Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM)

Cryptocurrency Price Prediction Using Long Short Term Memory (LSTM) Algorithm

  • Moch Farryz Rizkilloh Universitas Telkom
  • Sri Widiyanesti Universitas Telkom
Keywords: Cryptocurrency, RNN, LSTM, Price Forecasting, BTC


Technological developments continue to encourage the creation of various innovations in almost all aspects of human life. One of the innovations that is becoming a worldwide phenomenon today is the presence of cryptocurrency as a digital currency that is able to replace the role of conventional currency as a means of payment. Currently, the number of cryptocurrency investors in Indonesia has reached 4.45 million people as of March 2021, an increase of 78% compared to the end of the previous year. Very volatile price movements make cryptocurrency investments considered speculative so the risks faced are also very high. The purpose of this study is to build a predictive model that is able to forecast prices on the cryptocurrency market. The algorithm used to build the prediction model is Long Short Term Memory (LSTM). LSTM is the development of the Recurrent Neural Network (RNN) algorithm to overcome problems in the RNN in managing data for a long period. LSTM is considered superior to other algorithms in managing time series data. The data in this study were taken from the Yahoo Finance website using the Pandas Datareader library through Google Collaboratory. The entire prediction model development process is carried out through Google Collaboratory tools. To improve the accuracy of the model, the Nadam optimization algorithm was used and three testing sessions were carried out with the number of Epochs of 1, 10, and 20 in each session. The final test results show that the best prediction performance occurs when testing the DOGE coin type with the number of Epoch 20 which gets an RMSE value of 0.0630.


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How to Cite
Moch Farryz Rizkilloh, & Sri Widiyanesti. (2022). Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM). Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(1), 25 - 31.
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