Visualisasi Data Inflow dan Outflow Uang Kartal di Sulawesi Tennggara
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 Tenggara 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 Tenggara setiap periode
plot(datainflow$Keterangan,datainflow$`Sulawesi Tenggara`,type = "l", col= "steelblue")
2.Visualisasi Prediksi Data outflow Uang Kartal Sulawesi Tenggara setiap periode
plot(dataoutflow$Keterangan,dataoutflow$`Sulawesi Tenggara`,type = "l", col= "red")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sulawesi Tenggara Setiap Periode
plot(datainflow$Keterangan,datainflow$`Sulawesi Tenggara`,type = "l", col= "steelblue")
lines(dataoutflow$Keterangan,dataoutflow$`Sulawesi Tenggara`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Sulawesi Tenggara 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$`Sulawesi Tenggara`, type = "l", col = "green")
lines(dataoutflowperbulan$`Sulawesi Tenggara`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))
Sulawesitenggaratimeseries <- datainflowperbulan$`Sulawesi Barat`
plot.ts(Sulawesitenggaratimeseries , type = "l", col = "green")
logSulawesiTenggara <- log(datainflowperbulan$`Sulawesi Tenggara`)
plot.ts(logSulawesiTenggara)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Sulawesi Tenggara
SulawesiTenggarainflowtimeseries <- ts(datainflowperbulan$`Sulawesi Tenggara`, frequency=12, start=c(2011,1))
SulawesiTenggarainflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 17.46064 29.07231 77.95622 42.71456 43.41467 40.89543
## 2012 156.64694 120.34831 90.28786 24.40074 34.33966 36.44918
## 2013 869.33607 566.88049 554.55945 634.03951 683.15507 636.33399
## 2014 366.38827 225.27405 135.91558 194.93213 111.05742 147.93138
## 2015 428.76170 213.55862 296.39058 136.84080 149.80915 143.99229
## 2016 601.87371 345.83870 331.52097 222.14196 208.26145 148.86011
## 2017 571.93147 305.85325 365.48682 254.02307 244.40816 177.20922
## 2018 779.00940 327.74353 215.69921 179.84834 259.27769 584.58320
## 2019 853.90869 334.64408 251.31856 303.03695 279.92184 977.38046
## 2020 858.61880 388.90259 218.58032 168.97056 165.39020 496.32594
## 2021 1092.75184 403.63074 260.86608 174.81246 659.24168 286.11471
## Jul Aug Sep Oct Nov Dec
## 2011 38.24892 36.34873 222.56268 44.96187 34.03192 31.76244
## 2012 60.30533 103.90681 175.78040 29.97181 112.26945 18.88082
## 2013 734.71012 1192.57059 82.99706 42.72064 67.48235 28.04933
## 2014 58.82318 491.83317 160.55754 192.03389 115.37769 55.55635
## 2015 395.63102 220.23850 137.63382 133.69577 69.54667 58.73375
## 2016 598.68937 206.45070 335.09777 240.08222 109.60234 142.48754
## 2017 689.80737 241.27598 322.97465 178.58539 164.75809 101.46082
## 2018 283.75064 238.99532 271.88902 227.20078 166.43951 97.72832
## 2019 237.18913 384.87681 246.14702 236.21812 176.44776 108.56877
## 2020 237.86643 276.69428 216.00085 94.11392 138.69549 92.48508
## 2021 139.96932 252.71567SulawesiTenggaraoutflowtimeseries <- ts(dataoutflowperbulan$`Sulawesi Tenggara`, frequency=12, start=c(2011,1))
SulawesiTenggaraoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 62.454387 4.054187 46.868274 166.615713 258.589340 225.030821
## 2012 136.373728 6.833527 89.338626 185.990924 343.437969 307.764864
## 2013 137.409855 197.928453 239.198165 193.174364 247.780550 300.821058
## 2014 35.065775 8.708954 118.876568 298.102156 165.464251 367.636693
## 2015 107.959177 30.679383 91.090371 290.651096 306.314684 325.728078
## 2016 100.972989 54.667364 126.833802 132.693114 426.870704 1052.287353
## 2017 94.098294 139.071390 169.997116 343.718056 509.099936 1236.244837
## 2018 139.247004 158.972830 199.525024 397.642141 375.972851 1198.549784
## 2019 27.712205 150.411433 237.778014 787.672226 704.904071 1.657765
## 2020 56.560651 44.745958 214.460892 301.633039 1036.390058 116.632773
## 2021 14.993519 68.288670 166.351227 478.404140 1114.959645 169.129214
## Jul Aug Sep Oct Nov Dec
## 2011 360.696194 562.880535 25.140146 198.739503 328.378569 649.379967
## 2012 326.976555 396.228222 38.022504 316.186298 223.365829 579.698256
## 2013 633.860577 525.398099 234.258905 368.635352 421.852838 738.458393
## 2014 876.240420 64.422462 318.810130 297.459923 346.701790 639.994697
## 2015 980.346908 315.234939 461.114512 414.662820 621.838883 770.173803
## 2016 347.688297 345.668199 350.619057 277.198066 535.937679 736.547932
## 2017 105.912719 444.853393 252.007548 307.806304 674.779543 1015.205778
## 2018 235.309352 347.372459 173.762112 404.925909 484.432297 1108.583322
## 2019 496.038062 264.453632 250.288484 503.787786 619.087376 1012.576804
## 2020 389.419055 379.005192 341.255959 564.143567 529.763136 1155.425503
## 2021 294.650115 200.408008plot.ts(SulawesiTenggarainflowtimeseries)
plot.ts(SulawesiTenggaraoutflowtimeseries)
