Sumpeno, Surya and Arifin, Agus Zainal and Hariadi, Mochamad and Purnomo, Mauridhi Hery (2010) A Performance Evaluation of Classifiers Employ Language Dependent Tools for Indonesian Text. In: Scientific Article of 11Th Seminar On Intelligent Technology and ITS Application (SITIA 2010).
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Abstract
This paper evaluates the performance of Maximum
Entropy (MaxEnt), Support Vector Machine (SVM) and Na¨ıve
Bayes (NB) techniques for Indonesian text classification. Performance
of MaxEnt and SVM techniques are compared against
baseline NB technique. We also investigate the effect of language
dependent tools such as Indonesian stemming and stop words
removal can have on these techniques for text classification performances.
Up to now, there is no experimental report about the
effect of Indonesian stemmer on the text classification accuracy.
From our experiments, we conclude that maximum entropy
performs better than other classifiers in general. Language
dependent tools such as stemming and stop words removal have
only little effect on the accuracy of text classification. However
stemmed approach scored highest average accuracy and due to
the dimension reduction of feature vectors used in classification,
make this approach is viable step in pre-processing stage.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Collection ID : 51105120000032 Call Number : 005.13 Sum p |
| Uncontrolled Keywords: | maximum entropy, support vector machine, na¨ıve bayes, indonesian text classification, language dependent tool, stopwords removal, stemming |
| Subjects: | Q Science > Q Science (General) > Q370 Entropy (Information theory) |
| Divisions: | Faculty of Industrial Technology > Electrical Engineering |
| Depositing User: | - Davi Wah |
| Date Deposited: | 08 Oct 2019 03:06 |
| Last Modified: | 08 Oct 2019 03:06 |
| URI: | http://repository.its.ac.id/id/eprint/71038 |
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