Evaluation of Multi-Algorithm Clustering for Marketplace MSME Segmentation Using a Big Data Analytics Approach

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.