STUDENTS DATA CLASSIFICATION MODEL
In this project, research is conducted based on data sets of undergraduates at varsity level to classify student performance data. The objective of the project is to develop a system that utilizes various intelligent techniques with targeted accuracy being at a minimal level of88%. The system is...
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Universiti Teknologi PETRONAS
2006
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my-utp-utpedia.94592017-01-25T09:46:06Z http://utpedia.utp.edu.my/9459/ STUDENTS DATA CLASSIFICATION MODEL SETU, ILI AIMIE T Technology (General) In this project, research is conducted based on data sets of undergraduates at varsity level to classify student performance data. The objective of the project is to develop a system that utilizes various intelligent techniques with targeted accuracy being at a minimal level of88%. The system is designed to predict students' CGPA upon graduation. Any further actions that can be taken to avoid students' dismissals, or to strengthen their area of interest or expertise can be derived from the outcome of this intelligent system. The project is implemented using data sets Iris and Student. Techniques used to support classification are separated into two different subprojects: (1) Back propagation feed forward neural network using Bayes probability to initialize weights, and (2) Fuzzy system. The proposed optimization of neural network and Bayes Theorem returns 92.55% level of accuracy for the student data. Further improvements can be performed on areas such as the individual variations of each technique and the combination of all three techniques to optimize accuracy. The project contributes in customizing a grading system for Universiti Teknologi PETRONAS. This system structure is generally relevant to many universities in Malaysia as they adopt a fairly similar approach in grading Universiti Teknologi PETRONAS 2006-11 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/9459/1/2006%20Bachelor%20-%20Student%20Data%20Classification%20Model.pdf SETU, ILI AIMIE (2006) STUDENTS DATA CLASSIFICATION MODEL. Universiti Teknologi PETRONAS. (Unpublished) |
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In this project, research is conducted based on data sets of undergraduates at varsity level
to classify student performance data. The objective of the project is to develop a system
that utilizes various intelligent techniques with targeted accuracy being at a minimal level
of88%.
The system is designed to predict students' CGPA upon graduation. Any further actions
that can be taken to avoid students' dismissals, or to strengthen their area of interest or
expertise can be derived from the outcome of this intelligent system.
The project is implemented using data sets Iris and Student. Techniques used to support
classification are separated into two different subprojects: (1) Back propagation feed
forward neural network using Bayes probability to initialize weights, and (2) Fuzzy
system. The proposed optimization of neural network and Bayes Theorem returns
92.55% level of accuracy for the student data. Further improvements can be performed
on areas such as the individual variations of each technique and the combination of all
three techniques to optimize accuracy.
The project contributes in customizing a grading system for Universiti Teknologi
PETRONAS. This system structure is generally relevant to many universities in Malaysia
as they adopt a fairly similar approach in grading |
format |
Final Year Project |
author |
SETU, ILI AIMIE |
author_facet |
SETU, ILI AIMIE |
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SETU, ILI AIMIE |
title |
STUDENTS DATA CLASSIFICATION MODEL |
title_short |
STUDENTS DATA CLASSIFICATION MODEL |
title_full |
STUDENTS DATA CLASSIFICATION MODEL |
title_fullStr |
STUDENTS DATA CLASSIFICATION MODEL |
title_full_unstemmed |
STUDENTS DATA CLASSIFICATION MODEL |
title_sort |
students data classification model |
publisher |
Universiti Teknologi PETRONAS |
publishDate |
2006 |
url |
http://utpedia.utp.edu.my/9459/1/2006%20Bachelor%20-%20Student%20Data%20Classification%20Model.pdf http://utpedia.utp.edu.my/9459/ |
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13.18916 |