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Maliyaem, Maleerat
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Plagiarism detection using text-representing centroids techniques Nualnim, Sureeporn; Maliyaem, Maleerat; Unger, Herwig
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1722-1734

Abstract

This study addresses the limitations of traditional plagiarism detection methods by introducing the text-representing centroid (TRC) technique. TRC is designed to improve the accuracy of detecting semantic similarities and sophisticated forms of plagiarism. It utilizes a co-occurrence graph to identify centroid terms that represent the core meaning of text documents, effectively capturing the contextual associations between terms. Extensive experiments were conducted on a dataset of academic papers to assess TRC’s performance against traditional techniques across various categories of plagiarism, including near-copy, modified-copy, and paraphrasing. The results demonstrate the effectiveness of the TRC technique, achieving an average precision of 0.96 and a recall of 0.71. This performance surpasses methods such as Jaccard and Cosine similarity in accurately detecting more, complex forms of plagiarism. These findings highlight TRC’s potential as a robust tool for both academic and industry applications, helping to ensure integrity in textual content through precise and comprehensive plagiarism detection.
Co-Authors Nualnim, Sureeporn Unger, Herwig