Implementasi Metode Backpropagation untuk Prediksi Harga Batu Bara - Miracle Fachrunnisa Almas; Budi Darma Setiawan; Sutrisno Sutrisno (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Coal is a natural resource that belongs to one of the fossil fuels. Indonesia is one of the countries with the largest quantity of coal production and export in the world. Coal becomes an important component in the running of a large-scale industrial company as an industrial fuel. Predicted coal prices are needed because coal prices released by the government usually takes a long time. Coal price data is in the form of time series. The data used is coal price data starting from January 2009 to September 2017 with trademark of Gunung Bayan I. This research discusses Backpropagation method that is used to predict the coal price. In this research, the effect of change parameter value from Backpropagation in predicting coal price it can be seen. Output generated by the system is in the form of predicted coal price in the next month. The results of the tests are, the lowest MSE (Mean Square Error) value of 0,00205284 with a combination of 10 neurons on the input layer, 10 neurons in the hidden layer, 1 neuron produced as output, learning rate of 0.1 and the number of iterations of 500.