A Hybrid Clustering–Classification Framework for SMEs Success Level Prediction - Saputra, Andika Dermawan; Yustanti, Wiyli (Universitas Negeri Surabaya, 2025)
Micro, Small, and Medium Enterprises (SMEs) are vital to economic growth, yet their complex success determinants necessitate advanced predictive modeling. This study proposes a hybrid clustering-classification framework to classify and predict SME success levels based on 22 multidimensional indicators, including financial literacy, FinTech adoption, and entrepreneurial resilience. K-Means clustering was first applied to the survey data, yielding three optimal success personas, validated by the highest Silhouette Score (0.5238). These clusters were labeled with Beginner and Conventional, Stable Digital Adopter, and Digital Innovator SMEs. These empirically derived clusters served as pseudo-labels for the classification stage. Classification algorithms were tested with and without the Synthetic Minority Oversampling Technique (SMOTE). While ensemble methods (Random Forest, LightGBM) and SVM performed well, the K-Nearest Neighbors (KNN) algorithm consistently outperformed all others, achieving the highest F1-Score (0.9324) under SMOTE implementation. The findings validate the effectiveness of the hybrid clustering-classification approach in accurately mapping and predicting SME success levels. The resulting model serves as a robust, data-driven tool for policymakers to guide targeted interventions and digital training programs, fostering sustainable SME development.