Visualisasi Data Inflow dan Outflow Uang Kartal di Sulawesi Tengah
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 Tengah 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 Tengah setiap periode
plot(datainflow$Keterangan,datainflow$`Sulawesi Tengah`,type = "l", col= "red")
2.Visualisasi Prediksi Data outflow Uang Kartal Sulawesi Tengah setiap periode
plot(dataoutflow$Keterangan,dataoutflow$`Sulawesi Tengah`,type = "l", col= "green")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sulawesi Tengah Setiap Periode
plot(datainflow$Keterangan,datainflow$`Sulawesi Tengah`,type = "l", col= "red")
lines(dataoutflow$Keterangan,dataoutflow$`Sulawesi Tengah`,col="green")
legend("top",c("Inflow","Outflow"),fill=c("red","green"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sulawesi Tengah 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$`Sulawesi Tengah`, type = "l", col = "grey")
lines(dataoutflowperbulan$`Sulawesi Tengah`,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("grey","blue"))
SulawesiTengahtimeseries <- datainflowperbulan$`Sulawesi Tengah`
plot.ts(SulawesiTengahtimeseries , type = "l", col = "green")
logSulawesiTengah <- log(datainflowperbulan$`Sulawesi Tengah`)
plot.ts(logSulawesiTengah)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Sulawesi Tengah
SulawesiTengahinflowtimeseries <- ts(datainflowperbulan$`Sulawesi Tengah`, frequency=12, start=c(2011,1))
SulawesiTengahinflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 167.26998 46.14598 133.41347 91.49704 105.98355 77.04844
## 2012 248.82399 172.28505 143.79868 103.60364 103.26596 132.61135
## 2013 319.55655 134.35793 68.99486 97.45267 68.34871 67.38469
## 2014 553.00370 194.23294 261.88003 218.51428 126.30500 128.39565
## 2015 566.04017 203.76501 235.78921 90.38058 131.05897 91.30496
## 2016 741.37684 269.47784 143.30129 112.94513 64.78936 83.73299
## 2017 654.07171 143.02319 204.10374 132.85430 125.83405 52.46426
## 2018 861.21263 234.21232 206.87087 182.57411 223.58674 673.64369
## 2019 816.78979 397.26963 291.72698 153.83717 183.97768 904.17065
## 2020 1077.50837 316.13860 219.91759 71.42271 78.69819 600.38224
## 2021 800.04493 352.14545 233.88792 147.24854 541.70387 115.18993
## Jul Aug Sep Oct Nov Dec
## 2011 112.77641 76.91953 445.70102 113.01301 111.62657 81.54623
## 2012 190.51813 322.09493 138.02878 91.82040 160.95987 77.01847
## 2013 67.68421 229.46316 141.98064 160.64699 96.96520 67.60625
## 2014 110.04381 762.50091 204.35996 187.64927 147.31859 105.86965
## 2015 618.67821 232.53226 96.78322 123.08644 133.47048 70.41339
## 2016 605.51154 130.71891 203.60870 96.05308 121.71920 92.01940
## 2017 663.14823 236.76203 219.78909 168.89347 123.81309 81.67147
## 2018 391.59146 254.94142 264.99702 158.68451 166.02966 82.87243
## 2019 201.60482 294.47078 291.60007 239.21960 190.54611 77.17666
## 2020 138.66612 154.73949 224.44496 76.82091 68.87308 24.23890
## 2021 83.65763 179.32124SulawesiTengahoutflowtimeseries <- ts(dataoutflowperbulan$`Sulawesi Tengah`, frequency=12, start=c(2011,1))
SulawesiTengahoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 83.52626 139.20889 189.01339 266.09811 316.50246 311.42269
## 2012 48.78968 173.68455 201.01544 448.80453 434.20940 541.68714
## 2013 62.31053 184.98441 199.45461 216.53300 346.60076 405.05813
## 2014 126.51288 336.77990 349.59949 286.80337 462.15133 504.86635
## 2015 63.43339 154.98554 210.97966 331.34238 491.36575 436.54248
## 2016 51.66787 122.01281 154.68674 248.33836 540.42497 1111.78429
## 2017 20.53858 177.66741 203.18767 305.75983 460.39274 1377.23447
## 2018 21.91652 117.74833 300.01911 358.78197 582.67537 1185.23212
## 2019 87.50441 157.62047 253.22245 516.36839 1388.74256 136.28136
## 2020 100.65534 176.27693 328.42979 403.43130 962.66225 107.25400
## 2021 93.35880 121.20598 299.44720 590.39948 844.79079 258.39079
## Jul Aug Sep Oct Nov Dec
## 2011 351.32202 656.06484 105.28887 356.16598 518.52231 724.32784
## 2012 363.84048 558.95138 169.59933 490.16620 264.96123 762.17182
## 2013 672.49109 239.78137 357.32851 487.24861 494.76592 877.37009
## 2014 1097.49275 95.99591 551.07389 530.05262 587.82883 766.93840
## 2015 1173.56490 225.14058 406.80329 263.45876 535.89720 1016.26644
## 2016 261.57613 383.65763 322.55177 281.77009 550.43161 933.48129
## 2017 94.82192 338.85094 260.08472 368.41938 711.63039 907.88480
## 2018 175.43220 373.18549 263.24590 411.73780 523.05951 1264.98624
## 2019 380.71561 325.42171 242.57880 376.25458 525.40022 1141.18330
## 2020 366.26116 227.07939 410.22490 424.05307 279.96218 887.58567
## 2021 313.85120 241.47787plot.ts(SulawesiTengahinflowtimeseries)
plot.ts(SulawesiTengahoutflowtimeseries)
