Optirnasi Jaringan Saraf Tiruan Menggunakan Algoritme GenetH Untuk Peramalan Panjang Musim Hujan

Abstract

The focus of this research is to predict length of the rainy season in Indramayu using artificial neural networks (ANN) optimized by genetic algorithm (GA). The weights generated by the neural network is random, so the result of predictive value can change in each training. GA is a computational model that has operator selection, crossover, and mutation for generating a new population. The initialization of weights that is given by ANN will optimize using GA. The predictors which are used in this research are southern oscillation index (SOl) data and beginning of rainy season (AMH) data with the length of rainy season data in previous year to predict length of rainy season in current year. The best result is obtained from the model on average region. On this region, the RMSE is amount of 1.4 dasarian and the correlation coefficient between the observed values and predicted values is amount of 0.741 at 10% the significance level, the RMSEis amount of 1.9 dasarian and the correlation coefficient between the observed values and predicted values is amount of 0.694 at 5% the significance level.