Implementation Of Neural Networks In Predicting The Understanding Level Of Students Subject

Abstract

This paper implements artificial neuralnetworkin predictingthe understanding level<br> ofstudent’scourse. By implementing artificial neural network based on backpropagation<br> algorithm, an institution can give a fair decision in prediction level of students'<br> understanding of particular course / subject. This method was chosen because it is able to<br> determine the level of students' understanding of the subject based on input from<br> questionnaires given. The study was conducted into two ways, namely training and<br> testing. Data will be divided into two parts, the first data for the training process and the<br> second reading data of the testing process. The training process aims to identify or search<br> for goals that are expected to use a lot of patterns. Thus, it will be able to produce the<br> best pattern to train the data. After reaching the goal of training which is based on the<br> best pattern, then it will be tested with new data to seeat the accuracy of the target data<br> using Matlab 6.1 software. The results show that it can accelerate the process of<br> prediction of students' understanding. By using architectural models 6-50-1 as the best<br> model, some architectural models are tested and the result of prediction is reach to<br> 87.75%. In other word, this model is good enough to make predictions on the level of<br> students' understanding of the subject.<br>