Early Model of Student's Graduation Prediction Based on Neural Network

Budi Rahmani, Hugo Aprilianto

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.

 


Keywords


prediction, time of graduation, Artifical Neural Network, Backpropagation

Full Text:

PDF


DOI: http://dx.doi.org/10.12928/telkomnika.v12i2.47

Refbacks

  • There are currently no refbacks.


Copyright (c) 2014 TELKOMNIKA (Telecommunication Computing Electronics and Control)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.


TELKOMNIKA Telecommunication, Computing, Electronics and Control
website: http://telkomnika.ee.uad.ac.id
online system: http://journal.uad.ac.id/index.php/TELKOMNIKA
Phone: +62 (274) 563515, 511830, 379418, 371120 ext: 3208
Fax    : +62 (274) 564604

View TELKOMNIKA Stats