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

TULJANNAH, ISNANIA and Yusliani, Novi and Rachmatullah, M. Naufal (2025) CLUSTERING MENGGUNAKAN METODE K-MEDOIDS PADA ARTIKEL BERITA ONLINE BERBAHASA INDONESIA. Undergraduate thesis, Sriwjaya Unversity.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Subjects:	Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Isnania Tuljannah
Date Deposited:	22 Sep 2025 01:41
Last Modified:	22 Sep 2025 01:41
URI:	http://repository.unsri.ac.id/id/eprint/184453

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