Cover Image

A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy

Game and Online Video Streaming are the most viewed web pages. Users who spend too much time on these types of web pages may suffer from internet addiction. Access to Game and Online Video Streaming web pages should be restricted to combat internet addiction. A tool is required to recognise the cate...

Full description

Saved in:
Bibliographic Details
Main Authors: Siti Hawa, Apandi, Jamaludin, Sallim, Rozlina, Mohamed
Format: Article
Language:English
Published: Department of Information Technology - Politeknik Negeri Padang 2023
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/40032/1/A%20Convolutional%20Neural%20Network%20%28CNN%29%20Classification%20Model.pdf
http://umpir.ump.edu.my/id/eprint/40032/
https://doi.org/10.30630/jitsi.4.3.181
https://doi.org/10.30630/jitsi.4.3.181
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.40032
record_format eprints
spelling my.ump.umpir.400322024-02-07T07:30:06Z http://umpir.ump.edu.my/id/eprint/40032/ A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy Siti Hawa, Apandi Jamaludin, Sallim Rozlina, Mohamed QA75 Electronic computers. Computer science QA76 Computer software Game and Online Video Streaming are the most viewed web pages. Users who spend too much time on these types of web pages may suffer from internet addiction. Access to Game and Online Video Streaming web pages should be restricted to combat internet addiction. A tool is required to recognise the category of web pages based on the text content of the web pages. Due to the unavailability of a matrix representation that can handle long web page text content, this study employs a document representation known as word cloud image to visualise the words extracted from the text content web page after data pre-processing. The most popular words are shown in large size and appear in the centre of the word cloud image. The most common words are the words that appear frequently in the text content web page and are related to describing what the web page content is about. The Convolutional Neural Network (CNN) recognises the pattern of words presented in the core portions of the word cloud image to categorise the category to which the web page belongs. The proposed model for web page classification has been compared with the other web page classification models. It shows the good result that achieved an accuracy of 85.6%. It can be used as a tool that helps to make identifying the category of web pages more accurate Department of Information Technology - Politeknik Negeri Padang 2023 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/40032/1/A%20Convolutional%20Neural%20Network%20%28CNN%29%20Classification%20Model.pdf Siti Hawa, Apandi and Jamaludin, Sallim and Rozlina, Mohamed (2023) A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy. Jurnal Ilmiah Teknologi Sistem Informasi, 4 (3). pp. 110-121. ISSN Print ISSN : 2722-4619 ; Online ISSN : 2722-4600. (Published) https://doi.org/10.30630/jitsi.4.3.181 https://doi.org/10.30630/jitsi.4.3.181
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
QA76 Computer software
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
Siti Hawa, Apandi
Jamaludin, Sallim
Rozlina, Mohamed
A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy
description Game and Online Video Streaming are the most viewed web pages. Users who spend too much time on these types of web pages may suffer from internet addiction. Access to Game and Online Video Streaming web pages should be restricted to combat internet addiction. A tool is required to recognise the category of web pages based on the text content of the web pages. Due to the unavailability of a matrix representation that can handle long web page text content, this study employs a document representation known as word cloud image to visualise the words extracted from the text content web page after data pre-processing. The most popular words are shown in large size and appear in the centre of the word cloud image. The most common words are the words that appear frequently in the text content web page and are related to describing what the web page content is about. The Convolutional Neural Network (CNN) recognises the pattern of words presented in the core portions of the word cloud image to categorise the category to which the web page belongs. The proposed model for web page classification has been compared with the other web page classification models. It shows the good result that achieved an accuracy of 85.6%. It can be used as a tool that helps to make identifying the category of web pages more accurate
format Article
author Siti Hawa, Apandi
Jamaludin, Sallim
Rozlina, Mohamed
author_facet Siti Hawa, Apandi
Jamaludin, Sallim
Rozlina, Mohamed
author_sort Siti Hawa, Apandi
title A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy
title_short A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy
title_full A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy
title_fullStr A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy
title_full_unstemmed A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy
title_sort convolutional neural network (cnn) classification model for web page: a tool for improving web page category detection accuracy
publisher Department of Information Technology - Politeknik Negeri Padang
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40032/1/A%20Convolutional%20Neural%20Network%20%28CNN%29%20Classification%20Model.pdf
http://umpir.ump.edu.my/id/eprint/40032/
https://doi.org/10.30630/jitsi.4.3.181
https://doi.org/10.30630/jitsi.4.3.181
_version_ 1822924112699326464
score 13.232414

Similar Items