PENERAPAN NEURAL NETWORK LSTM DALAM MEMPREDIKSI SENTIMEN PENGGUNA TWITTER TERHADAP BITCOIN - Pratama, Duta; Wijaya, Salim; Santosa, Sofian Ali; Tamba, Saut Parsaoran (Jurnal Tekinkom (Teknik Informasi dan Komputer), 2023)
Abstract This research aims to apply the Long Short-Term Memory (LSTM) Neural Network to predict Twitter users' sentiment towards the price of Bitcoin. Bitcoin, as the leading cryptocurrency in the world, faces high price volatility influenced by external factors and market sentiment. Twitter has become a valuable source of information for market analysis, including sentiment towards Bitcoin. Several algorithms have been studied previously for predicting sentiment towards cryptocurrencies, but LSTM has shown excellent results in text analysis and sequence-based data prediction. This research utilizes LSTM to account for the temporal dependencies in Bitcoin tweet data. During testing, the implementation of Bitcoin sentiment prediction using the LSTM model achieved an accuracy level of 96%, indicating the model's capability to make accurate predictions regarding Bitcoin tweet sentiment. The results of this research can contribute to the development of Bitcoin trading strategies and a better understanding of the cryptocurrency market based on Twitter users' sentiment.