Implementation of K-Means, K-Medoid and DBSCAN Algorithms In Obesity Data Clustering

Elsa Setiawati (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Ustara Dwi Fernanda (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Suci Agesti (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Muhammad Iqbal (Al-Azhar University, Egypt)
Muhammad Okten Adetama Herjho (Al-Azhar University, Egypt)

Indonesian Journal of Applied Technology and Innovation Science
Vol. 1 No. 1 (2024): IJATIS February 2024

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.