<![CDATA[Technological developments in the financial sector have facilitated the emergence of various digital investment instruments, one of which is cryptocurrency. Bitcoin and Ethereum are digital assets with the largest market capitalization, while the USD remains a significant player in global trade. The high price volatility of these three assets demands accurate and adaptive prediction methods. This study aims to apply the Long Short-Term Memory (LSTM) learning algorithm to predict Bitcoin, Ethereum, and USD prices based on historical data from Yahoo Finance from 2019 to 2024. Preprocessing includes data normalization with a Min-Max Scaler and feature engineering in the form of daily returns. Model evaluation was conducted using the Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics. The results showed that the LSTM model performed best, with the lowest MAE value of 1,320.41 and an MSE of 3,464,596.53 for the highest price prediction. These findings demonstrate that LSTM excels in consistently handling complex and fluctuating data patterns. This research is expected to serve as a reference in the development of a machine learning-based digital asset price prediction system, particularly for assets with high volatility.]]>
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