Predicting Hotel Booking Cancellations Using CatBoost and SHAP: An Explainable AI Approach Based on 2020–2026 Operational Data

I Made Sudana; Endang Wahyu Pamungkas

doi:10.58477/cj.v4i2.511

Articles Vol. 4, No. 2 (2026)

I Made Sudana, Endang Wahyu Pamungkas

Universitas Muhammadiyah Surakarta

10.58477/cj.v4i2.511 Published: 2026-08-30

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:

Hotel Booking Cancellation CatBoost Class Imbalance Explainable Artificial Intelligence (XAI) Shapley Additive exPlanations (SHAP)

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

I Made Sudana

Universitas Muhammadiyah Surakarta

Master of Informatics, Universitas Muhammadiyah Surakarta, Sukoharjo Regency, Central Java Province, Indonesia.

Endang Wahyu Pamungkas

Universitas Muhammadiyah Surakarta

Master of Informatics, Universitas Muhammadiyah Surakarta, Sukoharjo Regency, Central Java Province, Indonesia.

Issue	Vol. 4 No. 2 (2026)
Section	Articles
DOI	10.58477/cj.v4i2.511
Publication Date	2026-08-30
Pages	328-343

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