A Machine-Learning-Based Approach for Tourist-Arrival Trend Prediction

Abstract

This study proposes a machine-learning-based technique to predict trend in tourist arrivals based on online news headlines and the number of previous tourist arrivals. Tourist arrivals prediction is important to give information to destinations’ local governments and businesses to prepare their services. We use Logistic Regression and Support Vector Machine to create a model to predict the increase in tourist arrivals monthly. News headlines from three online Indonesian news portals are used. A total of 47,298 online news headlines were collected. The results show that Logistic Regression can achieve up to 67.4% of F-score while Support Vector Machine can achieve up to 62.9% of F-score. These results show that adding online news headlines and machine-learning algorithms can give significantly better results in predicting tourist arrivals.