Prediksi Harga Crypto dengan Algoritma Jaringan Saraf Tiruan - Fegiyanto, Rizki; Hermawan, Arief; Ardiani, Farida (Jurnal Indonesia : Manajemen Informatika dan Komunikasi, 2024)
Cryptocurrency has emerged as a crucial element in the global financial market, prompting investor interest in predicting prices for informed investment decision-making. However, the highly volatile nature of cryptocurrency prices has made trading in the crypto market speculative and laden with significant risks. Therefore, this study aims to apply Artificial Neural Networks (ANNs) using the Long Short-Term Memory (LSTM) algorithm to forecast cryptocurrency prices. This method leverages the ANN's ability to recognize complex patterns and trends in historical cryptocurrency price data. The research findings demonstrate significant accuracy levels for three types of crypto: BTC (86.86%), BNB (96.8%), and Doge (97%). Evaluation was conducted using the k-fold cross-validation method with k=5, where data was divided into five equally sized groups. Accuracy was computed by comparing actual prices with those predicted by the LSTM model. This evaluation approach provides robust insights into the LSTM model's effectiveness in predicting cryptocurrency prices, considering significant data variations. The predicted values from this study are BTC at $4306, BNB at $58.7, and Dogecoin at $0.037. These implications underscore the critical role of the number of epochs in influencing the performance and accuracy of cryptocurrency price predictions. Thus, this research is poised to offer deeper insights into cryptocurrency market behavior and provide practical guidance for investors to make more informed and measured investment decisions.