COMPARATIVE ANALYSIS OF TREE-BASED ALGORITHMS FOR CUSTOMER SATISFACTION CLASSIFICATION IN THE LOGISTICS INDUSTRY: A CASE STUDY OF JNE AND J&T EXPRESS
Published:
2026-03-18Downloads
Abstract
The rapid growth of Indonesia’s e-commerce sector has intensified competition within the logistics industry, positioning customer satisfaction as a critical determinant of competitive advantage. However, the multidimensional and non-linear nature of service quality complicates traditional statistical analysis. This study aims to compare the performance of three tree-based machine learning algorithms (Decision Tree, Random Forest, and Gradient Boosting) in classifying customer satisfaction for JNE and J&T Express, while identifying the key service quality dimensions driving satisfaction. Using a validated dataset of 408 respondents, individual service indicators are modeled as predictive features. Hyperparameter tuning is conducted through 500-iteration Randomized Search with 5-fold cross-validation. The results show that the Decision Tree achieves the highest performance for the JNE dataset with an accuracy of 78.05%, precision of 79.16%, recall of 78.05%, and an F1-score of 77.84%. In contrast, Gradient Boosting outperforms other models for the J&T Express dataset with an accuracy of 81.71%, precision of 81.69%, recall of 81.71%, and an F1-score of 81.37%. Furthermore, Feature Importance analysis consistently identifies Shipping Cost as the dominant predictor of satisfaction. These findings highlight the efficacy of tree-based machine learning in decoding complex satisfaction patterns, offering actionable, data-driven insights for logistics service providers.
Keywords:
Customer Satisfaction Decision Tree Gradient Boosting Random Forest Service QualityReferences
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