Predicting tourist arrivals to a tropical island using artificial intelligence

Abstract

This research leverages artificial intelligence (AI) techniques to develop a predictive model for forecasting tourist arrivals in East Java Province, Indonesia, using a comprehensive dataset encompassing historical tourism statistics from 2018 to 2020, seasonal trends, promotional campaigns, and various economic and social variables. The study evaluates three AI methodologies: artificial neural network (ANN), extreme learning machine (ELM), and Jordan recurrent neural network (JRNN), each known for their distinct strengths in processing complex data and adapting to changing trends. The comparative analysis reveals that the JRNN model outperforms others with the highest precision, achieving an average prediction deviation of just 2.98% from actual data, effectively capturing temporal and seasonal trends. The ANN follows closely with a deviation of 3.31%, showing strong capabilities in handling complex, nonlinear relationships. In contrast, the ELM, though fastest in training, exhibits a larger deviation of 10.51%, indicating a trade-off between speed and accuracy. These results highlight the potential of AI to significantly enhance the accuracy and operational efficiency of tourism forecasts, offering robust tools for stakeholders to engage in informed strategic planning and resource allocation in dynamic market conditions.