Aristoteles, Aristoteles and Widarti, Widiarti and Wibowo, Eko Dwi Text Feature Weighting for Summarization of Documents Bahasa Indonesia by Using Binary Logistic Regression Algorithm. International Journal of Computer Science and Telecommunications, 5 (7). pp. 29-33. ISSN 2047-3338

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Abstract

The research was conducted the text feature weighting on Indonesian text by using binary logistic regression algorithms. Features of the text using text features eleven [1]. Eleven text features used are sentence position, positive keyword negative keywords, similarity between sentences, sentences that resemble the title sentence, sentences containing names of entities, sentences that contain numeric data, length of sentence, the connection between sentences, the sum of the weight of the connection between sentences, and sentence semantics. The purpose of this research was to conduct the optimization of summarization text by using binary logistic regression algorithm and the influence of the eleven features text by using binary logistic regression algorithms. Binary logistic regression algorithm used in compression rate 30%. The results of this research show the accuracy of compaction on the 30% compression rate amount 91.1% and on “positive keyword (f2)” can represent the eleven text features to perform compaction of text. Index Terms— Binary Logistic Regression Algorithm, Compression Rate and Text Features

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Ilmu Komputer
Depositing User:	Aristoteles Aristoteles
Date Deposited:	06 Jan 2017 01:25
Last Modified:	06 Jan 2017 01:25
URI:	http://repository.lppm.unila.ac.id/id/eprint/1358

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