Implementasi Metode Backpropagation Untuk Peramalan Luas Area Terbakar di Hutan dengan Inisialisasi Bobot Nguyen-Widrow - Afrizal Aminulloh; Sigit Adinugroho; Ahmad Afif Supianto (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Forest fires are a serious event that must be watched out for areas dominated by forest areas. In forest fires, there are several factors that can affect the occurrence of fires such temperature, humidity, rain, wind, and others. This paper implements the backpropagation method to predict the area of the fire. The input used is a factor that influences the occurrence of 7 forest fires. The process of backpropagation method begins with normalizing input data with a range based on the activation function used, after that initialization is weighted and can use the Nguyen-Widrow algorithm, feeds the feedforward and continues to the next process, feedbackward with the MSE requirement less than the error or iteration limit. less than the same as the maximum iteration, if the requirements have been met the output will be normalized, will get a forecasting value, and the last process calculates the results of MSE and SMAPE as a result of the success of the forecasting process. Based on the results of the tests that have been done, it is obtained that the optimal parameters are 5 hidden layer neurons, 0.1 learning rate, and maximum 1500 iterations. The highest average SMAPE result from this study is 49,1796 and the lowest SMAPE average is 31,4492 which shows that the backpropagation method can be used to forecast burn areas in the forest.