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All Journal Indonesian Journal of Applied Technology and Innovation Science
Elsa Setiawati
Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Implementation of K-Means, K-Medoid and DBSCAN Algorithms In Obesity Data Clustering Elsa Setiawati; Ustara Dwi Fernanda; Suci Agesti; Muhammad Iqbal; Muhammad Okten Adetama Herjho
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 1 (2024): IJATIS February 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i1.1109

Abstract

Obesity is an excessive accumulation of body fat and can be harmful to health. This study aims to understand the patterns and relationships between obesity data that have been obtained, so a data clustering step will be carried out using the K-Means, K-Medoid and DBSCAN algorithms. This study utilizes the Davies Bouldin Index (DBI) to determine the best cluster value comparison and validated. So the results of the best cluster value in processing obesity data are using the K-Means K2 algorithm with a value of 0.604. The K-Medoid algorithm obtained the best cluster k2, with a DBI value of around 0.614. and the DBSCAN algorithm clustering trial K3, with a value of 1.040. Thus in this study the comparison results of the application of 3 clustering algorithms, the results obtained are the K-Means algorithm shows the value of the resulting cluster is the best of other algorithms in clustering obesity data with a value of 0.604.
Co-Authors Muhammad Iqbal Muhammad Okten Adetama Herjho Suci Agesti Ustara Dwi Fernanda