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All Journal ELINVO (Electronics, Informatics, and Vocational Education)
Purnama, Panji Satria Taqwa Putra
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Computer Science & IT Education Electrical & Electronics Engineering

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Optimizing Bitcoin Price Prediction with LSTM: A Comprehensive Study on Feature Engineering and the April 2024 Halving Impact Purnama, Panji Satria Taqwa Putra
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 9 No. 1 (2024): Mei 2024
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v9i1.72518

Abstract

This research aims to develop a Bitcoin price prediction model using machine learning techniques, with a specific focus on Long Short-Term Memory (LSTM) neural networks. The Bitcoin market is characterized by unique features such as high volatility and the influence of various external factors, which differ significantly from traditional financial markets. As such, precise feature engineering is crucial for accurately modelling Bitcoin prices. Utilizing historical Bitcoin price data from 2014 to 2023, this study extensively evaluates LSTM models. The results indicate that LSTM models provide highly accurate predictions, with a Mean Squared Error (MSE) of 0.0001798 and a Mean Absolute Error (MAE) of 0.0101322. These results demonstrate that LSTM effectively captures the complex and dynamic patterns of Bitcoin prices, outperforming other methods. The findings have significant implications for financial market analysis, especially within the rapidly evolving domain of crypto assets. By leveraging machine learning methodologies, this research enhances understanding of the complexities of the crypto market and offers potential strategies for smarter investment decisions. The success of the LSTM model in improving Bitcoin price prediction accuracy underscores its importance in navigating the volatile and dynamic nature of the crypto market. Overall, this study highlights the substantial potential of machine learning approaches, particularly LSTM models, in analyzing and predicting crypto market behavior. It contributes to the growing academic discourse on the application of advanced technologies in finance and can stimulate further discussions on how machine learning can address challenges and opportunities in the crypto market.
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