Application of Artificial Neural Network Method using Hyperparameter Tuning for Predicting of Euro Exchange Rupiah - Dian Kurniasari; Amelia Fallizia Putri; Warsono Warsono; Notiragayu Notiragayu (Jurnal Sistem Informasi, 2023)
The Covid-19 pandemic has significantly impacted the economic decline in many countries, such as Italy, the United States and the European Union. Indonesia, also affected by Covid-19, was not spared from economic turmoil, especially in the foreign exchange market, where the rupiah exchange rate against the Euro experienced significant fluctuations in early 2020, hampering international trade and investment activities. Therefore, an appropriate method is needed to predict changes in the rupiah exchange rate against the Euro to minimize the obstacles. This study uses the ANN model to predict the Rupiah (Rp) exchange rate against the Euro (€). The best model is obtained through the hyper-tuning process. The optimal parameter values obtained are the input layer with 10 nodes, 2 hidden layers with 19 nodes and 13 nodes, the output layer, dropout of 0.2, 32 batch sizes, 100 epochs, and the Tanh activation function in the distribution scheme of 90% training data and 10 % testing data. Based on the MAPE value of 0.0042% and 0.0041% obtained, the prediction results on the selling and buying rates of the Rupiah against the Euro, it can be concluded that the model has good predictive ability with an accuracy value of 99.996%.Keywords: Currency Exchange Rates, Data Mining, Machine Learning, Artificial Neural Networks