PERINGKASAN TEKS BAHASA INDONESIA PADA CERPEN MENGGUNAKAN METODE LATENT SEMANTIC ANALYSIS (LSA)

KASIM, NABILAH THAHIRAH and Yusliani, Novi and Fachrurrozi, Muhammad (2022) PERINGKASAN TEKS BAHASA INDONESIA PADA CERPEN MENGGUNAKAN METODE LATENT SEMANTIC ANALYSIS (LSA). Undergraduate thesis, Sriwijaya University.

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Abstract

Cerpen or short stories are entertaining readings for readers of all ages. In the presentation, often the category of naming the title and presenting does not represent the content of the written story, so it will cause problems when finding the desired poem. Finally, to find out from a reader's knowledge it is necessary to understand the whole of the short story which of course takes a very long time. Another alternative to get information and understand a value-based quickly is to read the summary or synopsis of the short story. Automated text summarization is research in the field of assisting natural language to enable readers to summarize a book more efficiently to get to the heart of the story. In this study, text summarization was carried out using the Latent Semantic Analysis (LSA) algorithm. The test was carried out using test data of 30 short stories texts and the level of compression of the summary sentences was as much as 30% of the number of short stories text sentences. In this study, there are three stages, first text preprocessing, next calculating the weight of TF-IDF, and the last determining the sentence that will be a summary by calculating Latent Semantic Analysis (LSA). The level of text summary results is measured by the calculation of recall, precision, and f-measure. This study resulted in the value of the text in the short story having an average precision of 74.93%, recall of 68.4%, and f-measure of 71.43%. Based on the accuracy it can be said that it gives summary results that resemble the results of a manual summary quite well in describing the contents of the entire short story.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Automatic Text Summarization, TF-IDF, Latent Semantic Analysis.
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Ms. Nabilah Thahirah Kasim
Date Deposited:	25 Aug 2022 03:57
Last Modified:	25 Aug 2022 03:57
URI:	http://repository.unsri.ac.id/id/eprint/77791

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