PERBANDINGAN PERFORMA MODEL ARIMA-GARCH DAN LSTM DALAM MERAMALKAN JUMLAH KUNJUNGAN WISATAWAN DANAU KASTOBA - Laily Nissa Atul Mualifah; Dalilah Husna; Jasmita Yasmin; Avrel Chesia Berbina; Fadhilah Yumna; Muhammad Ali Uraidly; Adelia Putri Pangestika (Jurnal Gaussian, 2025)
Kastoba Lake, located on Bawean Island, East Java, is a unique natural tourist destination with significant potential for further development. To enhance strategic tourism management, predicting tourist visit numbers is necessary. This study aims to assess the performance of the ARIMA-GARCH and Long Short-Term Memory (LSTM) models in predicting daily tourist arrivals to Kastoba Lake, based on data collected between March 2023 and July 2024. These two methods were specifically selected because the dataset exhibits nonlinear patterns and heterogeneous variance. The ARIMA-GARCH model was employed to handle heteroscedasticity within the data, while LSTM was chosen for its ability to effectively learn and represent long-term patterns. The findings indicate that both models deliver comparable performance and are highly capable of identifying the underlying data trends. Moreover, each model is effective in forecasting short-term tourist visits, particularly over a 7-day horizon (one week). Consequently, these models are reliable tools for predicting and analyzing tourism trends at Kastoba Lake.