Early Model of Student's Graduation Prediction Based on Neural Network
Budi Rahmani, Hugo Aprilianto
Predicting timing of student graduation would be a valuable input for the management of a Department at a University. However, this is a difficult task if it is done manually. With the help of learning on the existing Artificial Neural Networks, it is possible to provide training with a certain configuration, in which based on experience of previous graduate data, it would be possible to predict the time grouping of a student’s graduation. The input of the system is the performance index of the first, second, and third semester. Based on testing performed on 166 data, the Artificial Neural Networks that have been built were able to predict with up to 99.9% accuracy.
prediction, time of graduation, Artifical Neural Network, Backpropagation