Penerapan Metode Recurrent Neural Network dengan Pendekatan Long Short-Term Memory (LSTM) Untuk Prediksi Harga Saham - Hanafiah, Anggi; Arta, Yudhi; Nasution, Hafiza Oktasia; Lestari, Yuyun Dwi (Bulletin of Computer Science Research, 2023)
Investing in shares is now increasingly popular and growing rapidly in Indonesia. By investing in shares, investors will get quite fast and large profits in a fairly short time. Investors need to analyze previous stock movements as a form of investment strategy to get maximum investment results. Many techniques have been applied to predict stock prices, one of which uses techniques in Deep Learning such as Recurrent Neural Network. In this research, research was conducted on stock price predictions using the Recurrent Neural Network method with the Long-Short Term Memory (LSTM) approach on BBNI stock data. The research only uses close data or data about daily closing stock prices. In designing LSTM there are several things that are configured such as dropout size, density, activation function, and number of neurons used, and in the training process one of the optimizers provided by the Keras framework is used, namely the ADAM (Adaptive Moment Estimation) optimizer. The test scenario is carried out using a number of epochs of 10 and 20 with a batch size of 32. The test results will produce MAE and MAPE values, where the lower the MAE and MAPE values, the better the model's performance in making accurate predictions. The test results using epoch 10 got an MAE value of 0.0286 and a MAPE value of 0.0488, while the test results using epoch 20 got an MAE value of 0.0150 and a MAPE value of 0.0257.