DATA MINING UNTUK KLASIFIKASI STATUS PANDEMI COVID 19

  • Pastima Simanjuntak Universitas Putera Batam
  • Cosmas Eko Suharyanto Universitas Putera Batam
  • Sunarsan Sitohang Universitas Putera Batam
  • Koko Handoko Universitas Putera Batam

Abstract

We all know that since the number of Covid-19 cases has increased in Indonesia, many problems have arisen in society. Covid-19 has crippled the socio-economic conditions of all Indonesian people. As a result of the Covid-19 problem, the Indonesian government must establish policies such as issuing rules for social distancing, and also calling for Work From Home for those who work as employees, imposing restrictions on each region, building hospitals to handle Covid-19, and others. With the existence of government policy decisions will have an impact on all people, both the community. Socio-economic problems arise and have a direct impact on society. The purpose of this study is to prediction the covid 19 pandemic. This study applies data mining techniques with the naïve Bayes algorithm with software implementation using Tanagra software. The results of this study can be used to see the clustering pattern of the Covid 19 pandemic, and it can be seen from the results of the probabilities of the Covid data so far for the spread of Covid 19 which has 90.7% correct predictions and 9.3% wrong predictions.

References

[1] World Health Organization. COVID- 19: A global pandemic. European Chemical Bulletin; 2020.
[2] Kemenkes RI PHEOC. (2020).COVID19. Https://Infeksiemerging.Kemkes.Go.Id.
[3] Irawan, Hendra, (2020). “Inovasi Pendidikan Sebagai Antisipasi Penyebaran Covid-19”. Ombudsman.go.id.
[4] Adityo Susilo, C, dkk (2020). Coronavirus Disease 2019: Tinjauan Literatur Terkini. Jurnal. Penyakit Dalam Indonesia. Vol.7 No.1 Maret 2020.
[5] Akhmad. R. P and Fuad. D. H. (2020). Perbandingan Performa Teknik Sampling Data untuk Klasifikasi Pasien Terinfeksi Covid-19 Menggunakan Rontgen Dada. Journal of Applied Informatics and Computing (JAIC) Vol.5, No.1, Juli 2021, pp. 37~42.
[6] Diah Handayani, et.al. 2020. Penyakit Virus Corona 2019. Jurnal Respirologi Indonesia. Vol 40. No. 2, April 2020. Perhimpunan Dokter Paru Indonesia.
[7] Berrar, D. (2018). Bayes’ Theorem and Naive Bayes Classifier. Reference Module in Life Sciences, 1. https://doi.org/10.1016/B978-0-12-809633-8.20473-1.
[8] Annur, H. (2018). Klasifikasi Masyarakat Miskin Menggunakan Metode, 10, 160–165.
[9] Lee, C. H. (2018). An information-theoretic filter approach for value weighted classification learning in naive Bayes. Data and Knowledge Engineering, 113, 116–128. https://doi.org/10.1016/j.datak.2017.11.002.
[10] Wahyudi, E. N. (2013). Teknik Klasifikasi untuk Melihat Kecenderungan Calon Mahasiswa Baru dalam Memilih Jenjang Pendidikan Program Studi di Perguruan Tinggi, 18(1), 55–64.
[11] I. G. Bendesa Subawa, “Prediksi Kelulusan Mahasiswa Menggunakan Teorema Teorema Bayes,” JANAPATI, vol. 8, no. 3, pp. 227–236, 2019
[12] Kim, H. joon, Kim, J., Kim, J., & Lim, P. (2018). Towards perfect text classification with Wikipedia-based semantic Naïve Bayes learning. Neurocomputing, 315, 128–134. https://doi.org/10.1016/j.neucom.2018.07.002
[13] Manalu, E., Sianturi, F. A., & Manalu, M. R. (2017). Penerapan Algoritma Naive Bayes Untuk Memprediksi Jumlah Produksi Barang Berdasarkan Data Persediaan Dan Jumlah Pemesanan Pada Cv . Papadan Mama Pastries. Mantik Penusa, 1(2), 16–21.
[14] Meilani, Dwi, B., & Azmuri, W. (2015). Penentuan Pola Yang Sering Muncul Untuk Penerima Kartu Jaminan Kesehatan Masyarakat. Seminar Nasional “Inovasi Dalam Desain Dan Teknologi,” 424–431.
[15] Kustiyahningsih, Y., & Rahmanita, E. (2016). Aplikasi Sistem Pendukung Keputusan Menggunakan Algoritma C4.5. untuk Penjurusan SMA. Universitas Trunojoyo, 5(2), 101–108.
[16] Navia Rani, L. (2015). Klasifikasi Nasabah Menggunakan Algoritma C4.5 Sebagai Dasar Pemberian Kredit, 2(2), 33–38.
[17] Simanjuntak, P., & Elisa, E. (2016). Data Mining Untuk Pemilihan Celuler Card Di Kota Batam. Journal Information System Development, 4 (2), 1-5.
[18] Keputusan Menteri Kesehatan Republik Indonesia (2020). Pedoman Pencegahan Dan Pengendalian Coronavirus Disease 2019 (Covid-19). Biro Hukum Dan Sekretariat Menteri Kesehatan Republik Indonesia.
[19] Liliana D, Maulana H and Setiawan A. 2021. Data Mining untuk Prediksi Status Pasien Covid-19 dengan Pengklasifikasi Naïve Bayes. JURNAL MULTINETICS, 7(1), 48-53.
[20] Ahmad Firdaus, Miftahul Walid and Anwari. 2022. KLASIFIKASI KASUS COVID-19 MENGGUNAKAN MODEL NAIVE BAYES CLASSIFIER. Jurnal JATI, 6(2), 583-588.
Published
2022-12-31
How to Cite
SIMANJUNTAK, Pastima et al. DATA MINING UNTUK KLASIFIKASI STATUS PANDEMI COVID 19. Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 5, n. 2, p. 327-332, dec. 2022. ISSN 2621-3079. Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/620>. Date accessed: 19 july 2024. doi: https://doi.org/10.37600/tekinkom.v5i2.620.
Section
Articles