Forecasting International Tourist Arrivals to Indonesia Using LSTM: Post-Pandemic Analysis for 2024-2025 - Ayu Sofia; Dien, Zulfanita; Erda, Gustriza (Journal of Mathematics, Computations and Statistics, 2025)
As Indonesia's main foreign exchange contributor, the tourism sector experienced significant dynamics after the COVID-19 pandemic, characterized by a sharp decline in the number of foreign tourists during the pandemic and consistent recovery in the post-pandemic period. This study aims to predict the number of foreign tourists to Indonesia from September 2024 to August 2025 using the Long Short-Term Memory (LSTM) method. The LSTM model is optimized with an 80:20 data split for training testing and uses optimal parameters, namely Learning Rate 0.005, Batch Size 64, Optimizer Adam, and Epoch 200. The prediction results show an increase in the number of tourists to a peak of 1,390,564 in November 2024, followed by a gradual decline to 987,970 in August 2025, with an accuracy level indicated by a MAPE value of 14.39%