Pendekatan Unsupervised learning dalam Segmentasi Kesehatan: Perbandingan K-Means dan DBSCAN
Sari
Segmentasi kesehatan berbasis data pemeriksaan medis penting untuk mendukung strategi pencegahan penyakit. Penelitian ini membandingkan metode clustering K-Means dan DBSCAN menggunakan Silhouette Score dan Davies-Bouldin Index. Hasil menunjukkan bahwa K-Means dengan 8 cluster memberikan performa terbaik dengan Silhouette Score 0.2972 dan Davies-Bouldin Index 1.2934, dibandingkan konfigurasi lainnya. DBSCAN memperoleh Silhouette Score 0.2837, menunjukkan pendekatan berbasis densitas juga efektif dalam pengelompokan data. Dengan hasil ini, K-Means dengan 8 cluster dipilih sebagai metode terbaik untuk segmentasi kesehatan dalam penelitian ini. Temuan ini dapat mendukung analisis data medis untuk pencegahan penyakit yang lebih efektif dan personal.
Kata kunci: Segmentasi Kesehatan, Clustering, K-Means, DBSCAN, Silhouette Score, Davies-Bouldin Index
Abstract
Health segmentation based on medical examination data plays a crucial role in supporting disease prevention strategies. This study compares K-Means and DBSCAN clustering methods, evaluated using Silhouette Score and Davies-Bouldin Index, to identify the most effective segmentation approach. Experimental results indicate that K-Means with 8 clusters achieves the best performance, yielding a Silhouette Score of 0.2972 and a Davies-Bouldin Index of 1.2934, outperforming other configurations. Meanwhile, DBSCAN attains a Silhouette Score of 0.2837, demonstrating the efficacy of density-based clustering in handling medical data. Based on these findings, K-Means with 8 clusters emerges as the most optimal method for health segmentation in this study. These insights contribute to the advancement of data-driven disease prevention strategies and personalized healthcare management..
Keywords: Health Segmentation, Clustering, K-Means, DBSCAN, Silhouette Score, Davies-Bouldin Index
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DOI: https://doi.org/10.26760/mindjournal.v10i1.99-113
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