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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

Early Model of Student's Graduation Prediction Based on Neural Network Budi Rahmani; Hugo Aprilianto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 2: June 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i2.47

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. 
Early Model of Traffic Sign Reminder Based on Neural Network Budi Rahmani; Supriyadi Supriyadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 4: December 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i4.864

Abstract

Recognizing the traffic signs installed on the streets is one of the requirements of driving on the road. Laxity in driving may result in traffic accident. This paper describes a real-time reminder model, by utilizing a camera that can be installed in a car to capture image of traffic signs, and is processed and later to inform the driver. The extracting feature harnessing the morphological elements (strel) is used in this paper. Artificial Neural Networks is used to train the system and to produce a final decision. The result shows that the accuracy in detecting and recognizing the ten types of traffic signs in real-time is 80%.