Comparative Analysis of Linear Regression and Neural Network Algorithms for Stock Price Prediction - Wibowo, Eldrianto Christian; Cahyono, Ariya Dwika (Sistemasi: Jurnal Sistem Informasi, 2025)
This study compares the performance of Linear Regression and Neural Network algorithms in predicting stock prices using historical data from PT Bank Central Asia Tbk (BBCA) for the period from January 1, 2019, to February 17, 2025. The dataset includes daily open, high, low, and close prices, as well as trading volume. Linear Regression is employed as a conventional statistical approach, while Neural Networks are applied as a machine learning method based on deep learning. Performance evaluation is conducted using three error metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The experimental results show that the Linear Regression model consistently produces more accurate predictions with lower error values compared to the Neural Network. Although Neural Networks are more flexible in capturing non-linear patterns, Linear Regression demonstrates greater stability under market conditions present in the observed data period.