Analisis Optimasi Pada Algoritma Long ShortTerm Memory Untuk Memprediksi Harga Saham
DOI:
https://doi.org/10.30865/mib.v7i1.5421Keywords:
Stock Price Prediction, Neural Network, LSTM, Comparison, Optimizer.Abstract
Stocks are the most popular financial market instrument at the moment, indicated by an increase in investors' shares of 27.15% from the previous year. The biggest risk for stock investors in investing is the risk of falling prices (capital loss) and the risk of liquidation. To minimize this risk, before investing, you should do an analysis first, one of which is by predicting stock price movements. The best method for predicting stock prices is to use Long Short Term Memory (LSTM). In order to predict optimally, it is important to select an optimization algorithm before creating a model. Of the eight optimization algorithms studied, namely SGD, RMSProp, Adam, AdaGrad, AdaMax, AdaDelta, Nadam, and Ftrl. Adam's optimization has the highest level of accuracy in predicting stock prices, where the accuracy value between predicted stock prices and actual stock prices is 98.88% with the average difference between the predicted price and the actual price of IDR 46. This research is expected to provide benefits in predicting stock prices as accurately as possible using the Long Short Term Memory (LSTM) model with the selection of the right optimization algorithm.
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