<![CDATA[Introduction: Accurate stock price forecasting is important for investors and financial institutions because stock movements reflect dynamic market conditions and influence investment decision-making. This study compares CNN-LSTM and XGBoost-LSTM models for predicting stock prices in the Indonesian banking sector. Method: Historical daily closing prices of PT Bank Central Asia Tbk (BBCA), PT Bank Rakyat Indonesia (Persero) Tbk (BBRI), and PT Bank Mandiri (Persero) Tbk (BMRI) were collected from Yahoo Finance, with 1,000 observations for each stock. Data were normalized using Min-Max scaling, transformed using a four-day sliding window to predict the following day, and chronologically divided into 80% training and 20% testing sets. Both models were evaluated using RMSE, MAE, R², and MAPE, with each experiment repeated ten times. Results and Discussion: CNN-LSTM consistently outperformed XGBoost-LSTM on all three test datasets. For BBCA, BBRI, and BMRI, CNN-LSTM achieved R² values of 0.8589, 0.7746, and 0.7409, respectively, compared with 0.8048, 0.7509, and 0.6260 for XGBoost-LSTM. CNN-LSTM also produced lower RMSE, MAE, and MAPE values across all test sets, indicating stronger and more stable generalization. Conclusion: CNN-LSTM provides more reliable predictive performance than XGBoost-LSTM for the evaluated Indonesian banking stocks, demonstrating the effectiveness of combining local feature extraction with temporal dependency learning for stock price forecasting]]>
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