Cross-Language Plagiarism Detection System Using Latent Semantic Analysis and Learning Vector Quantization +
Footnote †: 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. [PERSON]  
Correspondence: [EMAIL_ADDRESS]; Tel. : +62-21-727-0078  
[PERSON]  
Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia; [EMAIL_ADDRESS] (P.D.P. ); [PERSON]IL_ADDRESS] (B.A.A. ); [EMAIL_ADDRESS] (F.A.E. ); [PERSON]_ADDRESS] (M.S. ); [EMAIL_ADDRESS] (M.M. ); [EMAIL_ADDRESS] (D.J.W.) P. E.  
characters with graphically identical symbols from foreign alphabets or inserting letters in white font color [3]. \"Undue paraphrasing\" means the intentional rewriting of foreign thoughts in the vocabulary and style of the plagiarist without giving due credit for concealing the original source [2]. \"Idea plagiarism\" encompasses the usage of a broader foreign concept without due source acknowledgement [4]. \"Self-plagiarism\" characterizes the partial or complete reuse of one's own previous writings not being justified by scientific goals, e.g., for presenting updates or providing access to a larger community, but primarily serving the author, e.g., for artificially increasing citation counts [5; 6]. Hence, it is necessary to create countermeasures; one of which is checking the submitted papers for any indication of plagiarism and apply penalties to its authors. Manual plagiarism detection; however; is time consuming and requires a lot of effort, diminished its effectiveness. An automated plagiarism detection system; so-called computer-assisted plagiarism detection; is needed to able to detect cross-language similarities between the documents which are currently under development. Automated plagiarism detection is the process of determining plagiarism in a text-based paper by using software. Some methods can be used to perform textual plagiarism detection automatically, such as checking the grammatical structure of the document (grammar based), using vector-space models to detect similarities between two documents (semantic based), using a reference corpus of documents whose contents may have been plagiarized, using a clustering method, or combining two or more of these techniques [7]. Currently, the majority of academic sources (such as journals, proceedings, and books) are written in English, and the action of plagiarism often involves language translation process. In order to provide better tools for local publishers, the plagiarism detection system developed in this research focuses on bilingual plagiarism detection. This paper discusses a case study in which the document tested for plagiarism is in the Indonesian language while the suspected source of plagiarism is written in English. Since the sentences' structure between Indonesian and English are very different, structure- and grammar-based detection techniques are highly sophisticated. In this research, the detection method is conducted using a semantic-based technique. The cross-language plagiarism detection system needs to be a semantic-based computer-assisted plagiarism detector and the choice of algorithm used in the system is therefore Latent Semantic Analysis (LSA). The algorithm does not only find the similarity between two documents, but it is also supported by a language translation process. Since the detection is based on semantics, current work only focuses on word translation without paying attention to grammatical rules. This is because semantics only views a document as interrelated words and the frequency of those words within, removing the necessity for applying accurate language rules. The method proposed in this research uses LSA, which previously showed good results in an essay grading system.