University admission selection for SPM leavers using Rule-Based Expert System / Intan Syazlinda Mohd Shahidon

Mohd Shahidon, Intan Syazlinda (2006) University admission selection for SPM leavers using Rule-Based Expert System / Intan Syazlinda Mohd Shahidon. Degree thesis, Universiti Teknologi MARA.

Abstract

Entering higher educational institutions such as university is always being one's aim
after the Malaysia Certification Examination. They will choose universities and
programs they hke, sometimes without taking into consideration the university and
program requirements. The problems come to the universities where they need to choose
and selects qualified apphcants among thousand of applications. This processes become
harder when applicants do not consider the requirements. A system is needed to assist
the administrators in selecting the qualified students. Consequently, the processes will be
more systematic, efficient and reliable. University Admission Selection For SPM
Leavers Using Rule-Based Expert System is an alternatives solution to the problems
using Artificial Intelligence approach. This system can help processing the application
and then produce output which is tiie list of qualified students for each program as a
solution to the problem stated. A rule-based technique is chosen because Ae knowledge
in this problem domain can be directly transformed into rules

Metadata

Item Type: Thesis (Degree)
Creators:
Creators
Email / ID Num.
Mohd Shahidon, Intan Syazlinda
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences
Date: 2006
URI: https://ir.uitm.edu.my/id/eprint/1495
Edit Item
Edit Item

Download

[thumbnail of TD_INTAN SYAZLINDA SHAHIDON CS 06_5 P01.pdf] Text
TD_INTAN SYAZLINDA SHAHIDON CS 06_5 P01.pdf

Download (63kB)

Digital Copy

Digital (fulltext) is available at:

Physical Copy

Physical status and holdings:
Item Status:

ID Number

1495

Indexing

Statistic

Statistic details