Early Model of Student's Graduation Prediction Based on Neural Network
Abstract: 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.
Author: Budi Rahmani, Hugo
Aprilianto
Journal Code: jptkomputergg140047
