Forecasting Occupancy Rate using Neural Network and Decision Tree at Hotel X

Abstract

Occupancy rate is a critical factor in a hotel because it is used to measure the operational success of a hotel. The higher the hotel occupancy rate, the more successful the hotel business is in generating revenue. In the hospitality business, occupancy rates are very important to monitor and analyse as marketing strategies and pricing policies. This study compares the forecasting of occupancy rates at Hotel X using neural network method and decision tree method. The dataset used in this study is room available, room sold, and available occupancy percentage data at Hotel X from April 2018 to June 2023. The simulation was carried out by dividing the data into training data and testing data with a percentage ratio of 70:30, 75:25, 80:20, 85:15, and 90:10. The forecasting results that have been carried out using a neural network with one hidden layer have an optimal RSME result of 0.010 for split data of 70%:30% and 80%:20% while using a neural network with two hidden layers the optimal result of RSME is 0.013 for split data of 75%:25%. Forecasting results using decision tree RSME optimal results of 0.022 for split data 85%:15% and 90%:10%. From this forecasting, the most optimal results use a neural network with one hidden layer for data splits of 70%:30% and 80%:20% with RSME results of 0.010. The results of the research can be used by Hotel X as a policy determination in the next hotel management.