Visualisasi Data Inflow dan Outflow Uang Kartal di Papua
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 Papua 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 Papua setiap periode
plot(datainflow$Keterangan,datainflow$`Papua`,type = "l", col= "steelblue")
2.Visualisasi Prediksi Data outflow Uang Kartal Papua setiap periode
plot(dataoutflow$Keterangan,dataoutflow$`Papua`,type = "l", col= "red")
3.Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Papua Setiap Periode
plot(datainflow$Keterangan,datainflow$`Papua`,type = "l", col= "steelblue")
lines(dataoutflow$Keterangan,dataoutflow$`Papua`,col="red")
legend("top",c("Inflow","Outflow"),fill=c("steelblue","red"))
4. Visualisasi Prediksi Data Inflow-Outflow Uang Kartal di Papua 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$`Papua`, type = "l", col = "green")
lines(dataoutflowperbulan$`Papua`,col="yellow")
legend("top",c("Inflow","Outflow"),fill=c("green","yellow"))
Papuatimeseries <- datainflowperbulan$`Papua`
plot.ts(Papuatimeseries , type = "l", col = "green")
logPapua <- log(datainflowperbulan$`Papua`)
plot.ts(logPapua)
5. Visualisasi Prediksi Data Inflow-Outflow Time Series Uang Kartal di Papua
Papuainflowtimeseries <- ts(datainflowperbulan$`Papua`, frequency=12, start=c(2011,1))
Papuainflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 565.66438 300.52046 346.44322 343.88180 350.90360 336.02216
## 2012 1041.10646 699.00645 431.27912 451.47403 411.92150 316.52884
## 2013 148.56037 87.83191 35.12840 65.44961 38.76113 27.06003
## 2014 1800.58228 691.50944 321.08274 435.54429 339.99470 372.52000
## 2015 1687.71441 558.74194 447.99982 299.40325 273.25056 329.25464
## 2016 1462.78264 706.39532 289.35896 302.57791 333.84066 273.65554
## 2017 1487.21729 513.29471 351.36627 260.78437 323.16760 210.51053
## 2018 2025.87905 970.97606 503.87721 327.80040 410.85671 1033.35815
## 2019 2564.67247 944.51987 511.25994 560.69923 398.13134 1281.87838
## 2020 3209.57783 1031.99799 747.73632 402.81940 289.24240 610.81981
## 2021 3082.59633 1285.49586 712.54210 590.49444 1066.73763 589.82785
## Jul Aug Sep Oct Nov Dec
## 2011 365.97157 308.25466 684.63260 386.32396 373.60392 347.89896
## 2012 469.28970 563.08380 469.92275 463.36544 389.60006 340.23491
## 2013 55.03773 181.67326 419.30186 358.73256 312.75946 400.79232
## 2014 227.38995 870.11880 391.76106 446.03917 397.41237 499.74344
## 2015 791.82008 430.33950 311.03578 334.50556 353.44281 281.47648
## 2016 744.37572 391.35350 359.54963 393.09652 360.48854 673.91699
## 2017 693.78588 439.33375 420.73043 503.65721 438.14056 710.97774
## 2018 671.18044 566.35274 453.05823 355.81881 412.64172 343.91175
## 2019 474.75518 444.49676 393.81552 567.95385 604.27640 512.38837
## 2020 511.37277 529.06073 653.42735 392.08801 676.22814 501.94251
## 2021 538.62563 642.73471Papuaoutflowtimeseries <- ts(dataoutflowperbulan$`Papua`, frequency=12, start=c(2011,1))
Papuaoutflowtimeseries## Jan Feb Mar Apr May Jun
## 2011 194.45568 229.89395 517.75286 693.61396 619.42368 735.05358
## 2012 158.64358 310.71038 537.04424 732.22198 795.95676 845.36837
## 2013 11.19840 63.83006 104.29619 115.90542 136.30296 189.17291
## 2014 173.99063 234.36432 468.82481 827.06580 442.06816 570.11261
## 2015 259.11474 217.74892 428.26705 587.57749 530.00101 770.33618
## 2016 77.82153 189.66181 307.97176 549.89514 663.34684 1846.27228
## 2017 56.73061 252.21600 182.24380 419.81005 599.97626 1497.01271
## 2018 152.21118 245.91574 449.86655 465.89474 1243.78640 1949.18643
## 2019 18.18296 76.29977 197.22944 810.89287 771.66864 45.53769
## 2020 232.44632 318.43406 628.13975 577.12000 990.03835 656.90805
## 2021 71.29951 167.39857 452.20032 864.02049 1364.86348 799.90861
## Jul Aug Sep Oct Nov Dec
## 2011 660.10737 1298.85207 320.75220 830.83112 918.28314 2967.05786
## 2012 977.32756 960.85980 534.65664 969.18617 1209.85472 5568.36943
## 2013 412.11826 199.01356 797.43057 850.89702 1417.59238 3503.70527
## 2014 1566.16605 265.50631 675.74330 1133.78665 1221.34415 3726.05212
## 2015 1688.22884 716.29519 768.84716 989.76610 1112.17797 3555.11869
## 2016 527.59998 695.06668 1193.63697 753.10671 800.07915 3895.57847
## 2017 301.57113 1133.37938 735.51619 551.49118 1207.09433 3713.27214
## 2018 652.58193 913.02193 563.36553 787.55099 1038.88697 3906.44858
## 2019 417.16127 647.68437 148.78954 340.00520 1401.07749 4730.37517
## 2020 767.52171 493.80411 711.10149 973.13583 1483.32489 4195.80676
## 2021 823.89483 865.61310plot.ts(Papuainflowtimeseries)
plot.ts(Papuaoutflowtimeseries)
