Perbandingan Kinerja Model LSTM dengan dan tanpa Indeks Fear & Greed dalam Prediksi Harga Bitcoin Berbasis Data Time Series

Mada Rabbani Syah (Universitas Muhammadiyah Malang, Malang)
Vinna Rahmayanti Setyaning Nastiti (Universitas Muhammadiyah Malang, Malang)

Building of Informatics, Technology and Science
Vol 8 No 1 (2026): June 2026

Bitcoin is a highly volatile digital asset whose price movements are influenced by historical market patterns and investor sentiment, making accurate prediction a significant challenge for conventional forecasting approaches. This study compares the predictive performance of the Long Short-Term Memory (LSTM) model in forecasting Bitcoin prices under two configurations: without and with the integration of the Crypto Fear & Greed Index as a market sentiment feature. The dataset consists of time series data including opening, highest, lowest, and closing prices obtained from the CoinGecko API, along with daily sentiment scores collected from the Alternative.me API. The data preprocessing stages include normalization using the Min-Max Scaler and sequence construction using the sliding window method, both of which are standard practices in deep learning-based time series forecasting. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) as quantitative error metrics. The experimental results show that the LSTM model without the Fear & Greed Index produced an MAE of 1011.34, an RMSE of 1340.55, and a MAPE of 1.20%, while the model incorporating the Fear & Greed Index achieved an MAE of 979.95, an RMSE of 1349.07, and a MAPE of 1.18%. These findings indicate that the inclusion of market sentiment improves overall prediction accuracy, as reflected by the reduction in MAE and MAPE, despite a marginal increase in RMSE. The main contribution of this study is providing empirical evidence regarding the impact of integrating the Crypto Fear & Greed Index as a market sentiment indicator on improving the performance of LSTM models for Bitcoin price prediction, as well as offering a direct comparison between models based solely on historical price data and models that combine historical price data with market sentiment information. This study concludes that combining deep learning techniques with measurable sentiment indicators provides a more comprehensive and effective framework for Bitcoin price prediction.