Application of Random Forest Method Classification to Predict BPJS Kesehatan Card Users Who Receive Contribution Assistance in Karangasem District, Bali Province 2017

Authors

  • Qonita Raihananda Politeknik Statistika STIS
  • I Wayan Edy Darma Putra Politeknik Statistika STIS
  • Monica Seftaviani Sijabat Politeknik Statistika STIS
  • Sifa Rofatunnisa Politeknik Statistika STIS
  • Ibnu Maruf Politeknik Statistika STIS
  • Hermarwan Hermarwan Politeknik Statistika STIS
  • Rani Nooraeni Politeknik Statistika STIS

DOI:

https://doi.org/10.20956/jmsk.v17i2.11710

Keywords:

BPJS Kesehatan PBI, Karangasem, Classification, Random Forest

Abstract

BPJS Kesehatan is a social security facility provided by the government to all people who are registered as members. BPJS Kesehatan membership is divided into two, namely BPJS for Contribution Assistance Recipients (BPJS PBI) and BPJS Non-Contribution Assistance Recipients (BPJS Non-PBI). In 2019, Bali Province is targeted to achieve Universal Health Coverage of 95 percent so that the Bali Provincial Government has budgeted funds worth IDR 945 billion to finance JKN - KBS services which are integrated with JKN - KIS. Karangasem is one of the four districts in Bali Province that received the most percentage of financing, which is 51 percent of the total budget needed when compared to other areas. This study aims to classify the BPJS-PBI recipient community based on education variables, employment indicators, age, and per capita expenditure in Karangasem Regency in 2017. The classification method used in this study is the random forest method. The results showed that the per capita expenditure variable had the largest contribution in classifying the status of PBI participants. The model that is formed produces an accuracy of 0.8017. This means that the model can predict 80.17 percent testing data correctly.

Downloads

Download data is not yet available.

References

Amir, S. & Prasetyo B. (2020). Comparison of Elliptic Envelope Method and Isolation Forest Method on Imbalance Dataset. Jurnal Matematika, Statistika, dan Komputasi, 17(1), 42-49. DOI : 10.20956/jmsk.v%vi%i.10899

BPJS Kesehatan. (2016). Peserta Program JKN. Diunduh dari https://faskes.bpjs-kesehatan.go.id/aplicares/#/app/peta pada tanggal 13 Oktober 2020.

BPJS Kesehatan. (2020). Peserta BPJS. Diunduh dari https://bpjs-kesehatan.go.id/bpjs/pages/detail/2014/11 pada tanggal 13 Oktober 2020.

BPJS Kesehatan. (2020). Seputar BPJS Kesehatan. Diunduh dari https://bpjs-kesehatan.go.id/bpjs/dmdocuments/eac4e7a830f58b4ade926754f74b6caf.pdf pada tanggal 29 Oktober 2020.

BPS. (2018). Persentase Penduduk Miskin Provinsi Bali menurut Kabupaten/Kota. Diunduh dari https://bali.bps.go.id/indicator/23/125/1/persentase-penduduk-miskin-provinsi-bali-menurut-kabupaten-kota.html pada tanggal 13 Oktober 2020.

BPS. (2019). Indikator Ketenagakerjaan Kabupaten Karangasem. Diakses dari https://karangasemkab.bps.go.id/indicator/6/93/1/indikator-ketenagakerjaan-kabupaten-karangasem.html pada tanggal 14 Oktober 2020.

Diana, F. N. (2019). Pekerja Informal dan Kemiskinan di Bekasi. Diunduh dari http://m.ayobekasi.net/read/2019/05/20/2805/pekerja-informal-dan-kemiskinan-di-kabupaten-bekasi pada tanggal 12 Oktober 2020.

Fudloli, M. T., & Sukidin. (2015). Tingkat Partisipasi Angkatan Kerja Masyarakat Miskin di RT.01 RW.06 Desa Tegal Gede Kecamatan Sumbersari Kabupaten Jember. Jurnal Pendidikan Ekonomi, 9(2), 15-25. Diakses dari https://jurnal.unej.ac.id/index.php/JPE/article/view/3368

Nisa, I. M. K., & Nooraeni, R. (2020). Penerapan Metode Random Forest Untuk Klasifikasi Wanita Usia Subur di Perdesaan Dalam Menggunakan Internet (SDKI 2017). Jurnal Matematika Dan Statistika Serta Aplikasinya, 8(1), 72-76. DOI: https://doi.org/10.24252/msa.v8i1.13162f

Pramana, S., Yuniarto, B., Mariyah, S., Santoso, I., Nooraeni, R. (2018). Data Mining dengan R: Konsep serta Implementasi. Bogor: In Media.

Wiratmini, N. E. (2018). Targetkan UHC 95% pada 2019, Bali Anggarkan Rp495 Miliar untuk Pembiayaan JKN. Diunduh dari Bali Bisnis: https://bali.bisnis.com/read/20181231/537/874159/targetkan-uhc-95-pada-2019-bali-anggarkan-rp495-miliar-untuk-pembiayaan-jkn pada tanggal 14 Oktober 2020.

Yuniati, M. (2020). Analisis Ekonomi Angkatan Kerja Perempuan Berdasarkan Tingkat Pendidikan Diploma dan Universitas di Provinsi NTB Tahun 2016 - 2018. Jurnal Bina Ilmiah, 14(6), 2703-2710. DOI : https://doi.org/10.33758/mbi.v14i6.416

Downloads

Published

2020-12-23

How to Cite

Raihananda, Q. ., Putra, I. W. E. D. ., Sijabat, M. S. ., Rofatunnisa, S. ., Maruf, I. ., Hermarwan, H., & Nooraeni, R. . (2020). Application of Random Forest Method Classification to Predict BPJS Kesehatan Card Users Who Receive Contribution Assistance in Karangasem District, Bali Province 2017. Jurnal Matematika, Statistika Dan Komputasi, 17(2), 178-188. https://doi.org/10.20956/jmsk.v17i2.11710

Issue

Section

Research Articles

Most read articles by the same author(s)