Prediksi Saham Menggunakan Recurrent Neural Network (RNN-LSTM) dengan Optimasi Adaptive Moment Estimation

Abstract

Predicting stock price movements is a complex challenge in the financial market due to unpredictable price fluctuations and high sensitivity levels. Noise in historical stock price data and temporal dependencies between previous and current prices make recognizing price movement patterns difficult. In a dynamic market environment, the model's ability to generate accurate predictions holds significant implications for more informed investment decision-making. The Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM) model holds great potential for stock price prediction because it can capture temporal dependencies, identify non-linear relationships, and decipher complex trends in stock price data. This study employs deep learning techniques with the RNN-LSTM model optimized using Adaptive Moment Estimation (Adam) to enhance stock price prediction accuracy by leveraging historical stock price data and technical factors. Data preprocessing, including handling missing values and data normalization, helps the model navigate the dataset's intricacies. Test results using the Mean Squared Error (MSE) metric show that the model can produce predictions that closely resemble actual stock prices, with a low loss value of 0.109012. The model also exhibits good predictive accuracy, as indicated by a favorable Mean Percentage Error (MPE) score of 1.74% between predicted and actual values. These findings provide valuable implications for assisting investors and financial practitioners in managing complexity and uncertainty within the stock market.