GOVERNMENT POLICIES MODELING IN CONTROLLING INDONESIA'S COVID-19 CASES USING DATA MINING

  • Ultach Enri Universitas Singaperbangsa Karawang
  • Eka Puspita Sari Universitas Bina Sarana Informatika
DOI: https://doi.org/10.33480/pilar.v17i1.2206
Keywords: government policies, covid-19, data mining

Abstract

Since the positive case of covid-19 in Indonesia, the government has taken several policies with the purpose of controlling the spread of the covid-19 virus, which has been regulated in Government Regulation No. 21 of 2020.  The purpose of research is to obtain a model of government policy in controlling cases of covid by using data mining classification techniques, and obtain attributes that have the greatest weight, as well as look at the impact of policies that have been carried out by the government on the cases of covid-19 in Indonesia. The methodology used in the research is Knowledge Discovery In Database (KDD). Based on the research that has been done, it can be concluded that the policies that have been done by the government in controlling cases of covid-19 can be said to be successful, the C4.5 algorithm is the algorithm that gives the best results compared to the Deep Learning algorithm, as well as the attribute that has the greatest weight is cancel public events. Secondary data will be used in this research.

Downloads

Download data is not yet available.

References

Amelia, Y., Eosina, P., & Setiawan, F. A. (2018). Perbandingan Metode Deep Learning Dan Machine Learning Untuk Klasifikasi (Uji Coba Pada Data Penyakit Kanker Payudara). Seminar Nasional Teknologi Informasi, 1, 789–796.

Anung Ahadi Pradana, Casman, N. (2020). Pengaruh Kebijakan Social Distancing pada Wabah COVID-19 terhadap Kelompok Rentan di Indonesia. Jurnal Kebijakan Kesehatan Indonesia : JKKI, 9(2), 61–67. Retrieved from https://jurnal.ugm.ac.id/jkki/article/view/55575

Bahri, S., Marisa Midyanti, D., Hidayati, R., Sistem Komputer, J., & Mipa, F. (2018). Perbandingan Algoritma Naive Bayes dan C4.5 Untuk Klasifikasi Penyakit Anak. Seminar Nasional Aplikasi Teknologi Informasi (SNATi), 24–31.

Benhar, H., Idri, A., & L Fernández-Alemán, J. (2020). Data preprocessing for heart disease classification: A systematic literature review. Computer Methods and Programs in Biomedicine (Vol. 195). https://doi.org/10.1016/j.cmpb.2020.105635

Gorunescu, F. (2011). Data Mining Concepts, Models and Technique. Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-19721-5

Hijrah, Mukhlizar, M., & Pandria, T. M. A. (2020). Perbandingan Teknik Klasifikasi Untuk Memprediksi Kualitas Kinerja Karyawan. Jurnal Optimalisasi, 6(1), 10–21. Retrieved from http://jurnal.utu.ac.id/joptimalisasi/article/view/1990

Ibrahim, D. (2017). Analisis Hubungan antar Faktor dan Komparasi Algoritma Klasifikasi pada Penentuan Penundaan Penerbangan. Senit, (September), 15–17.

Idri, A., Benhar, H., Fernández-Alemán, J. L., & Kadi, I. (2018). A systematic map of medical data preprocessing in knowledge discovery. Computer Methods and Programs in Biomedicine, 162, 69–85. https://doi.org/10.1016/j.cmpb.2018.05.007

Lengkong, N. C., Safitri, O., Machsus, S., Putra, Y. R., Syahadati, A., & Nooraeni, R. (2021). Analisis Sentimen Penerapan Psbb Di Dki Jakarta Dan Dampaknya Terhadap Pergerakan Ihsg. Jurnal Teknoinfo, 15(1), 20. https://doi.org/10.33365/jti.v15i1.866

Murphree, D. H., Puri, P., Shamim, H., Bezalel, S. A., Drage, L. A., Wang, M., … Comfere, N. (2020). Deep Learning for Dermatologists: Part I Fundamental Concepts. Journal of the American Academy of Dermatology. https://doi.org/10.1016/j.jaad.2020.05.056

Mutrofin, S., Machfud, M. M., Satyareni, D. H., Ginardi, R. V. H., & Fatichah, C. (2020). Komparasi Kinerja Algoritma C4.5, Gradient Boosting Trees, Random Forests, dan Deep Learning pada Kasus Educational Data Mining. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(4), 807. https://doi.org/10.25126/jtiik.2020742665

Noviandi. (2018). Implementasi Algoritma Decision Tree C4.5 Untuk Prediksi Penyakit Diabetes. Inohim, 6(1), 1–5.

Oxford University. (2021). Coronavirus Government Response Tracker | Blavatnik School of Government (ox.ac.uk). Retrieved February 26, 2021, from https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker

Parhusip, H. A. (2020). Study on COVID-19 in the World and Indonesia Using Regression Model of SVM, Bayesian Ridge and Gaussian. Jurnal Ilmiah Sains, 20(2), 49. https://doi.org/10.35799/jis.20.2.2020.28256

Rohman, A., Suhartono, V., & Supriyanto, C. (2017). Penerapan Agoritma C4.5 Berbasis Adaboost Untuk Prediksi Penyakit Jantung. Jurnal Teknologi Informasi, 13, 13–19.

Santosa, B., & Ardian, U. (2018). Data Mining dan Big Data Analytics. Yogyakarta: Penebar Media Pustaka.

Wahono, H., & Riana, D. (2020). Prediksi Calon Pendonor Darah Potensial Dengan Algoritma Naïve Bayes, K-Nearest Neighbors dan Decision Tree C4.5. JURIKOM (Jurnal Riset Komputer), 7(1), 7. https://doi.org/10.30865/jurikom.v7i1.1953

Published
2021-03-05
How to Cite
Enri, U., & Sari, E. (2021). GOVERNMENT POLICIES MODELING IN CONTROLLING INDONESIA’S COVID-19 CASES USING DATA MINING. Jurnal Pilar Nusa Mandiri, 17(1), 67-72. https://doi.org/10.33480/pilar.v17i1.2206
Issue
Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Period for March 2021
Section
Articles

Copyright (c) 2021 Ultach Enri, Eka Puspita Sari

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:

  1. Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
  2. Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
  3. Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
    Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.