Stock price forecasting using Long Short Term Memory - Andi, Tri; Kusuma, Candra Juni Cahya (Internet of Things and Artificial Intelligence Journal, 2025)
The objective of this research is to develop a solution for predicting BRI stock prices using Long Short-Term Memory (LSTM) models. The LSTM model was selected for its capacity to process extensive time series data and discern latent temporal patterns. In this study, a BRI stock dataset obtained from Yahoo Finance is employed for the training and testing of an LSTM model. The evaluation results demonstrate that the LSTM model exhibits excellent predictive performance, with a mean absolute percentage error (MAPE) of 1.58768% and a root mean square error (RMSE) of 81.88216%. The Google test results demonstrate a low mean absolute percentage error (MAPE) of 1.5%, indicating a strong correlation between the predicted and true values. In other words, the RMSE values indicate the absolute error level in predictions, indicating the extent to which the model performs well when predicting a value that takes into account the context of the data. In conclusion, the proposed LSTM model shows promise for use in stock price prediction applications. The precision of these models can be tested by using them to make predictions, which would validate the decision-making supported by data. This research suggests that there is room for improvement of these models using techniques such as hyperparameter optimization or ensemble methods (bagging with other weak learners, etc.) to improve their accuracy.