Skripsi

COMPARISON OF CLUSTERING MODELS FOR GROUPING LIFESTYLE PATTERNS AND OBESITY FACTORS

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Obesity is a growing global health issue and a major risk factor for various chronic diseases. This study aims to compare the performance of three clustering methods K-Means, Agglomerative Clustering, and Gaussian Mixture Model (GMM) in grouping lifestyle patterns and obesity-related factors. The research uses the Food Nutrition dataset containing variables on dietary habits, physical activity, and socio-economic conditions. Evaluation was conducted using Silhouette Score, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI) to measure clustering quality. The results indicate that K-Means achieved the best performance with the highest Silhouette Score and the most clearly separated cluster structure. Meanwhile, GMM demonstrated greater flexibility in handling non-spherical data distributions, whereas Agglomerative Clustering showed higher overlap between clusters. These findings suggest that the selection of a clustering method should align with the characteristics of the dataset. Overall, this study provides a data-driven foundation for developing public health intervention strategies aimed at obesity prevention.

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Detail Information

Series Title

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Call Number

T1895952025

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xii, 54 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

616.390 7

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Informasi
Lifestyle Patterns--Obesity Factors

Specific Detail Info

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Statement of Responsibility

MI

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