Jaringan Saraf Tiruan Memprediksi Nilai Pemelajaran Siswa Dengan Metode Backpropagation ( Studi kasus : SMP Negeri 1 Salapian)

Abstract

Backpropagationcial neural networks are one of the artificial representations of the human brain that are always trying to stimulate the learning process of the human brain. Backpropagation is a gradient descent method to minimize the squared of the output error. Backprorpagation works through an iterative process using a set of sample data (training data), comparing the predicted value of the network with each sample data. In each process, the weight of the relation in the network is modified to minimize the Mean Squared Error value between the predicted value from the network and the actual value. The purpose of this thesis is to be able to help teachers at SMP Negeri 1 Salakaran to predict the value of student learning. In the calculation using the maximum epouch = 10000, the target error is 0.01, and the learning rate is 0.3, then there is a calculation result where the need ratio A has a value of 0.7517, which means that the value has decreased and D has a value of 0.9202 which means that this value has increased..