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All Journal MIND (Multimedia Artificial Intelligent Networking Database) Journal Computer Science (CO-SCIENCE)
MALIK, KARENINA NURMELITA
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Computer Science & IT

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Pendekatan Unsupervised learning dalam Segmentasi Kesehatan: Perbandingan K-Means dan DBSCAN MASRURIYAH, ANIS FITRI NUR; MARDIAH, MARDIAH; ANANDA, MUHAMMAD DWI; MALIK, KARENINA NURMELITA
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 1 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i1.99-113

Abstract

AbstrakSegmentasi 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 IndexAbstractHealth 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
Studi Komparatif Algoritma K-Means dan K-Medoids untuk Segmentasi Informasi Kesehatan Ananda, Muhammad Dwi; Malik, Karenina Nurmelita; Masruriyah, Anis Fitri Nur; Mardiah, Mardiah
Computer Science (CO-SCIENCE) Vol. 5 No. 2 (2025): Juli 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i2.9207

Abstract

In analyzing medical data to support clinical decisions, segmentation of health information plays a crucial role. This study presents a comparative analysis of K-Means and K-Medoids algorithms in clustering Medical Examination data. This evaluation is conducted using two main internal approaches, namely Silhouette Score and Davies-Bouldin Index in measuring the quality of separation as well as cohesion between clusters. The experiment involved varying the number of clusters to determine the optimal configuration of each algorithm. The results show that K-Means provides representative performance and is more stable against data complexity, compared to the K-Medoids algorithm which is only optimal in a small number of clusters. Statistical analysis using one-way ANOVA was applied to test the significance of performance differences between algorithms based on the average Silhouette Score value, yielding an F-value of 4.8594 with a P-value of 0.0447. This indicates that the performance difference between the two algorithms is statistically significant at 5% significance rate. This research confirms the K-Means algorithm for segmenting health data with diverse distributions and is expected to serve as a foundation for the development of more efficient health data classification systems in the future.
Co-Authors ANANDA, MUHAMMAD DWI Fitri Nur Masruriyah, Anis Mardiah Mardiah