Penggunaan Algoritma LSTM Dengan Mekanisme Attention Untuk Prediksi Harga Cryptocurrency

Abstract

Cryptocurrency, particularly Ethereum, is a digital asset with high price volatility, making price prediction a challenging task for investors. This study aims to develop an Ethereum price prediction model using the Long Short-Term Memory (LSTM) algorithm with an attention mechanism. The dataset includes daily closing prices of Ethereum from November 2017 to October 2024, obtained from Yahoo Finance. The research methodology involves data preprocessing, LSTM model development, and the integration of an attention mechanism to enhance prediction accuracy. Model evaluation was conducted using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that the LSTM model with an attention mechanism achieved a lower MAPE of 2.72% and an RMSE of 102.78, demonstrating better adaptability to significant price fluctuations. Meanwhile, the standard LSTM model exhibited greater stability in consistent market conditions, with a MAPE of 3.28% and an RMSE of 115.00. This study contributes to the advancement of cryptocurrency price prediction technology and serves as a reference for future research. Keywords: LSTM, Attention Mechanism, Price Prediction, Ethereum, Cryptocurrency