OPTIMASI CONJUGATE GRADIENT PADA BACKPROPAGATION NEURAL NETWORK UNTUK PREDIKSI HASIL TANGKAP IKAN

Abstract

The need for fish catch by a company or fisherman in Rembang Regency affects market process and also welfare. The catch made by the fishermen is not on target, due to the weather and type of fishing gear. An accurate method is needed in making predictions and a correlation between catch and weather so that fisherman can get maximum predictions results, so that price adjustment can be made. The research was conducted using an experimental method, to determine the accuracy of the effect of the Conjugate Gradient on the Back Propagation Neural Network in obtaining the best value. Based on the results of the Cycle training test with the Conjugate Gradient Backpropagation Neural Network method, the smallest average value is obtained at the 400th Epoch compared to the Epoch Gradient Descent With Momentum method at Epoch 800.Thus it is proven that using the Conjugate Gradient Backpropagation Neural Network method is better with an average value of- MSE average 0.2223 in three stages of testing Training Cycle, Learning Rate and Hidden Layer.