APPLICATION OF ARTIFICIAL NEURAL NETWORK METHOD USING HYPERPARAMETER TUNING FOR PREDICTION OF EURO EXCHANGE RUPIAH

APPLICATION OF ARTIFICIAL NEURAL NETWORK METHOD USING HYPERPARAMETER TUNING FOR PREDICTION OF EURO EXCHANGE RUPIAH

Currency Exchange Rates, Data Mining, Machine Learning, Artificial Neural Networks

The Covid-19 pandemic has significantly impactedthe 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,hamperinginternational 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. Thisstudy uses the ANN model to predict the Rupiah (Rp) exchange rateagainst 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 and13 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 resultson 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%

Dian Kurniasari*1, Amelia Fallizia Putri2,Warsono3, Notiragayu4

https://jsi.ejournal.unsri.ac.id/index.php/jsi/article/view/102/99

Universitas Lampung

April 2023

Fajar bagus W

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Indonesia

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