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Muhammad Sukri
Universitas Mataram
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Journal : JOURNAL OF SCIENCE AND SOCIAL RESEARCH

 1 
ANALISIS TINGKAT KEPARAHAN COVID-19 DI SUATU NEGARA MENGGUNAKAN METODE K-MEDOID CLUSTERING Amelia, Syahputri; Yusri, Eldo; Irwansyah, Bambang; Andira, Ayu; Sukri, Muhammad; Yafi, Muhammad Fauzan; Affandi, Muhammad
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5787

Abstract

Abstract: This study aims to classify the severity of COVID-19 cases based on patient and region data using the K-Medoid Clustering method. COVID-19 has varying degrees of symptom severity, requiring cluster analysis to identify severity patterns to support decision-making in healthcare resource allocation and policy formulation. The data used included the number of positive cases, recovered cases, deaths, the average age of patients, and comorbidity levels. The results showed that the K-Medoid method was able to effectively cluster the data. In the raw dataset, the percentage of patients not infected with COVID-19 was 62.62%, while the percentage of infected patients was 37.38%. Based on sample characteristics, non-obese patients accounted for 74.54%, obese patients 25.46%, and patients with a combination of obesity and cardiovascular disease 0.57%. Keywords: Covid-19, Severity, K-Medoid Clustering, Data Mining Abstrak: Penelitian ini bertujuan untuk mengelompokkan tingkat keparahan kasus COVID-19 berdasarkan data pasien dan wilayah menggunakan metode K-Medoid Clustering. COVID-19 memiliki variasi tingkat keparahan gejala, sehingga diperlukan analisis klaster untuk mengidentifikasi pola keparahan yang mendukung pengambilan keputusan dalam alokasi sumber daya kesehatan dan perumusan kebijakan. Data yang digunakan meliputi jumlah kasus positif, kasus sembuh, kasus meninggal, usia rata-rata pasien, serta tingkat komorbiditas. Hasil penelitian menunjukkan bahwa metode K-Medoid mampu melakukan pengelompokan data secara efektif. Pada dataset mentah, persentase pasien tidak terjangkit COVID-19 sebesar 62,62%, sedangkan pasien terjangkit sebesar 37,38%. Berdasarkan karakteristik sampel, pasien non-obesitas memiliki persentase 74,54%, pasien obesitas 25,46%, dan pasien dengan kombinasi obesitas serta penyakit kardiovaskular sebesar 0,57%. Kata Kunci : Covid-19, Tingkat Keparahan, K-Medoid Clustering, Data Mining
Co-Authors Affandi, Muhammad Ahlun Ansar Ahmad Sirulhaq Al Faruq, Suhaib Amelia, Syahputri Andriansyah Andriansyah Anugrah, Alfiani Ariani Saputri , Chandra Arismunandar Arismunandar Asrin Asrin Aswandikari Atmanegara, Lalu Kusnendar Ayu Andira Ayuni, Ervina Azzara Azwan, Anisah Badruddin, Kartini Budirman Bachtiar Bukran Habibullah Burhan Burhan Burhanuddin Burhanuddin Burhanuddin Burhanuddin Darma, Sidrah Darmiany Efendi, Satria Ghiat Aditiya Ramdani Haeria Haeria Hafis Bin Amat Simin, Mohamad Harahap, Muhammad Ikhsan Hartini, Dina Hasibuan, Amnah Faridah Imamul Haramain Irma Setiawan, Irma Irundu, Daud Irwansyah, Bambang Ishak Hariyanto, Ishak Jannah, Nisa Ul Johan Mahyudi Mahyudi Johan Mahyudi, Johan Jurnal Pepadu Kaharuddin Kamil, Abdul Khairul Paridi Mahmudi Effendi Mahsun Mahsun Mahsun Mahsun Mahsun Mahyuni Mahyuni Maylani, Rani Moch Asyar Mr. Mukrimin Muhaimin, Ahmad Helmi Muhammad Hamdi Mulyadi, Irvan Musaddat, Syaiful Muzianti, Yeni N. Nurjanah Nabila, Fadya Nasaruddin Ali Nia Agustina Nita Wijayanti, Ni Putu Nur Istiqomah Nurhayati Nurhayati Nyoman Yudika Qadhawijayanti , Suci Raden Mohamad Herdian Bhakti Rahmad Hidayat Rusdiawan Rusdiawan S, Aswandikari Saharuddin Saharudin Saharudin Saharudi Saharudin Saharudin Solihan, Solihan Sri Ramdhani, Andi Suarni Suarni sukri Sukri, sukri Syahbudin Syahbudin Syahriani Syahriani, Syahriani syarifuddin Tiara Putri Suciana Wawan Irawan, Wawan Wini Erlina Yafi, Muhammad Fauzan Yeni Muzianti Yusri, Eldo Zainur, Zainur