Penggunaan Fungsi Aktivasi Linier dan Logarithmic Normalization pada Metode Backpropagation untuk Peramalan Luas Kebakaran Hutan - Gilang Pratama; Sigit Adinugroho; Bayu Rahayudi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Forest fires is a disaster that often occurs in various countries in the world, especially those with many forest areas. In June 2017, Portugal hit by a forest fire with a loss of more than 565 million US Dollars. In this case, meteorological data can affect several fire indices and can be used area forecasting for extra protection to prevent excessive losses and preservation of natural resources. This paper uses the backpropagation method, which begins with the calculation of preprocessing data (min max normalization, and logarithmic normalization). The weight normalization calculation use Nguyen Widrow method, the calculation of the feed forward process to determine the output at each iteration index, the error value is calculated at the iteration index and the weight is corrected using the backpropagation process. Furthermore, the output value is normalized to return the data to the initial range. The test results are calculated using Mean Square Error (MSE) on each parameter test. Test parameters get the best learning rate value that is 0.1 with the results of MSE 6743,716, 3 hidden neurons with MSE 6745,456, 10 epoch with MSE results 6740,684, and the 10% ratio of test 90% ratio of training data which produces MSE 1881,604.