Prediksi Nilai Tukar Rupiah Terhadap Dolar Amerika Dengan Menggunakan Algoritme Genetika - Backpropagation - Dwi Novi Setiawan; Candra Dewi; Sigit Adinugroho (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
The exchange rate is the value of the currency of a country which is expressed in the form of currency of another country. Exchange rate has an important role in international trade. To maintain the stability of the rupiah exchange rate, the government needs to enact the right policy. Therefore, a prediction algorithm that is able to recognize the pattern of exchange rate changes is needed. Backpropagation is one of method that is able to recognize patterns in time series data, while Genetic Algorithm is one of the capable method to exploring wider solutions for Backpropagation. In the Genetic Algorithm, the weight of Backpropagation is represented in real-code. Implementation of Genetic Algorithm - Backpropagation has initialization phase of population, crossover, mutation, individual training using Backpropagation, evaluation, and selection. The most optimum parameters for Genetic Algorithm - Backpropagation are in 90th generation, 20 population size, 0.1 crossover rate, 0.9 mutation rate, number of neurons in hidden layer 13, learning rate 1 and number of iteration of Backpropagation training were 500. The results of the tests that have been done got the best MAPE value of 1.575318 and the average MAPE of 1.741747. The algorithm is also capable of performing the best validation with MAPE of 1,0004917 and the average MAPE of 1.077603.