Prediksi Pola Pergerakan Saham Adro.Jk Melalui Model LSTM Berbasis Data Historis
DOI:
https://doi.org/10.47065/bulletincsr.v5i4.554Keywords:
Stock Price Prediction; Long Short-Term Memory; Yahoo Finance; Time Series; Machine Learning AlgorithmAbstract
The fluctuating nature of stock price movements presents a significant challenge in investment decision-making. To address this issue, a predictive model capable of capturing historical patterns and accurately forecasting stock prices is required. This study aims to develop a stock price prediction model for PT Alamtri Resources Indonesia Tbk (ADRO.JK) using the Long Short-Term Memory (LSTM) algorithm. The dataset comprises daily closing prices from January 1, 2020, to December 30, 2024, obtained from Yahoo Finance. The data was processed in a time series format using a sliding window approach, employing 30 historical data points to predict the next price point. The model was constructed using two LSTM layers, one Dense layer, and techniques such as Dropout and EarlyStopping to prevent overfitting.The training and testing results indicate that the model performs exceptionally well, achieving a Mean Absolute Percentage Error (MAPE) of 0.0341 or 3.41%, corresponding to a prediction accuracy of 96.59%. In a short-term prediction scenario over seven days, the model achieved an accuracy of 99.07% (MAPE = 0.0093), while in a medium-term scenario up to May 19, 2025, it achieved an accuracy of 98.76% (MAPE = 0.0124). The predicted stock price on May 19, 2025, is estimated at IDR 1,913.76. With its high accuracy and low error rate, the LSTM model has proven to be a reliable tool for forecasting stock prices based on historical data.
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