Prediksi Harga Saham BBCA Menggunakan Metode CNN, LSTM dan CNN-LSTM Berbasis Data Time Series
- Rifki Faiz Azzurananda Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Siska Kurnia Gusti
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Elin Haerani Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Eka Pandu Cynthia Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Predicting stock prices is a real challenge because financial data moves unpredictably and its patterns are hard to figure out. This study implements and compares three deep learning models, namely Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM, to predict the stock price of PT Bank Central Asia Tbk (BBCA) using OHLCV (Open, High, Low, Close, Volume) data from 2015 to 2025. The focus of this study is to compare the performance of each model under different experimental settings, rather than to propose a new architecture. Experiments were carried out using timesteps of 30, 60, and 90, combined with data split ratios of 70:30, 80:20, and 90:10. Model performance was measured using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results show that CNN gave the most stable performance across all tested scenarios, while LSTM was able to pick up temporal patterns but still showed lagging in some conditions. The hybrid CNN-LSTM model came out on top at timestep 90 with an 80:20 data split, reaching an RMSE of 130.5432, MAPE of 1.0669%, and R² of 0.9581. This shows that the CNN-LSTM hybrid works better at producing accurate and responsive predictions compared to single models.
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Copyright (c) 2026 Rifki Faiz Azzurananda, Siska Kurnia Gusti, Elin Haerani, Eka Pandu Cynthia
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