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All Journal Jurnal Mantik
Ihyak Ulumuddin
Universitas Ahmad Dahlan Yogyakarta
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Bitcoin Price Prediction Using Long Short Term Memory (LSTM): Bitcoin Price Prediction Using Long Short Term Memory (LSTM) Ihyak Ulumuddin; Sunardi Sunardi; Abdul Fadlil
Jurnal Mantik Vol. 4 No. 2 (2020): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.889.pp1090-1095

Abstract

Price fluctuation is a necessity in every business, including the price of Bitcoin. Therefore humans need a device or application that can assist in making predictions quickly and accurately. Deep Learning can be used to make predictions which include setting the parameters used during training, choosing the best parameters for prediction, and choosing the prediction results with the smallest error level in the actual situation. This study performs a Bitcoin price prediction using Long Short Term Memory (LSTM). This study uses quantitative calculations on the prediction results. Measurement of accuracy is done by testing the previous price (back testing) and calculating the average error using RMSE and MAPE. The method for generating predictions is LSTM as a type of Recurrent Neural Network (RNN) which has the advantage of using long-term memory in handling time series data. LSTM implementation using Python is used for price forecasting with stages starting from data collection and normalization, input and output modeling. The result of this research is a prediction of Bitcoin price with an accuracy rate of 97.48% based on the best model with input layer 2, the number of epochs 100, the number of hidden layers 100, and activation using Softmax. These price predictions can be used as a reference and consideration for traders to create trading strategies and run them automatically on the digital currency market.
Co-Authors Abdul Fadlil Sunardi, Sunardi