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All Journal JIKTEKS : Jurnal Ilmu Komputer dan Teknologi Informasi
Jefania Tilman Soares
Universita Timor
Computer Science & IT

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Analisis Perbandingan Algoritma K-Means dan K-Medoids dalam Penentuan Status Gizi Balita Krisantus uamrto Tey Seran; Jefania Tilman Soares; Fetronela Rambu Bobu; Debora Chrisinta
JIKTEKS : Jurnal Ilmu Komputer dan Teknologi Informasi Vol. 4 No. 02 (2026): April
Publisher : Faatuatua Media Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70404/jikteks.v4i02.648

Abstract

Nutritional status in toddlers is an important indicator in determining child growth and development quality. Inaccurate classification of nutritional status can affect early intervention efforts. This study aims to compare the performance of K-Means and K-Medoids algorithms in clustering toddler nutritional status data at Puskesmas Betun. The dataset consists of 1,036 toddler records with variables including age, weight, height, and mid-upper arm circumference (MUAC). Data preprocessing was conducted through normalization before clustering. The performance of both algorithms was evaluated using the Davies Bouldin Index (DBI). The results show that K-Means converged in 24 iterations with a DBI value of 1.0281, while K-Medoids converged in 6 iterations with a DBI value of 1.1236. Based on the DBI evaluation, K-Means produced better clustering performance compared to K-Medoids. Therefore, K-Means is more suitable for determining toddler nutritional status in this study.
Co-Authors Debora Chrisinta Fetronela Rambu Bobu Tey Seran, Krisantus Jumarto