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CLUSTERING MENGGUNAKAN METODE K-MEDOIDS PADA ARTIKEL BERITA ONLINE BERBAHASA INDONESIA

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The rapid growth of Indonesian online news articles presents challenges in clustering based on content similarity. This study aims to develop a clustering system for online news articles using the k-medoids method. A total of 500 articles from Kaggle were processed through text preprocessing (case folding, tokenizing, stopword removal, and stemming), TF-IDF weighting, and clustering with k-medoids. Evaluation using the davies bouldin index (DBI) showed the optimal result at 10 clusters with a DBI value of 8.2918. Each cluster represented specific themes such as health, politics, economy, entertainment, culture, and tourism. Wordcloud visualization and word frequency analysis strengthened topic interpretation. The findings demonstrate that k-medoids is effective in clustering Indonesian online news articles and supports text-based information analysis.

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Detail Information

Series Title

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Call Number

T1844532025

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, VI-2 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Kecerdasan Buatan
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

SEW

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