Abstract— Cryptocurrency price prediction is a crucial task
for financial investors as it helps determine appropriate
investment strategies and mitigate risk. In recent years, deep
learning methods have shown promise in predicting time-series
data, making them a viable approach for cryptocurrency price
prediction. In this study, we compare the effectiveness of two deep
learning techniques, the Recurrent Neural Network (RNN) and
Long-Short Term Memory (LSTM), in predicting the prices of
Bitcoin and Ethereum. Results of this research show that the
LSTM method outperformed the RNN method, obtaining lower
Root Mean Squared Error (RMSE) and Mean Absolute
Percentage Error (MAPE) values for predicting both
cryptocurrencies. Bitcoin and Ethereum. Specifically, the LSTM
model had a RMSE of 0.061 and MAPE of 5.66% for predicting
Bitcoin, and a RMSE of 0.036 and MAPE of 4.58% for predicting
Ethereum. In this research, we found that the LSTM model is a
more effective method for predicting cryptocurrency prices than
the RNN model.