Implementation of Neural Networks in Predicting the Understanding Level of Students Subject

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.