MSME Digital Transformation Readiness Prediction for Data-Driven Decision Support

Prediksi Kesiapan Transformasi Digital UMKM untuk Dukungan Pengambilan Keputusan Berbasis Data

Abstract

Digital transformation readiness among micro, small, and medium enterprises (MSMEs) varies across business sectors and organizational capabilities. This study develops a supervised classification model to predict MSME digital transformation readiness for data-driven decision support. Target labels were constructed from previous fuzzy clustering results and organized into three readiness levels: low, moderate, and high. The predictive model was built using Random Forest with business type, workforce transformation, dynamic capability, and SME performance as key predictors. Data preprocessing included categorical encoding, train-test separation, and class imbalance handling applied only to the training data to avoid data leakage. Model evaluation on the hold-out set produced 91.40% accuracy, 91.36% macro precision, 92.16% macro recall, and 91.72% macro F1-score. The confusion matrix showed that 85 of 93 test observations were correctly classified, with most errors occurring between adjacent readiness levels. Feature importance analysis indicated that dynamic capability was the most influential predictor, followed by workforce transformation and SME performance. The findings demonstrate that Random Forest can transform clustering-based insights into a practical predictive model for prioritizing MSME assistance, training, and digital development programs.