SulawesiTenggaraintimeseriescomponents <- decompose(SulawesiTenggarainflowtimeseries)
SulawesiTenggaraintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 376.498239 40.517623 -6.304671 -44.449103 -43.229370 90.832189
## 2012 376.498239 40.517623 -6.304671 -44.449103 -43.229370 90.832189
## 2013 376.498239 40.517623 -6.304671 -44.449103 -43.229370 90.832189
## 2014 376.498239 40.517623 -6.304671 -44.449103 -43.229370 90.832189
## 2015 376.498239 40.517623 -6.304671 -44.449103 -43.229370 90.832189
## 2016 376.498239 40.517623 -6.304671 -44.449103 -43.229370 90.832189
## 2017 376.498239 40.517623 -6.304671 -44.449103 -43.229370 90.832189
## 2018 376.498239 40.517623 -6.304671 -44.449103 -43.229370 90.832189
## 2019 376.498239 40.517623 -6.304671 -44.449103 -43.229370 90.832189
## 2020 376.498239 40.517623 -6.304671 -44.449103 -43.229370 90.832189
## 2021 376.498239 40.517623 -6.304671 -44.449103 -43.229370 90.832189
## Jul Aug Sep Oct Nov Dec
## 2011 69.903689 69.679585 -54.798207 -131.316379 -160.926049 -206.407545
## 2012 69.903689 69.679585 -54.798207 -131.316379 -160.926049 -206.407545
## 2013 69.903689 69.679585 -54.798207 -131.316379 -160.926049 -206.407545
## 2014 69.903689 69.679585 -54.798207 -131.316379 -160.926049 -206.407545
## 2015 69.903689 69.679585 -54.798207 -131.316379 -160.926049 -206.407545
## 2016 69.903689 69.679585 -54.798207 -131.316379 -160.926049 -206.407545
## 2017 69.903689 69.679585 -54.798207 -131.316379 -160.926049 -206.407545
## 2018 69.903689 69.679585 -54.798207 -131.316379 -160.926049 -206.407545
## 2019 69.903689 69.679585 -54.798207 -131.316379 -160.926049 -206.407545
## 2020 69.903689 69.679585 -54.798207 -131.316379 -160.926049 -206.407545
## 2021 69.903689 69.679585SulawesiTenggaraouttimeseriescomponents <- decompose(SulawesiTenggaraoutflowtimeseries)
SulawesiTenggaraouttimeseriescomponents$seasonal## Jan Feb Mar Apr May
## 2011 -289.0699481 -286.2929776 -200.8995789 -43.5904816 85.4474922
## 2012 -289.0699481 -286.2929776 -200.8995789 -43.5904816 85.4474922
## 2013 -289.0699481 -286.2929776 -200.8995789 -43.5904816 85.4474922
## 2014 -289.0699481 -286.2929776 -200.8995789 -43.5904816 85.4474922
## 2015 -289.0699481 -286.2929776 -200.8995789 -43.5904816 85.4474922
## 2016 -289.0699481 -286.2929776 -200.8995789 -43.5904816 85.4474922
## 2017 -289.0699481 -286.2929776 -200.8995789 -43.5904816 85.4474922
## 2018 -289.0699481 -286.2929776 -200.8995789 -43.5904816 85.4474922
## 2019 -289.0699481 -286.2929776 -200.8995789 -43.5904816 85.4474922
## 2020 -289.0699481 -286.2929776 -200.8995789 -43.5904816 85.4474922
## 2021 -289.0699481 -286.2929776 -200.8995789 -43.5904816 85.4474922
## Jun Jul Aug Sep Oct
## 2011 170.0710344 111.4663883 0.6993976 -120.0898692 -1.0602161
## 2012 170.0710344 111.4663883 0.6993976 -120.0898692 -1.0602161
## 2013 170.0710344 111.4663883 0.6993976 -120.0898692 -1.0602161
## 2014 170.0710344 111.4663883 0.6993976 -120.0898692 -1.0602161
## 2015 170.0710344 111.4663883 0.6993976 -120.0898692 -1.0602161
## 2016 170.0710344 111.4663883 0.6993976 -120.0898692 -1.0602161
## 2017 170.0710344 111.4663883 0.6993976 -120.0898692 -1.0602161
## 2018 170.0710344 111.4663883 0.6993976 -120.0898692 -1.0602161
## 2019 170.0710344 111.4663883 0.6993976 -120.0898692 -1.0602161
## 2020 170.0710344 111.4663883 0.6993976 -120.0898692 -1.0602161
## 2021 170.0710344 111.4663883 0.6993976
## Nov Dec
## 2011 107.3316969 465.9870621
## 2012 107.3316969 465.9870621
## 2013 107.3316969 465.9870621
## 2014 107.3316969 465.9870621
## 2015 107.3316969 465.9870621
## 2016 107.3316969 465.9870621
## 2017 107.3316969 465.9870621
## 2018 107.3316969 465.9870621
## 2019 107.3316969 465.9870621
## 2020 107.3316969 465.9870621
## 2021plot(SulawesiTenggaraintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(SulawesiTenggaraouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(SulawesiTenggaraintimeseriescomponents$trend,type = "l", col = "orange")
lines(SulawesiTenggaraouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(SulawesiTenggaraintimeseriescomponents$random ,type = "l", col = "orange")
lines(SulawesiTenggaraouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(SulawesiTenggaraintimeseriescomponents$figure ,type = "l", col = "orange")
lines(SulawesiTenggaraouttimeseriescomponents$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