Cross-Language Plagiarism Detection System Using Latent Semantic Analysis and Learning Vector Quantization †
Department of Electrical Engineering, Faculty of Enginering, Universitas Indonesia, Depok 16424, Indonesia
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Author to whom correspondence should be addressed.
†
This paper is an extended version of a conference paper entitled “Analysis of the Effect of Term–document Matrix on the Accuracy of Latent Semantic Analysis-Based Cross-Language Plagiarism Detection” presented at the International Conference on Network, Communication and Computing 2016 (ICNCC 2016) at Kyoto, Japan, 17–20 December 2016.
Academic Editor: Andras Farago
Algorithms 2017, 10(2), 69; https://doi.org/10.3390/a10020069
Received: 31 March 2017 / Revised: 16 May 2017 / Accepted: 10 June 2017 / Published: 13 June 2017
(This article belongs to the Special Issue Networks, Communication, and Computing)
Abstract
Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising). Due to the syntax disparity between Bahasa Indonesia and English, most of the existing methods for automated cross-language plagiarism detection do not provide satisfactory results. This paper analyses the probability of developing Latent Semantic Analysis (LSA) for a computerized cross-language plagiarism detector for two languages with different syntax. To improve performance, various alterations in LSA are suggested. By using a linear vector quantization (LVQ) classifier in the LSA and taking into account the Frobenius norm, output has reached up to 65.98% in accuracy. The results of the experiments showed that the best accuracy achieved is 87% with a document size of 6 words, and the document definition size must be kept below 10 words in order to maintain high accuracy. Additionally, based on experimental results, this paper suggests utilizing the frequency occurrence method as opposed to the binary method for the term–document matrix construction. View Full-Text
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ratna, A.A.P.; Purnamasari, P.D.; Adhi, B.A.; Ekadiyanto, F.A.; Salman, M.; Mardiyah, M.; Winata, D.J. Cross-Language Plagiarism Detection System Using Latent Semantic Analysis and Learning Vector Quantization . Algorithms 2017, 10, 69.
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