Penerapan Algoritma Long Short-Term Memory (LSTM) berbasis Multi Fungsi Aktivasi Terbobot dalam Prediksi Harga Ethereum - Timothy Bastian Sianturi; Imam Cholissodin; Novanto Yudistira (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2023)
One form of innovation of technological development is cryptocurrencies that have been widely recognized as an alternative to currency exchange. One of the cryptocurrencies that is quite popular today is Ethereum which started trading for the first time on August 7, 2015 at a price of US2.83 and reached its highest price on November 8, 2021 at a price of US4822.97. Ethereum has high price fluctuations and has many factors that affect the price of ethereum such as political or economic problems at the global level so as to cause sufficient investment risk This research performs several stages in predicting ethereum price movements, namely pre-processing, data normalization, training Long Short-Term Memory (LSTM) algorithms, and evaluating with Mean Square Error (MSE). Based on the results of this study, researchers succeeded in predicting the price of Ethereum using the multi-function activation based LSTM algorithm with testing parameters for the proportion of training data and testing data of 70%:30%, the number of sequences of 14 which describes data for 14 days, the hidden unit value of 64, the number of epochs of 150, and sigmoid as an activation function as evidenced by the MSE value of 0.0121..