<![CDATA[Hotel booking cancellations are a critical problem in hotel revenue management because they can cause operational inefficiencies and financial losses. This study develops an explainable cancellation prediction model using CatBoost integrated with SHAP, based on real-world Property Management System (PMS) operational data from a budget hotel in Central Java, Indonesia. The dataset spans 67 months (October 2020–April 2026) and, after preprocessing and data cleaning, yields 74,826 independent reservation records from 80,110 raw entries. The cancellation rate is extremely low (1.56%), creating a severe class imbalance challenge. Instead of synthetic oversampling, the proposed method applies cost-sensitive learning via CatBoost’s scale_pos_weight, computed from the natural class ratio. Model performance is evaluated using hold-out validation (80% training, 20% testing). The proposed CatBoost model achieves an F1-score of 72.04%, with precision of 87.20% for the cancellation class and an AUC-ROC of 0.86, outperforming Random Forest and XGBoost baselines. SHAP analysis indicates that lead time, deposit type, and arrival month are the most influential features driving cancellation predictions. These findings support early warning decision-making for risk mitigation in hotel operations.]]>
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