Stock Closing Price Prediction of PT Bank Central Asia Tbk (BBCA) with Long Short-Term Memory (LSTM)

Abstract

Stock price volatility remains one of the key challenges for investors in making accurate investment decisions in Indonesia’s capital market. To address this issue, predictive approaches based on machine learning—such as the Long Short-Term Memory (LSTM) algorithm—are increasingly utilized due to their effectiveness in processing time series data. This study aims to develop a model for predicting the closing price of PT Bank Central Asia Tbk (BBCA) shares using the LSTM method. The dataset consists of historical daily stock prices of BBCA from 2015 to mid-2025, obtained from Yahoo Finance. The research stages include data preprocessing, normalization, sequence generation, LSTM model construction, training and validation, and performance evaluation using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results show that the LSTM model successfully predicted closing stock prices with high accuracy, as indicated by a very low validation loss and prediction curves that closely follow actual price trends. This suggests that LSTM has a strong generalization ability and is effective in capturing complex stock movement patterns. The novelty of this research lies in the practical implementation of LSTM for BBCA stock price prediction and its potential application in real-time decision support systems for investors.