The Performance of the XGBOOST-LSTM and CNN-LSTM Algorithms in the Analysis of Stock Price Prediction Models for the Indonesian Banking Sector - M. Zainal Arifin; Filbert Chaitra Bessel Kristianto; Fadia Irsania Putri; Agung Bella Putra Utama (Indonesian Journal of Data and Science, 2026)
One indicator that can be used to gauge a country’s economic performance is its stock market, as it reflects investors’ expectations when buying or selling shares in line with the economic conditions of companies within that country. Stock price forecasting is necessary as it is in the interests of various parties, including both investors and the companies receiving investment funds. This study compares the XGBoost-LSTM and CNN-LSTM algorithms. The dataset used consists of daily closing prices over a period of 1,000 days for shares in the banking sector, specifically PT Bank Central Asia Tbk, PT Bank Rakyat Indonesia (Persero) Tbk, and PT Bank Mandiri (Persero) Tbk. Each model makes a prediction for the next day’s data based on the previous four days’ historical data. A comparison of the results from the CNN-LSTM and XGBoost-LSTM models was carried out using predefined evaluation metrics, namely RMSE, RMAE, R², and MAPE. The results showed that the CNN-LSTM model generally performed better across most evaluation metrics.