Adaboost-multilayer perceptron to predict the student’s performance in software engineering

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a st...

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Main Authors:	Ahmad Firdaus, Zainal Abidin, Mohd Faaizie, Darmawan, Mohd Zamri, Osman, Shahid, Anwar, Shahreen, Kasim, Yunianta, Arda, Sutikno, Tole
Format:	Article
Language:	English
Published:	IAES 2019
Subjects:	QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/26805/ http://umpir.ump.edu.my/id/eprint/26805/ http://umpir.ump.edu.my/id/eprint/26805/1/Adaboost-multilayer%20perceptron%20to%20predict%20the%20student%E2%80%99s%20performance%20.pdf

id	ump-26805
recordtype	eprints
spelling	ump-268052020-02-28T09:02:07Z http://umpir.ump.edu.my/id/eprint/26805/ Adaboost-multilayer perceptron to predict the student’s performance in software engineering Ahmad Firdaus, Zainal Abidin Mohd Faaizie, Darmawan Mohd Zamri, Osman Shahid, Anwar Shahreen, Kasim Yunianta, Arda Sutikno, Tole QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students. IAES 2019-12 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26805/1/Adaboost-multilayer%20perceptron%20to%20predict%20the%20student%E2%80%99s%20performance%20.pdf Ahmad Firdaus, Zainal Abidin and Mohd Faaizie, Darmawan and Mohd Zamri, Osman and Shahid, Anwar and Shahreen, Kasim and Yunianta, Arda and Sutikno, Tole (2019) Adaboost-multilayer perceptron to predict the student’s performance in software engineering. Bulletin of Electrical Engineering and Informatics, 8 (4). pp. 1556-1562. ISSN 2089-3191 (Print); 2302-9285 (Online) http://journal.portalgaruda.org/index.php/EEI/article/view/1923
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Ahmad Firdaus, Zainal Abidin Mohd Faaizie, Darmawan Mohd Zamri, Osman Shahid, Anwar Shahreen, Kasim Yunianta, Arda Sutikno, Tole Adaboost-multilayer perceptron to predict the student’s performance in software engineering
description	Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students.
format	Article
author	Ahmad Firdaus, Zainal Abidin Mohd Faaizie, Darmawan Mohd Zamri, Osman Shahid, Anwar Shahreen, Kasim Yunianta, Arda Sutikno, Tole
author_facet	Ahmad Firdaus, Zainal Abidin Mohd Faaizie, Darmawan Mohd Zamri, Osman Shahid, Anwar Shahreen, Kasim Yunianta, Arda Sutikno, Tole
author_sort	Ahmad Firdaus, Zainal Abidin
title	Adaboost-multilayer perceptron to predict the student’s performance in software engineering
title_short	Adaboost-multilayer perceptron to predict the student’s performance in software engineering
title_full	Adaboost-multilayer perceptron to predict the student’s performance in software engineering
title_fullStr	Adaboost-multilayer perceptron to predict the student’s performance in software engineering
title_full_unstemmed	Adaboost-multilayer perceptron to predict the student’s performance in software engineering
title_sort	adaboost-multilayer perceptron to predict the student’s performance in software engineering
publisher	IAES
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/26805/ http://umpir.ump.edu.my/id/eprint/26805/ http://umpir.ump.edu.my/id/eprint/26805/1/Adaboost-multilayer%20perceptron%20to%20predict%20the%20student%E2%80%99s%20performance%20.pdf
first_indexed	2023-09-18T22:41:58Z
last_indexed	2023-09-18T22:41:58Z
_version_	1777416974908784640

Adaboost-multilayer perceptron to predict the student’s performance in software engineering

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