Deep Learning Rnn-Lstm Model For Forecasting Tourist Visits In Yogyakarta Using Bps Time-Series Data - Munir, Agus Qomaruddin; Wardani, Ratna; Setiyawan, Ramadhana; Mustofa, Zaenal; Nurkhamid, Nurkhamid (Jurnal Teknik Informatika (Jutif), 2025)
Tourism is a crucial sector in Indonesia's economic growth, particularly in Yogyakarta, contributing significantly to revenue, job creation, and infrastructure development. However, the COVID-19 pandemic has significantly impacted the tourism industry, making tourist arrival forecasting crucial for effective government policy decision-making. This study aims to predict tourist arrivals in Yogyakarta using deep learning models, specifically the Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) algorithms, chosen for their ability to process time series data and address non-linearity issues. Tourist arrival data from the Yogyakarta Central Statistics Agency (BPS) was used to train and test the model. Model evaluation was conducted using the Root Mean Squared Error (RMSE) metric to measure prediction accuracy. The results show that this model can accurately predict tourist arrival patterns, which can support strategic decision-making regarding the procurement of tourism facilities in Yogyakarta. The impact of this research is to provide practical benefits for local governments and tourism industry players in planning tourism promotion and management strategies. With more accurate predictions, relevant parties can prepare necessary resources and optimize tourism services according to projected visitor numbers.