LSTM Neural Network for Predicting Tourist Arrivals to Bali

Abstract

Tourism is a key pillar of Bali’s economy, contributing significantly to employment, cultural preservation, and income generation. Accurate forecasting of tourist arrivals is crucial for sustainable growth and resource optimization. This study applies Long Short-Term Memory (LSTM) neural networks to predict tourist arrivals in Bali, leveraging historical data and external factors such as global economic indicators, flight frequencies, cultural events, and environmental conditions. LSTM’s ability to model complex temporal dependencies and non-linear relationships offers significant advantages over traditional methods like ARIMA, especially in handling seasonal patterns and irregularities. The model was trained on a robust dataset, preprocessed to address missing values, outliers, and variability. Performance evaluation metrics, including RMSE, demonstrate high predictive accuracy during stable periods but highlight limitations in handling anomalies such as the COVID-19 pandemic. To address these challenges, recommendations include integrating additional external variables, employing hybrid models, and conducting scenario-based sensitivity analyses to enhance adaptability and robustness. The results highlight the practical utility of AI-driven forecasting tools in tourism management, providing actionable insights for policymakers and stakeholders to optimize planning, mitigate risks, and support sustainable development. This research contributes to the growing field of AI applications in tourism, promoting resilience and competitiveness in an increasingly dynamic global market.