REGIONAL TYPOLOGY OF E-COMMERCE BUSINESS CONSTRAINTS IN INDONESIA: A MACHINE LEARNING APPROACH
Keywords:
E-commerce, machine learning, clustering analysis, regional typologyAbstract
This study analyzes regional disparities in e-commerce business constraints in Indonesia using an unsupervised machine learning approach. Using province-level data from Statistik E-Commerce 2024 published by Statistics Indonesia, the analysis covers 38 provinces. It examines seven major constraints, including funding limitations, skilled labor shortages, limited internet access, fraud, marketing challenges, delivery constraints, and other operational barriers. K-Means clustering with z-score standardization is applied to identify regional typologies of e-commerce business constraints. The optimal number of clusters is determined using the elbow method, the silhouette score, the Davies-Bouldin index, and the Calinski-Harabasz index. The results reveal five distinct regional clusters with different combinations of constraints. The findings show that provinces in Java and Bali are mainly constrained by capital and marketing pressures despite relatively advanced digital infrastructure. Several regions outside Java face balanced structural constraints involving multiple interrelated obstacles, while capital-heavy constraints dominate others. In contrast, Papua Pegunungan and Papua Tengah exhibit severe digital infrastructure constraints, indicating persistent digital divides. This study contributes by providing a province-level typology of e-commerce business constraints using official statistics and machine learning, offering a data-driven basis for designing region-specific strategies to support inclusive e-commerce development in Indonesia.
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