INTEGRATED TEXT CLASSIFICATION METHOD FOR INDONESIAN NEWS DOCUMENTS

Textclassification hasbeena popular research field in the areaof computer science. It deals with the assignment of labels into a group of similar textual document. However, there have been very limited approaches which are focused on improving the unique character of news corpus, even less for In...

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Main Author: FUDDOLY, AINI RACHMANIA KUSUMAAGAMA
Format: Thesis
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.22685 /
Published: 2013
Subjects:
QA75 Electronic computers. Computer science
Online Access: http://utpedia.utp.edu.my/22685/1/2013-COMPUTER%20AND%20INFORMATION-INTEGRATED%20TEXT%20CLASSIFICATION%20METHOD%20FOR%20INDONESIAN%20NEWS%20DOCUMENTS-AINI%20RACHMANIA%20KUSUMAAGAMA%20FUDDOLY.pdf
http://utpedia.utp.edu.my/22685/
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spelling utp-utpedia.226852022-02-22T06:48:40Z http://utpedia.utp.edu.my/22685/ INTEGRATED TEXT CLASSIFICATION METHOD FOR INDONESIAN NEWS DOCUMENTS FUDDOLY, AINI RACHMANIA KUSUMAAGAMA QA75 Electronic computers. Computer science Textclassification hasbeena popular research field in the areaof computer science. It deals with the assignment of labels into a group of similar textual document. However, there have been very limited approaches which are focused on improving the unique character of news corpus, even less for Indonesian news document. Apart from that, onlyfew were aimed at categorizing and identifying topics. The aim of this study is to develop a text classifier for Indonesian news articles based on Bracewell's algorithm and top-n keywords selection method to improve the online news corpora challenges: the significant difference among articles m one category, the high computational time,andthe possibility of a new, previously-never-existed topic. 2013 Thesis NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/22685/1/2013-COMPUTER%20AND%20INFORMATION-INTEGRATED%20TEXT%20CLASSIFICATION%20METHOD%20FOR%20INDONESIAN%20NEWS%20DOCUMENTS-AINI%20RACHMANIA%20KUSUMAAGAMA%20FUDDOLY.pdf FUDDOLY, AINI RACHMANIA KUSUMAAGAMA (2013) INTEGRATED TEXT CLASSIFICATION METHOD FOR INDONESIAN NEWS DOCUMENTS. Masters thesis, UTP.
institution Universiti Teknologi Petronas
collection UTPedia
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
FUDDOLY, AINI RACHMANIA KUSUMAAGAMA
INTEGRATED TEXT CLASSIFICATION METHOD FOR INDONESIAN NEWS DOCUMENTS
description Textclassification hasbeena popular research field in the areaof computer science. It deals with the assignment of labels into a group of similar textual document. However, there have been very limited approaches which are focused on improving the unique character of news corpus, even less for Indonesian news document. Apart from that, onlyfew were aimed at categorizing and identifying topics. The aim of this study is to develop a text classifier for Indonesian news articles based on Bracewell's algorithm and top-n keywords selection method to improve the online news corpora challenges: the significant difference among articles m one category, the high computational time,andthe possibility of a new, previously-never-existed topic.
format Thesis
author FUDDOLY, AINI RACHMANIA KUSUMAAGAMA
author_sort FUDDOLY, AINI RACHMANIA KUSUMAAGAMA
title INTEGRATED TEXT CLASSIFICATION METHOD FOR INDONESIAN NEWS DOCUMENTS
title_short INTEGRATED TEXT CLASSIFICATION METHOD FOR INDONESIAN NEWS DOCUMENTS
title_full INTEGRATED TEXT CLASSIFICATION METHOD FOR INDONESIAN NEWS DOCUMENTS
title_fullStr INTEGRATED TEXT CLASSIFICATION METHOD FOR INDONESIAN NEWS DOCUMENTS
title_full_unstemmed INTEGRATED TEXT CLASSIFICATION METHOD FOR INDONESIAN NEWS DOCUMENTS
title_sort integrated text classification method for indonesian news documents
publishDate 2013
url http://utpedia.utp.edu.my/22685/1/2013-COMPUTER%20AND%20INFORMATION-INTEGRATED%20TEXT%20CLASSIFICATION%20METHOD%20FOR%20INDONESIAN%20NEWS%20DOCUMENTS-AINI%20RACHMANIA%20KUSUMAAGAMA%20FUDDOLY.pdf
http://utpedia.utp.edu.my/22685/
_version_ 1741195850750623744
score 11.62408

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