<![CDATA[Order cancellation is a significant issue for e-commerce platforms because it can result in revenue loss, increased operational costs, and reduced transaction management efficiency. This study aims to compare the performance of Random Forest and Extreme Gradient Boosting (XGBoost) in predicting customer order cancellations on Shopee and to interpret the factors contributing to the prediction outcomes using an Explainable Artificial Intelligence approach based on SHapley Additive exPlanations (SHAP). The study employed the Shopee Consumer Behaviour Cancellation Order Analysis dataset obtained from Kaggle. The research process consisted of data preprocessing, dataset partitioning using the Stratified Train-Test Split method with an 80:20 ratio, implementation of both algorithms, model evaluation using Accuracy, Precision, Recall, F1-Score, and Receiver Operating Characteristic–Area Under Curve (ROC-AUC), followed by model interpretation using SHAP. The evaluation results indicate that Random Forest achieved better performance across most evaluation metrics, while XGBoost obtained a slightly higher ROC-AUC value. SHAP analysis identified Total Discount, Buyer-Paid Shipping Cost, Estimated Shipping Cost, and Estimated Shipping Fee Deduction as the variables contributing most to order cancellation predictions.These findings indicate that combining predictive algorithms with an Explainable Artificial Intelligence approach not only supports the classification of order cancellations but also provides insights into the factors influencing prediction outcomes, which can serve as a consideration for reducing order cancellations on e-commerce platforms.]]>
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