Rancang Bangun Aplikasi Untuk Prediksi Harga Bitcoin Menggunakan Algoritma Long Short-Term Memory

Abstract

Bitcoin, as the first cryptocurrency launched in 2009, has experienced rapid growth and significant volatility.
The high price fluctuations of Bitcoin have attracted the attention of investors and traders worldwide.
Predicting Bitcoin prices poses a challenge due to its inherent complexity and volatility. Various methods
are employed to predict Bitcoin prices, including fundamental analysis, technical analysis, and deep
learning techniques. This study explores the use of neural networks, specifically Long Short-Term Memory
(LSTM), as a tool for predicting Bitcoin prices. Long Short-Term Memory is a type of recurrent neural
network (RNN) designed to address the vanishing and exploding gradient problems present in traditional
RNNs and can learn long-term dependencies in data. The LSTM model used in this research comprises 50
neurons, 400 epochs, a batch size of 32, and utilizes Adam optimization. Model evaluation indicates that
LSTM performs well, with a Mean Square Error (MSE) of 2688. Additionally, the model achieves a Mean
Absolute Percentage Error (MAPE) accuracy of 97.77%. Based on these predictions, Bitcoin's price is
expected to decrease over the next month, with an estimated price of approximately 66,191.00 on June 15,
2024, and around 64,271.64 on July 15, 2024