TY - JOUR
T1 - Cross-language plagiarism detection system using latent semantic analysis and learning vector quantization
AU - Ratna, Anak Agung Putri
AU - Purnamasari, Prima Dewi
AU - Adhi, Boma Anantasatya
AU - Ekadiyanto, F. Astha
AU - Salman, Muhammad
AU - Mardiyah, Mardiyah
AU - Winata, Darien Jonathan
N1 - Publisher Copyright:
© 2017 by the authors.
PY - 2017/6/1
Y1 - 2017/6/1
N2 - 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.
AB - 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.
KW - Latent Semantic Analysis
KW - Learning vector quantization
KW - Plagiarism detection system
UR - http://www.scopus.com/inward/record.url?scp=85021264885&partnerID=8YFLogxK
U2 - 10.3390/a10020069
DO - 10.3390/a10020069
M3 - Article
AN - SCOPUS:85021264885
SN - 1999-4893
VL - 10
JO - Algorithms
JF - Algorithms
IS - 2
M1 - 69
ER -