Classification of Covid-19 Patient Spread Rate By Age and Region With K-Means Algorithm
DOI:
10.33395/sinkron.v7i3.11603Keywords:
Spread, Covid-19, Data Mining, Classification, K-Means AlgorithmAbstract
The Covid-19 virus is a new type of disease, the first case of covid-19 was found in Wuhan Province, China in 2019 with general symptoms such as pneumonia. This virus can grow rapidly and can cause serious infections and even death. Due to the very fast transmission of the virus, the WHO declared the Covid-19 virus a pandemic on March 11, 2020. Anyone can be infected with the covid-19 virus, from small children to the elderly. However, various ways have been done, but the cases of covid-19 continue to increase. Various ways have been done to reduce the spread of COVID-19 so that the Covid-19 virus does not spread quickly. Then data mining techniques are needed by implementing the K-Means algorithm because the K-Means algorithm can group data. In this study, 790 patient data were used for COVID-19 patients. The test resulted in 3 clusters grouped based on low, medium, and high categories with a DBI value of -0.332. In cluster 0 with a low category there are 3 districts, in cluster 1 with a medium category there is 1 sub-district, in cluster 2 with a high category, there are 6 districts. From the results of the test, it can be seen that the age susceptible to COVID-19 is 26 to 45 years.
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Abdullah, D., Susilo, S., Ahmar, A. S., Rusli, R., & Hidayat, R. (2022). The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data. Quality and Quantity, 56(3), 1283–1291. https://doi.org/10.1007/s11135-021-01176-w
Alvina Felicia Watratan, Arwini Puspita. B, & Dikwan Moeis. (2020). Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia. Journal of Applied Computer Science and Technology, 1(1), 7–14. https://doi.org/10.52158/jacost.v1i1.9
Arifandi, M. H. A. H. A. A. D. A. D. (2021). Implementasi algoritma K-Medoids untuk clustering wilayah terinfeksi kasus COVID-19 di DKI Jakarta. JTT (Jurnal Teknologi Terapan), 7(2), 120–128. https://jurnal.polindra.ac.id/index.php/jtt/article/view/353
Darmansah, D. D. (2021). Analisis Penyebaran Penularan Virus Covid-19 di Provinsi Jawa Barat Menggunakan Algoritma K-Means Clustering. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 8(3), 1188–1199. https://doi.org/10.35957/jatisi.v8i3.1034
Dwitri, N., Tampubolon, J. A., Prayoga, S., R.H Zer, F. I., & Hartama, D. (2020). Penerapan Algoritma K-Means Dalam Menentukan Tingkat Penyebaran Pandemi Covid-19 Di Indonesia. Jurnal Teknologi Informasi, 4(1), 128–132. https://doi.org/10.36294/jurti.v4i1.1266
Gayatri, L., & Hendry, H. (2021). Pemetaan Penyebaran Covid-19 Pada Tingkat Kabupaten/Kota Di Pulau Jawa Menggunakan Algoritma K-Means Clustering. Sebatik, 25(2), 493–499. https://doi.org/10.46984/sebatik.v25i2.1307
Gunawan, I., Anggraeni, G., Rini, E. S., & Mustofa, Y. (2020). Klasterisasi provinsi di Indonesia berbasis perkembangan kasus Covid-19 menggunakan metode K-Medoids. Seminar Nasional Matematika Dan Pendidikan Matematika (5thSENATIK), 301–306.
Lestandy, M., & Syafa’ah, L. (2020). Prediksi Kasus Aktif Covid-19 Menggunakan Metode K-Nearest Neighbors. Seminar Nasional Teknologi Dan Rekayasa (SENTRA) 2020, 45–48.
Muliono, R., & Sembiring, Z. (2019). Data Mining Clustering Menggunakan Algoritma K-Means Untuk Klasterisasi Tingkat Tridarma Pengajaran Dosen. CESS (Journal of Computer Engineering, System and Science), 4(2), 2502–2714.
Mustaghfiroh, L., Ariani, M. H., Info, A., Neighbor, K., Mustaghfiroh, L., Informatika, P. S., Informatika, F. T., Tinggi, S., & Pati, T. (2022). KLASIFIKASI PASIEN COVID-19 DI INDONESIA MENGGUNAKAN METODE K-NEAREST NEIGHBOR. Jurnal Nasional AMRI (Analisa, Metode, Rekayasa, Informatika), 1(1), 16–21. https://doi.org/10.12487/AMRI.v1i1.xxxxx
R Sianipar, K. D., Wanti Siahaan, S., Siregar, M., & Fikrul Ilmi Zer, P. R. (2020). Penerapan Algoritma K-Means Dalam Menentukan Tingkat Kepuasan Pembelajaran Online Pada Masa Pandemi Covid-19. Jurnal Teknologi Informasi, 4(1), 101–105.
Salsabila, F., & Intani, S. M. (2021). Implementasi Algoritma K-Means Dan C4.5 Dalam Menentukan Tingkat Penyebaran Covid-19 Di Indonesia. Jurnal Siliwangi, 7(1), 25–30.
Sari, Y. P., Primajaya, A., & Irawan, A. S. Y. (2020). Implementasi Algoritma K-Means untuk Clustering Penyebaran Tuberkulosis di Kabupaten Karawang. INOVTEK Polbeng - Seri Informatika, 5(2), 229. https://doi.org/10.35314/isi.v5i2.1457
Sindi, S., Ningse, W. R. O., Sihombing, I. A., R.H.Zer, F. I., & Hartama, D. (2020). Analisis Algoritma K-Medoids Clustering Dalam Pengelompokan Penyebaran Covid-19 Di Indonesia. Jurnal Teknologi Informasi, 4(1), 166–173. https://doi.org/10.36294/jurti.v4i1.1296
Sugianto, C. A., Rahayu, A. H., & Gusman, A. (2020). Algoritma K-Means untuk Pengelompokkan Penyakit Pasien pada Puskesmas Cigugur Tengah. Journal of Information Technology, 2(2), 39–44. https://doi.org/10.47292/joint.v2i2.30
Sunia, D., Kurniabudi, & Alam Jusia, P. (2019). Penerapan Data Mining Untuk Clustering Data Penduduk Miskin Menggunakan Algoritma K-Means. Jurnal Ilmiah Mahasiswa Teknik Informatika, 1(2), 121–134.
Suriani, L. (2020). Pengelompokan Data Kriminal Pada Poldasu Menentukan Pola Daerah Rawan Tindak Kriminal Menggunakan Data Mining Algoritma K-Means Clustering. Jurnal Sistem Komputer Dan Informatika (JSON), 1(2), 151. https://doi.org/10.30865/json.v1i2.1955
Sy, H., Rismayani, & Syam, A. (2019). Data Mining Menggunakan Algoritma K-Means Pengelompokan Penyebaran Diare di Kota Makassar. SISITI : Seminar Ilmiah Sistem Informasi Dan Teknologi Informasi, 8(1), 73–82.
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Copyright (c) 2022 Adya Zizwan Putra, Ryan Wijaya Pinem, Sehat Silalahi, Fendianu Gulo, Juan Antonio Adityo Liukhoto
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