📅 12 March 2025
DOI: 10.62951/router.v3i1.400
Predicting Hotel Booking Cancellations Using Machine Learning for Revenue Optimization
Router : Jurnal Teknik Informatika dan Terapan
Asosiasi Riset Teknik Elektro dan Informatika Indonesia (ARTEII)
📄 Abstract
Hotel booking cancellations pose significant challenges to the hospitality industry, affecting revenue management, demand forecasting, and operational efficiency. This study explores the application of machine learning techniques to predict hotel booking cancellations, leveraging structured data derived from hotel management systems. Various classification algorithms, including Random Forest, XGBoost, and LightGBM were evaluated to identify the most effective predictive model. The findings reveal that XGBoost model outperforms other models, achieving F2-score of 0.7897. Key influencing factors include deposit type, total number of special requests, and marketing segment. The results underscore the potential of predictive modeling in optimizing hotel revenue strategies by enabling proactive measures such as dynamic pricing, targeted customer engagement, and improved overbooking policies. This study contributes to the ongoing advancements in data-driven decision-making within the hospitality industry, offering insights into how machine learning can mitigate financial risks associated with booking cancellations.
🔖 Keywords
#Hotel Booking Cancellations; Machine Learning; XGBoost; Revenue Management; Classification; LightGBM
ℹ️ Informasi Publikasi
Tanggal Publikasi
12 March 2025
Volume / Nomor / Tahun
Volume 3, Nomor 1, Tahun 2025
📝 HOW TO CITE
Andy Hermawan; Aji Saputra; Nabila Lailinajma; Reska Julianti; Timothy Hartanto; Troy Kornelius Daniel, "Predicting Hotel Booking Cancellations Using Machine Learning for Revenue Optimization," Router : Jurnal Teknik Informatika dan Terapan, vol. 3, no. 1, Mar. 2025.
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