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