Prediksi Harga Forex Menggunakan Algoritma Long Short-Term Memory

Abstract

Trillions of dollars per day of foreign currency trading activity occur in the forex market, which has very volatile movements in foreign currency trading. Trade based on bid and ask prices. The market determines foreign exchange rates based on supply and demand rules. Currency trading in pairs such as EUR/USD is a comparison of the value of the Euro against the Dollar as a basis for research, rising and falling currency prices in forex move fluctuatingly, so a market participant must be able to decide on buying and selling positions. Because wrong decisions can lead to losses. One of the ways to reduce risk in making decisions in buying and selling in forex can be using forecasting. This study uses the LSTM method in predicting forex prices which will be tested on several scales of dataset distribution. The smallest error results using a total dataset of 2631 with a dataset division of 70:15:15, which is divided into 70% data for training, 15% data for validation and 15% data for testing produces an RMSE value of 0.038, MAPE 2.5%. In measuring how well the regression model used with Rsquare on the data distribution is 70:15:15 and the total dataset used is 4979 to get the best results, namely 97%.