Hakim, Ari Aulia and Erwin, Alva and Eng, Kho I (2014) Automated Document Classification for News Article in Bahasa Indonesia Based on Term Frequency Inverse Document Frequency (TF-IDF) Approach. Bachelor thesis, Swiss German University.

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Abstract

The exponential growth of the data both in the digital or printed media may lead us to the information explosion era, where most of the data cannot be maintained easily. The research in the text mining might prevent the world to enter that era. One of the text mining studies that can help in maintaining the data is automated text classification. This research can classify one or more articles based on predefined categories. Automated text classification can be considered important, due to the big number of the data exist, and text classification may not be handled manually, because it will consume a lot of time and human resource. Then, the classifier developed by implementing term frequency inverse document frequency (TF-IDF).

Item Type:	Thesis (Bachelor)
Uncontrolled Keywords:	Text Mining, Automated Document Classification, TF-IDF
Subjects:	T Technology > T Technology (General) > T58.5 Information technology
Divisions:	Faculty of Engineering and Information Technology > Department of Information Technology
Depositing User:	Faisal Ifzaldi
Date Deposited:	04 May 2021 15:29
Last Modified:	04 May 2021 15:29
URI:	http://repository.sgu.ac.id/id/eprint/1999

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