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Tri Yani Elisabeth Nababan
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
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PEMODELAN WAVELET NEURAL NETWORK UNTUK PREDIKSI NILAI TUKAR RUPIAH TERHADAP DOLAR AS Tri Yani Elisabeth Nababan; Budi Warsito; Agus Rusgiyono
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (686.591 KB) | DOI: 10.14710/j.gauss.v9i2.27823

Abstract

Each country has its own currency that is used as a tool of exchange rate valid in the transaction process. In the process of transaction between countries often experience problems in terms of payment because of the difference in the value of money prevailing in each country. The price movement of the exchange rate or the value of foreign currencies that fluctuate from time to time it encouraged predictions of the value of the rupiah exchange rate against the U.S. dollar. Wavelet Neural Network (WNN) is a combination of methods between wavelet transforms and Neural networks. WNN modeling begins with wavelet decomposition resulting in wavelet coefficients and scale coefficients. Selection of inputs is based on PACF plots and divides into training data and testing data. To determine the final output by calculating the value of MAPE in data testing. The best architecture on WNN model for prediction of the value of the rupiah exchange rate against the U.S. dollar is a model with sigmoid logistic activation function, 2 neurons in the input layer, 10 neurons in the hidden layer, and 1 neuron in the output layer. The MAPE value is obtained at 0.2221%.  
Co-Authors Agus Rusgiyono Budi Warsito