Information Retrieval System for Determining The Title of Journal Trends in Indonesian Language Using TF-IDF and Na?ve Bayes Classifier
| Main Authors: | Trihanto, Wandha Budhi, Arifudin, Riza, Muslim, Much Aziz |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Negeri Semarang
, 2017
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| Subjects: | |
| Online Access: |
https://journal.unnes.ac.id/nju/index.php/sji/article/view/11876 https://journal.unnes.ac.id/nju/index.php/sji/article/view/11876/6967 |
| ctrlnum |
article-11876 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">Information Retrieval System for Determining The Title of Journal Trends in Indonesian Language Using TF-IDF and Na?ve Bayes Classifier</title><creator>Trihanto, Wandha Budhi</creator><creator>Arifudin, Riza</creator><creator>Muslim, Much Aziz</creator><subject lang="en-US">TF-IDF; Nave Bayes Classifier; Trends.</subject><description lang="en-US">The journal is known as one of the relevant serial literature that can support a researcher in doing his research. In its development journal has two formats that can be accessed by library users namely: printed format and digital format. Then from the number of published journals, not accompanied by the growing amount of information and knowledge that can be retrieved from these documents. The TF-IDF method is one of the fastest and most efficient text mining methods to extract useful words as the value of information from a document. This method combines two concepts of weight calculation that is the frequency of word appearance on a particular document and the inverse frequency of documents containing the word. Furthermore, data analysis of journal title is done by Nave Bayes Classifier method. The purpose of the research is to build a website-based information retrieval system that can help to classify and define trends from Indonesian journal titles. This research produces a system that can be used to classify journal titles in Indonesian language, with system accuracy in determining the classification of 90,6% and 9,4% error rate. The highest percentage result that became the trend of title classification was decision support system category which was 24.7%.</description><publisher lang="en-US">Universitas Negeri Semarang</publisher><contributor lang="en-US"/><date>2017-11-10</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Journal:Article</type><type>File:application/pdf</type><identifier>https://journal.unnes.ac.id/nju/index.php/sji/article/view/11876</identifier><identifier>10.15294/sji.v4i2.11876</identifier><source lang="en-US">Scientific Journal of Informatics; Vol 4, No 2 (2017): November 2017; 179-190</source><source>2460-0040</source><source>2407-7658</source><source>10.15294/sji.v4i2</source><language>eng</language><relation>https://journal.unnes.ac.id/nju/index.php/sji/article/view/11876/6967</relation><rights lang="en-US">Copyright (c) 2017 Scientific Journal of Informatics</rights><recordID>article-11876</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other File:application/pdf File Journal:eJournal |
| author |
Trihanto, Wandha Budhi Arifudin, Riza Muslim, Much Aziz |
| title |
Information Retrieval System for Determining The Title of Journal Trends in Indonesian Language Using TF-IDF and Na?ve Bayes Classifier |
| publisher |
Universitas Negeri Semarang |
| publishDate |
2017 |
| topic |
TF-IDF Nave Bayes Classifier Trends |
| url |
https://journal.unnes.ac.id/nju/index.php/sji/article/view/11876 https://journal.unnes.ac.id/nju/index.php/sji/article/view/11876/6967 |
| contents |
The journal is known as one of the relevant serial literature that can support a researcher in doing his research. In its development journal has two formats that can be accessed by library users namely: printed format and digital format. Then from the number of published journals, not accompanied by the growing amount of information and knowledge that can be retrieved from these documents. The TF-IDF method is one of the fastest and most efficient text mining methods to extract useful words as the value of information from a document. This method combines two concepts of weight calculation that is the frequency of word appearance on a particular document and the inverse frequency of documents containing the word. Furthermore, data analysis of journal title is done by Nave Bayes Classifier method. The purpose of the research is to build a website-based information retrieval system that can help to classify and define trends from Indonesian journal titles. This research produces a system that can be used to classify journal titles in Indonesian language, with system accuracy in determining the classification of 90,6% and 9,4% error rate. The highest percentage result that became the trend of title classification was decision support system category which was 24.7%. |
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Scientific Journal of Informatics |
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Program Komputer dan Teknologi Informasi |
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KOTA SEMARANG |
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JAWA TENGAH |
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