Papuaintimeseriescomponents <- decompose(Papuainflowtimeseries)
Papuaintimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 1259.82187 155.61686 -156.54380 -215.59672 -248.95621 -69.24604
## 2012 1259.82187 155.61686 -156.54380 -215.59672 -248.95621 -69.24604
## 2013 1259.82187 155.61686 -156.54380 -215.59672 -248.95621 -69.24604
## 2014 1259.82187 155.61686 -156.54380 -215.59672 -248.95621 -69.24604
## 2015 1259.82187 155.61686 -156.54380 -215.59672 -248.95621 -69.24604
## 2016 1259.82187 155.61686 -156.54380 -215.59672 -248.95621 -69.24604
## 2017 1259.82187 155.61686 -156.54380 -215.59672 -248.95621 -69.24604
## 2018 1259.82187 155.61686 -156.54380 -215.59672 -248.95621 -69.24604
## 2019 1259.82187 155.61686 -156.54380 -215.59672 -248.95621 -69.24604
## 2020 1259.82187 155.61686 -156.54380 -215.59672 -248.95621 -69.24604
## 2021 1259.82187 155.61686 -156.54380 -215.59672 -248.95621 -69.24604
## Jul Aug Sep Oct Nov Dec
## 2011 -58.14814 -100.83057 -123.14328 -161.26165 -153.57056 -128.14177
## 2012 -58.14814 -100.83057 -123.14328 -161.26165 -153.57056 -128.14177
## 2013 -58.14814 -100.83057 -123.14328 -161.26165 -153.57056 -128.14177
## 2014 -58.14814 -100.83057 -123.14328 -161.26165 -153.57056 -128.14177
## 2015 -58.14814 -100.83057 -123.14328 -161.26165 -153.57056 -128.14177
## 2016 -58.14814 -100.83057 -123.14328 -161.26165 -153.57056 -128.14177
## 2017 -58.14814 -100.83057 -123.14328 -161.26165 -153.57056 -128.14177
## 2018 -58.14814 -100.83057 -123.14328 -161.26165 -153.57056 -128.14177
## 2019 -58.14814 -100.83057 -123.14328 -161.26165 -153.57056 -128.14177
## 2020 -58.14814 -100.83057 -123.14328 -161.26165 -153.57056 -128.14177
## 2021 -58.14814 -100.83057Papuaouttimeseriescomponents <- decompose(Papuaoutflowtimeseries)
Papuaouttimeseriescomponents$seasonal## Jan Feb Mar Apr May Jun
## 2011 -804.616322 -716.999591 -542.008020 -346.418468 -228.941898 6.838166
## 2012 -804.616322 -716.999591 -542.008020 -346.418468 -228.941898 6.838166
## 2013 -804.616322 -716.999591 -542.008020 -346.418468 -228.941898 6.838166
## 2014 -804.616322 -716.999591 -542.008020 -346.418468 -228.941898 6.838166
## 2015 -804.616322 -716.999591 -542.008020 -346.418468 -228.941898 6.838166
## 2016 -804.616322 -716.999591 -542.008020 -346.418468 -228.941898 6.838166
## 2017 -804.616322 -716.999591 -542.008020 -346.418468 -228.941898 6.838166
## 2018 -804.616322 -716.999591 -542.008020 -346.418468 -228.941898 6.838166
## 2019 -804.616322 -716.999591 -542.008020 -346.418468 -228.941898 6.838166
## 2020 -804.616322 -716.999591 -542.008020 -346.418468 -228.941898 6.838166
## 2021 -804.616322 -716.999591 -542.008020 -346.418468 -228.941898 6.838166
## Jul Aug Sep Oct Nov Dec
## 2011 -121.467115 -185.383638 -272.214486 -99.659642 259.520155 3051.350858
## 2012 -121.467115 -185.383638 -272.214486 -99.659642 259.520155 3051.350858
## 2013 -121.467115 -185.383638 -272.214486 -99.659642 259.520155 3051.350858
## 2014 -121.467115 -185.383638 -272.214486 -99.659642 259.520155 3051.350858
## 2015 -121.467115 -185.383638 -272.214486 -99.659642 259.520155 3051.350858
## 2016 -121.467115 -185.383638 -272.214486 -99.659642 259.520155 3051.350858
## 2017 -121.467115 -185.383638 -272.214486 -99.659642 259.520155 3051.350858
## 2018 -121.467115 -185.383638 -272.214486 -99.659642 259.520155 3051.350858
## 2019 -121.467115 -185.383638 -272.214486 -99.659642 259.520155 3051.350858
## 2020 -121.467115 -185.383638 -272.214486 -99.659642 259.520155 3051.350858
## 2021 -121.467115 -185.383638plot(Papuaintimeseriescomponents$seasonal,type = "l", col = "orange")
lines(Papuaouttimeseriescomponents$seasonal,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Papuaintimeseriescomponents$trend,type = "l", col = "orange")
lines(Papuaouttimeseriescomponents$trend,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Papuaintimeseriescomponents$random ,type = "l", col = "orange")
lines(Papuaouttimeseriescomponents$random,col="grey")
legend("top",c("Inflow","Outflow"),fill=c("orange","grey"))
plot(Papuaintimeseriescomponents$figure ,type = "l", col = "orange")
lines(Papuaouttimeseriescomponents$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