Kategorisasi dokumen web secara otomatis berdasarkan folksonomy menggunakan multinomial naive Bayes classifier
Hendy Irawan

TL;DR
This paper presents an automatic web document categorization system using multinomial naive Bayes classifier to address folksonomy's issues like inconsistent tags, demonstrating its effectiveness as a categorization aid.
Contribution
It introduces a novel application of multinomial naive Bayes for automatic folksonomy-based document classification, including implementation details and evaluation.
Findings
Bayes classifier effectively categorizes web documents
System can assist manual categorization processes
Implementation shows practical feasibility
Abstract
Folksonomy is a non-hierarchical document categorizing system, that treats every category in a flat manner, dan every category is entered freely by anyone who submitted a document in these categories. Categorization is done automatically at the time a document is submitted, by entering the list of categories that best fit the document. del.icio.us (http://del.icio.us) site is one of the most popular social bookmarking sites that uses folksonomy. Usage of folksonomy, although very easy, also has its weaknesses, such as use of different tags for the same concept, use of the same tag for different concepts, no quality control, etc. We try to provide a solution for some of these problems by analyzing Web documents' contents and categorizing them automatically using multinomial naive Bayes algorithm. Bayes classifier works by using a set of evidences and a set of classes. By training the…
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