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Neural Network Prediction of Suitability Course for Post PMR Students

Tuck, Looi (2003) Neural Network Prediction of Suitability Course for Post PMR Students. Masters thesis, Universiti Utara Malaysia.

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This study aims to develop a Neural Network Model for predicting suitability course for post PMR students. Artificial Neural Network technique, using Multilayer Perceptron and backpropagation algorithm was employed in this case study. A total of 127 data sample of Form Four students of year 2003 from SMK St Michael, Alor Star was trained using the above mentioned algorithm. The findings show that the best model composes of 10 nodes in input layer; 7 nodes in hidden layer and one node in output layer. A training percentage of correctness 81.04% and testing percentage of correctness 76.79 were achieved using this model. By identifying critical factors to the students’ suitability to the course, students and parents can make an informed decision. This project should be able to provide us with some insights into the type of pattern that exits in educational data. Therefore, Neural Network has great potential in educational planning.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Neural Network, Multi-Layer Feedforward, Backpropagation, Penilaian Menengah Rendah (PMR)
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions: Faculty and School System > Sekolah Siswazah
Depositing User: Mrs Shamsiah Mohd Shariff
Date Deposited: 30 Dec 2009 02:31
Last Modified: 24 Jul 2013 12:10
URI: http://etd.uum.edu.my/id/eprint/1031

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