Segmentation and Prediction of Store Performance on the Shopee Marketplace Using a Hybrid Clustering Approach, Spatial Analysis, and Feature Importance
DOI:
https://doi.org/10.32664/j-intech.v14i01.2256Keywords:
Data Mining, e-commerce, K-Means Clustering, random forest, marketplace analyticsAbstract
Marketplace platforms have become a central component of digital commerce, particularly in Southeast Asia where Shopee has emerged as one of the dominant e-commerce ecosystems. This study aims to analyze and predict the performance of Shopee stores using a hybrid data mining approach integrating clustering, spatial analysis, and classification. The dataset consists of 655 Shopee stores collected on February 18, 2026. K-Means clustering is applied to segment store performance, while spatial analysis examines geographic distribution patterns. Furthermore, a Random Forest classifier is used to predict performance categories and identify influential features. The clustering results reveal three distinct store performance groups with a Silhouette Score of 0.6704, indicating a good cluster structure. Although K = 2 produced a higher score (0.7891), K = 3 was selected to provide more meaningful segmentation (low, medium, and high performance). The Random Forest model achieved an accuracy of 79%, with precision, recall, and F1-score demonstrating reliable predictive performance across all classes. Feature importance analysis shows that promotional activity, chat responsiveness, and follower count significantly influence store performance classification. Spatial analysis indicates that provinces such as West Java and Jakarta dominate high-performance clusters. The findings contribute to hybrid data mining frameworks and provide practical insights for improving seller competitiveness in digital commerce ecosystems.
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