1096.1824 1921.2403 1384.3146 1086.1001
## 2015 1477.0931 1288.5123 1604.9752 1340.7632 886.6034
## 2016 1231.0532 1397.8638 1635.0335 1628.3034 1236.0943
## 2017 1290.0620 1258.9023 1858.8599 1402.9977 706.0825
## 2018 1147.0689 1380.6095 2200.5911 1950.7287 816.4931
## 2019 1448.0190 1832.4393 2337.3045 1729.5425 908.7993
## 2020 1327.9169 1718.9034 1249.1544 2010.3617 1182.8652
## 2021 1547.1478SulawesiSelatanoutflowtimeseries <- ts(dataoutflowperbulan$`Sulawesi Selatan`, frequency=12, start=c(2011,1))
SulawesiSelatanoutflowtimeseries## Jan Feb Mar Apr May Jun Jul
## 2011 308.8302 237.8154 704.9791 738.6864 573.7936 593.2387 697.0496
## 2012 510.4450 454.6563 931.5922 1184.4716 912.5994 1076.4658 1011.8941
## 2013 154.0943 456.5932 538.6588 450.5097 726.9099 762.7118 1715.5574
## 2014 571.5631 669.3362 1082.1114 1236.1172 1142.5821 978.4584 3281.9684
## 2015 449.8504 624.7593 1184.9137 1558.5959 1136.3951 1374.2081 3073.0906
## 2016 343.9866 641.9787 824.1808 1442.7011 1391.4076 3169.4211 1467.6018
## 2017 488.8822 582.9862 1170.1978 862.8797 878.1953 3410.4420 721.6211
## 2018 163.1490 578.0414 1446.4317 1365.6673 1504.9645 3092.2174 880.0667
## 2019 343.7722 509.6206 1602.1095 2025.2509 4346.8133 141.9142 1323.0795
## 2020 414.5397 1016.5017 1727.2575 1794.6297 3671.4842 634.0485 1261.8806
## 2021 237.7465 809.6438 1897.2916 2804.0416 2712.6154 972.5763 1553.6345
## Aug Sep Oct Nov Dec
## 2011 2067.3531 489.8115 724.9256 731.3198 1099.4002
## 2012 1894.6604 681.6072 968.0655 616.1420 1630.0031
## 2013 975.9749 1480.8559 1165.7191 1060.4188 1996.6188
## 2014 833.3638 1668.2002 909.6691 1107.7162 2163.5076
## 2015 1402.7793 1483.1910 848.9114 1283.1252 1815.9132
## 2016 1009.3475 1233.0616 893.9218 1163.5838 1912.4182
## 2017 1746.5581 1122.7270 800.9915 1375.4004 1997.9313
## 2018 1660.2147 1270.9735 1219.2608 1317.6617 2280.1121
## 2019 1607.7889 1145.5304 909.9568 1620.9901 2511.9732
## 2020 1815.4617 1463.4415 2319.1946 1609.1700 2775.8378
## 2021 1029.6811plot.ts(SulawesiSelataninflowtimeseries)
plot.ts(SulawesiSelatanoutflowtimeseries)
SulawesiSelatanintimeseriescomponents <- decompose(SulawesiSelataninflowtimeseries)
SulawesiSelatanintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2012 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2013 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2014 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2015 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2016 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2017 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2018 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2019 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2020 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## 2021 1495.193991 29.942375 -516.735310 -457.666507 -323.342981 247.163478
## Jul Aug Sep Oct Nov Dec
## 2011 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2012 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2013 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2014 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2015 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2016 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2017 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2018 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2019 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2020 535.054604 49.695394 -192.747537 1.601677 -116.897535 -751.261648
## 2021 535.054604 49.695394SulawesiSelatanouttimeseriescomponents <- decompose(SulawesiSelatanoutflowtimeseries)
SulawesiSelatanouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2012 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2013 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2014 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2015 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2016 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2017 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2018 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2019 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2020 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## 2021 -941.40276 -674.03939 -98.98738 46.16529 455.89008 325.01312
## Jul Aug Sep Oct Nov Dec
## 2011 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2012 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2013 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2014 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2015 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2016 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2017 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2018 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2019 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2020 290.34538 246.22820 -58.53264 -199.98463 -105.01084 714.31556
## 2021 290.34538 246.22820plot(SulawesiSelatanintimeseriescomponents$seasonal,type = "l", col = "yellow")
lines(SulawesiSelatanouttimeseriescomponents$seasonal,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(SulawesiSelatanintimeseriescomponents$trend,type = "l", col = "yellow")
lines(SulawesiSelatanouttimeseriescomponents$trend,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(SulawesiSelatanintimeseriescomponents$random ,type = "l", col = "yellow")
lines(SulawesiSelatanouttimeseriescomponents$random,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(SulawesiSelatanintimeseriescomponents$figure ,type = "l", col = "yellow")
lines(SulawesiSelatanouttimeseriescomponents$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