Predicting Employment Condition of TARC'S ICT Graduates Using Backpropagation Neural Network

This research is conducted with the purpose of classifying the employment condition of ICT students after their graduation using Backpropagation Neural Network (BPNN). To narrow down the scope of the research, ICT students from Tunku Abdul Rahman College (TARC) are targeted. The employment condition...

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Main Author: Tay, Shu Shiang
Format: Thesis
Language:en
en
Published: 2009
Subjects:
Online Access:https://etd.uum.edu.my/2064/1/Tay_Shu_Shiang.pdf
https://etd.uum.edu.my/2064/2/1.Tay_Shu_Shiang.pdf
https://etd.uum.edu.my/2064/
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author Tay, Shu Shiang
author_facet Tay, Shu Shiang
author_sort Tay, Shu Shiang
building UUM Library
collection Institutional Repository
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
continent Asia
country Malaysia
description This research is conducted with the purpose of classifying the employment condition of ICT students after their graduation using Backpropagation Neural Network (BPNN). To narrow down the scope of the research, ICT students from Tunku Abdul Rahman College (TARC) are targeted. The employment condition will be predicted and classified based on several macroscopic and microscopic criterion indentified. The macroscopic reasons include the social and the governmental factors while the microscopic reasons cover the college and the student factors. This paper will show the BPNN steps involved in creating a suitable multilayer-perceptron classification model for the employment condition. Detail descriptions of the BPNN methodologies applied are also included in the report. The findings of the research are expected to provide TARC's management an in-depth view on their students' marketability and adaptability in the work fields.
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language en
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spelling my.uum.etd-20642013-07-24T12:14:14Z https://etd.uum.edu.my/2064/ Predicting Employment Condition of TARC'S ICT Graduates Using Backpropagation Neural Network Tay, Shu Shiang QA71-90 Instruments and machines This research is conducted with the purpose of classifying the employment condition of ICT students after their graduation using Backpropagation Neural Network (BPNN). To narrow down the scope of the research, ICT students from Tunku Abdul Rahman College (TARC) are targeted. The employment condition will be predicted and classified based on several macroscopic and microscopic criterion indentified. The macroscopic reasons include the social and the governmental factors while the microscopic reasons cover the college and the student factors. This paper will show the BPNN steps involved in creating a suitable multilayer-perceptron classification model for the employment condition. Detail descriptions of the BPNN methodologies applied are also included in the report. The findings of the research are expected to provide TARC's management an in-depth view on their students' marketability and adaptability in the work fields. 2009 Thesis NonPeerReviewed application/pdf en https://etd.uum.edu.my/2064/1/Tay_Shu_Shiang.pdf application/pdf en https://etd.uum.edu.my/2064/2/1.Tay_Shu_Shiang.pdf Tay, Shu Shiang (2009) Predicting Employment Condition of TARC'S ICT Graduates Using Backpropagation Neural Network. Masters thesis, Universiti Utara Malaysia.
spellingShingle QA71-90 Instruments and machines
Tay, Shu Shiang
Predicting Employment Condition of TARC'S ICT Graduates Using Backpropagation Neural Network
title Predicting Employment Condition of TARC'S ICT Graduates Using Backpropagation Neural Network
title_full Predicting Employment Condition of TARC'S ICT Graduates Using Backpropagation Neural Network
title_fullStr Predicting Employment Condition of TARC'S ICT Graduates Using Backpropagation Neural Network
title_full_unstemmed Predicting Employment Condition of TARC'S ICT Graduates Using Backpropagation Neural Network
title_short Predicting Employment Condition of TARC'S ICT Graduates Using Backpropagation Neural Network
title_sort predicting employment condition of tarc's ict graduates using backpropagation neural network
topic QA71-90 Instruments and machines
url https://etd.uum.edu.my/2064/1/Tay_Shu_Shiang.pdf
https://etd.uum.edu.my/2064/2/1.Tay_Shu_Shiang.pdf
https://etd.uum.edu.my/2064/
url_provider http://etd.uum.edu.my/