SulawesiTengahintimeseriescomponents <- decompose(SulawesiTengahinflowtimeseries)
SulawesiTengahintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 426.802772 4.345354 -39.367330 -106.264181 -112.312018 69.065209
## 2012 426.802772 4.345354 -39.367330 -106.264181 -112.312018 69.065209
## 2013 426.802772 4.345354 -39.367330 -106.264181 -112.312018 69.065209
## 2014 426.802772 4.345354 -39.367330 -106.264181 -112.312018 69.065209
## 2015 426.802772 4.345354 -39.367330 -106.264181 -112.312018 69.065209
## 2016 426.802772 4.345354 -39.367330 -106.264181 -112.312018 69.065209
## 2017 426.802772 4.345354 -39.367330 -106.264181 -112.312018 69.065209
## 2018 426.802772 4.345354 -39.367330 -106.264181 -112.312018 69.065209
## 2019 426.802772 4.345354 -39.367330 -106.264181 -112.312018 69.065209
## 2020 426.802772 4.345354 -39.367330 -106.264181 -112.312018 69.065209
## 2021 426.802772 4.345354 -39.367330 -106.264181 -112.312018 69.065209
## Jul Aug Sep Oct Nov Dec
## 2011 83.298159 38.878645 -9.199992 -91.391511 -102.895891 -160.959216
## 2012 83.298159 38.878645 -9.199992 -91.391511 -102.895891 -160.959216
## 2013 83.298159 38.878645 -9.199992 -91.391511 -102.895891 -160.959216
## 2014 83.298159 38.878645 -9.199992 -91.391511 -102.895891 -160.959216
## 2015 83.298159 38.878645 -9.199992 -91.391511 -102.895891 -160.959216
## 2016 83.298159 38.878645 -9.199992 -91.391511 -102.895891 -160.959216
## 2017 83.298159 38.878645 -9.199992 -91.391511 -102.895891 -160.959216
## 2018 83.298159 38.878645 -9.199992 -91.391511 -102.895891 -160.959216
## 2019 83.298159 38.878645 -9.199992 -91.391511 -102.895891 -160.959216
## 2020 83.298159 38.878645 -9.199992 -91.391511 -102.895891 -160.959216
## 2021 83.298159 38.878645SulawesiTengahouttimeseriescomponents <- decompose(SulawesiTengahoutflowtimeseries)
SulawesiTengahouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -357.51933 -251.00793 -181.07041 -81.06665 203.39706 218.93626
## 2012 -357.51933 -251.00793 -181.07041 -81.06665 203.39706 218.93626
## 2013 -357.51933 -251.00793 -181.07041 -81.06665 203.39706 218.93626
## 2014 -357.51933 -251.00793 -181.07041 -81.06665 203.39706 218.93626
## 2015 -357.51933 -251.00793 -181.07041 -81.06665 203.39706 218.93626
## 2016 -357.51933 -251.00793 -181.07041 -81.06665 203.39706 218.93626
## 2017 -357.51933 -251.00793 -181.07041 -81.06665 203.39706 218.93626
## 2018 -357.51933 -251.00793 -181.07041 -81.06665 203.39706 218.93626
## 2019 -357.51933 -251.00793 -181.07041 -81.06665 203.39706 218.93626
## 2020 -357.51933 -251.00793 -181.07041 -81.06665 203.39706 218.93626
## 2021 -357.51933 -251.00793 -181.07041 -81.06665 203.39706 218.93626
## Jul Aug Sep Oct Nov Dec
## 2011 75.88177 -75.42309 -109.34314 -21.09983 75.66095 502.65436
## 2012 75.88177 -75.42309 -109.34314 -21.09983 75.66095 502.65436
## 2013 75.88177 -75.42309 -109.34314 -21.09983 75.66095 502.65436
## 2014 75.88177 -75.42309 -109.34314 -21.09983 75.66095 502.65436
## 2015 75.88177 -75.42309 -109.34314 -21.09983 75.66095 502.65436
## 2016 75.88177 -75.42309 -109.34314 -21.09983 75.66095 502.65436
## 2017 75.88177 -75.42309 -109.34314 -21.09983 75.66095 502.65436
## 2018 75.88177 -75.42309 -109.34314 -21.09983 75.66095 502.65436
## 2019 75.88177 -75.42309 -109.34314 -21.09983 75.66095 502.65436
## 2020 75.88177 -75.42309 -109.34314 -21.09983 75.66095 502.65436
## 2021 75.88177 -75.42309plot(SulawesiTengahintimeseriescomponents$seasonal,type = "l", col = "yellow")
lines(SulawesiTengahouttimeseriescomponents$seasonal,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(SulawesiTengahintimeseriescomponents$trend,type = "l", col = "yellow")
lines(SulawesiTengahouttimeseriescomponents$trend,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(SulawesiTengahintimeseriescomponents$random ,type = "l", col = "yellow")
lines(SulawesiTengahouttimeseriescomponents$random,col="blue")
legend("top",c("Inflow","Outflow"),fill=c("yellow","blue"))
plot(SulawesiTengahintimeseriescomponents$figure ,type = "l", col = "yellow")
lines(SulawesiTengahouttimeseriescomponents$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