Automated Document Classification for News Article in Bahasa Indonesia Based on Term Frequency Inverse Document Frequency (TF-IDF) Approach

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Automated Document Classification for News Article in Bahasa Indonesia Based on Term Frequency Inverse Document Frequency (TF-IDF) Approach

Hakim, Ari Aulia - Personal Name; Erwin, Alva - Personal Name; Kho, I Eng - Personal Name;

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).

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B01612 (Rack Thesis) Available

Detail Information

Series Title: -
Call Number: 1612
Publisher: : Swiss German University., 2014
Collation: -
Language: English
ISBN/ISSN: -
Classification: NONE
Content Type: -
Media Type: -
Carrier Type: -
Edition: -
Subject(s): IT
Text mining
Automated document classification
TF-IDF
Specific Detail Info: -
Statement of Responsibility: -

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