Open AccessAnalysis of backpropagation neural neural network algorithm on student ability based cognitive aspects
Author(s) -
Fahmi Izhari,
Muhammad Zarlis,
Sutarman Sutarman
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/725/1/012103
Subject(s) - sigmoid function , backpropagation , artificial neural network , activation function , binary number , mean squared error , epoch (astronomy) , artificial neuron , error function , computer science , value (mathematics) , algorithm , set (abstract data type) , function (biology) , cognition , artificial intelligence , mathematics , arithmetic , statistics , machine learning , psychology , computer vision , stars , neuroscience , programming language , evolutionary biology , biology
Back propagation is one of the supervised learning and multi-layered training program and uses errors during the process of changing the weight value in the backward process as well as the forward propagation. In the method for predicting cognitive abilities backpropagation the first step is to set the input neuron number, the number of neurons that are hidden, and the number of output neurons. The number of neurons used in the program is 6 neurons consisting of cognitive criteria, 6 hidden neuron layers, and 2 neuron outputs. The highest level of accuracy is in the binary sigmoid and bipolar sigmoid activation functions at the 64th epoch with the accuracy of each function of 82.93% +/- 37.63% and 85.37% +/- 35.34%. The smallest root mean squared error value was found in binary sigmoid of 0.266 with a tolerance of +/- 0.258 on the 100th epoch with an accuracy of 80.49% while for the sigmoid bipolar activation function the smallest root mean squared error value was obtained at the epoch 500 of 0.282 with tolerance +/- 0.353.