Pengembangan model prediksi saham perbankan Indonesia dengan optimasi algoritma Long Short-Term Memory (LSTM) - Didik Nugroho; Eka Ardhianto (AITI, 2026)
Banking stocks have attracted public attention because they are often perceived as relatively stable and capable of reflecting the current economic conditions. However, in reality, banking stocks are quite volatile for various reasons. Investors need an accurate stock prediction model to make investment decisions and reduce risk. This study proposes a stock prediction model based on the Long Short-Term Memory (LSTM) deep learning algorithm. The dataset consists of ten years of historical stock market data from banking companies listed on the Indonesia Stock Exchange (IDX), covering the period from 2014 to 2023. LSTM was chosen because it is known to be effective at predicting time-series data and can capture long-term dependencies and patterns. The results of this model were optimized using the Adam optimizer and hyperparameter tuning. These findings suggest that the proposed approach can serve as a reliable tool for stock price forecasting and support more informed investment strategies in the banking sector.