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Kurniansyah, Juliandi
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Computer Science & IT

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Implementasi Model Long Short Term Memory (LSTM) dalam Prediksi Harga Saham Kurniansyah, Juliandi; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2005

Abstract

Stock market investment is gaining popularity, although predicting stock price fluctuations remains challenging. Accurate stock prediction models can assist investors in decision-making. In this research, a Long Short-Term Memory (LSTM) model was employed to make predictions regarding the stock prices of BBCA based on daily historical data from January 1 2015 to January 1 2025. The data was gathered from the Yahoo Finance website, utilizing only the closing price ('close') variable. The research process included data pre-processing, Min-Max normalization, LSTM modeling with varying timesteps (30, 60, 90 days), and evaluation of prediction results. The LSTM model was built with two LSTM layers, a dropout layer, and a final dense layer, and its training involved the application of the mean_squared_error loss function and Adam optimizer. Evaluation results showed that the model configuration with 60 timesteps achieved optimal performance with a RMSE of 114.17, MAPE percentage of 0.96%, and an R-Squared of 0.98, indicating highly accurate and reliable predictions. This study demonstrated that LSTM is an effective model for stock price prediction based on time series data.
Co-Authors Febi Yanto Muhammad Affandes Siska Kurnia Gusti