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All Journal MIND (Multimedia Artificial Intelligent Networking Database) Journal Computer Science (CO-SCIENCE)
ANANDA, MUHAMMAD DWI
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Computer Science & IT

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Journal : Computer Science (CO-SCIENCE)

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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 Fitri Nur Masruriyah, Anis MALIK, KARENINA NURMELITA Mardiah Mardiah