Penerapan Metode Long Short-Term Memory (LSTM) untuk Prediksi Harga Saham Berdasarkan Tingkat Volatilitas
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Abstract
Stock price prediction is a complex challenge due to the dynamic influence of external factors. This study employs the Long Short-Term Memory (LSTM) method to predict stock prices of the LQ45 index during the 2021–2024 period, focusing on the effect of volatility levels on model accuracy. The evaluation was conducted using the Mean Absolute Percentage Error (MAPE). Results indicate that LSTM is highly effective, achieving an overall average MAPE of 1.85%, which falls into the “highly accurate” category. A significant difference was observed between the two groups: low-volatility stocks achieved an average MAPE of 1.48%, while high-volatility stocks recorded 2.22%. These findings confirm that price stability enables the model to better capture historical patterns, leading to more precise predictions. The study contributes theoretically by emphasizing volatility as a crucial factor in deep learning–based prediction, and practically by providing recommendations for investors in designing trading strategies that are more adaptive to market risks.
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