Perbandingan Algoritma Backpropagation Neural Network dan Long Short-Term Memory dalam Memprediksi Harga Bitcoin
Abstract
In actual practice, Bitcoin is the decentralized currency that allows two individuals to transact without third-party intervention. However, due to its high volatility, it has been such an attraction to investors to gain profit. But, that also mean that high volatility can also bring disadvantage if someone predicts the increase or decrease of the price of Bitcoin incorrectly. The technical analysis which is often used to predict Bitcoin prices has a weakness, that is specifically depends on the users of technical indicators. Therefore, it is necessary to use the Data Mining algorithm as an alternative solution to predict Bitcoin prices. In this paper, the implemented algorithms to predict Bitcoin prices are Long Short-Term Memory (LSTM) and Backpropagation Neural Network. The final results using T-Test showed there is no significant difference between LSTM and Backpropagation in predicting the data test with an average RMSE value of 661.580 and 1.812.503, respectively. However Backpropagation has the advantage to predict new data (outside of the dataset) with an average RMSE value of 629.545, while the average RMSE value of the LSTM is 2.818.248.
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