Short-Term Cryptocurrency Price Prediction Using Bi-LSTM Method with Interactive Web - Andriansyach, Dimas Jordy; Sarwido, Sarwido; Mulyo, Harminto (Jurnal Teknologi Dan Sistem Informasi Bisnis, 2024)
Short-term Bitcoin price prediction is a crucial aspect of transaction decision-making, especially for investors. In this study, a Bidirectional Long Short-Term Memory (Bi-LSTM) model was developed for short-term Bitcoin price prediction. The Bidirectional LSTM is designed to capture temporal context in both directions, allowing the model to process information from past and future time steps simultaneously. The model was validated using real-world data, including Bitcoin stock price datasets. The results show that the model achieved high accuracy, with a Root Mean Square Error (RMSE) of 56.90 on the training data and 157.35 on the test data, along with a Mean Absolute Error (MAE) of 366.40 and 486.63, respectively. The Bidirectional Least Square Memory model accurately predicted Bitcoin prices over a specific time period. This application integrates the model into a web application, enabling users to obtain real-time Bitcoin price predictions through a user-friendly interface.