Forecasting Hotel Demand with Time Series Prediction Model Using Random Forest Regression - Pramita, Dewa Ayu Kadek; Saraswati, Ni Wayan Sumartini; Sandana, I Putu Dedy; Dewi, Dewa Ayu Putu Rasmika; Krismentari, Ni Kadek Bumi (Sinkron : jurnal dan penelitian teknik informatika, 2026)
The tourism sector, as one of the main contributors to national foreign exchange, relies heavily on the growth of the hospitality industry. Improvements in this sector are expected to enhance service quality and strengthen the overall image of tourism. However, the hospitality industry is highly dynamic, with fluctuating room demand influenced by both internal and external factors, creating challenges for accurate demand forecasting. This study develops a hotel demand prediction model using internal variables (occupancy rate, reservations, cancellations, and lead time) and external variables (events and visitor numbers). The Random Forest Regression method was employed, with predictive performance evaluated through a proxy demand index. The dataset was obtained from Adiwana Unagi Suites, Ubud, Bali, covering historical time series data from November 2021 to July 2025 with a total of 18.674 transactions. Evaluation metrics included Mean Absolute Error, Mean Square Error, Root Mean Square Error, and R-squared, applied to each hotel room type. The results demonstrate strong predictive performance, with R-squared values of 99.83% for test data, 99.95% for training data, and 88.24% for three-month prediction data, accompanied by low error values across all metrics. The lower performance in the three-month forecast may be due to the proxy demand index not fully representing actual demand. Overall, the findings highlight the potential of machine learning approaches, particularly Random Forest Regression, to support decision-making in hotel management. The model can serve as a reference for room pricing, allocation, and operational strategies, enabling stakeholders to adapt effectively to fluctuating market demand.