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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Taufan, Anas
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : INOVTEK Polbeng - Seri Informatika

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Evaluation of Multi-Algorithm Clustering for Marketplace MSME Segmentation Using a Big Data Analytics Approach Taufan, Anas; Redjeki, Sri
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/k9gn7n75

Abstract

The rapid development of the digital economy has significantly driven MSME activity on marketplaces like Tokopedia, generating vast heterogeneous datasets. This study conducted a comparative evaluation of six clustering algorithms, including K-Means, Agglomerative Clustering, and GMM, using the Silhouette Score, Davies–Bouldin Index (DBI), and Calinski–Harabasz Index (CHI). Using a standardized Tokopedia MSME dataset from Yogyakarta, empirical results showed Silhouette scores ranging from 0.050 to 0.057, DBI from 0.45 to 0.53, and CHI from 950 to 1310. Although indicating low absolute cluster separation, these values facilitated meaningful relative comparisons. Among the tested algorithms, agglomerative clustering with Ward linkage demonstrated the best relative performance and consistency. Metric variability was examined through multiple runs to ensure stability. The analysis identified three segments: high-performing, medium-performing, and high-potential MSMEs, serving as a foundation for data-driven strategies. These findings underscore the necessity of a consistent multi-metric evaluation approach in MSME big data clustering studies.
Co-Authors Almagribi, Ahmad Bilal Ardianto, Fikky Domy Kristomo, Domy Sri Redjeki