4
Volume
2
Issue
2026
Year
Abstract
Hotel booking cancellations are a major challenge in revenue management because they may cause operational inefficiencies and financial losses. This study develops a cancellation prediction model using CatBoost based on real-world Property Management System (PMS) data from a budget hotel in Central Java. The dataset covers 67 months, from October 2020 to April 2026. After preprocessing and data cleaning, 74,826 independent reservation records were obtained from 80,110 raw records. To address the extreme class imbalance, with a cancellation ratio of 1.56%, this study applied cost-sensitive learning through the scale_pos_weight parameter, which was set based on the class ratio without synthetic oversampling. SHAP was used to improve model interpretability. The proposed CatBoost model achieved an F1-score of 72.04%, precision of 87.20% for the cancellation class, and an AUC-ROC of 0.86, outperforming the baseline models. SHAP analysis indicates that lead time, deposit type, and arrival month were the main features contributing to cancellation predictions.
Keywords
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How to Cite
Sudana, I. M., & Pamungkas, E. W. (2026). Predicting Hotel Booking Cancellations Using CatBoost and SHAP: An Explainable AI Approach Based on 2020–2026 Operational Data. Computer Journal, 4(2), 328-343. https://doi.org/10.58477/cj.v4i2.511
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BASE Issue Information
| Volume | 4 |
| Issue | 2 |
| Year | 2026 |
| Published | 2026-08-30 |
| Pages | 328-343 |
| Section | Articles |
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Master of Informatics, Universitas Muhammadiyah Surakarta, Sukoharjo Regency, Central Java Province, Indonesia.
Master of Informatics, Universitas Muhammadiyah Surakarta, Sukoharjo Regency, Central Java Province, Indonesia.
| Article Title | Predicting Hotel Booking Cancellations Using CatBoost and SHAP: An Explainable AI Approach Based on 2020–2026 Operational Data |
| DOI | 10.58477/cj.v4i2.511 |
| Publication Date | 2026-08-30 |
| Journal | Computer Journal |
| Volume | 4 |
| Issue | 2 |
| Pages | 328-343 |
| Section | Articles |
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