PLAGIARISM CHECKER FOR ACADEMIC PROJECT ABSTRACTS
DOI:
https://doi.org/10.48047/hsq7h845Keywords:
.Abstract
With the easy access to digital content, upholding academic integrity has become critical. Plagiarism detection software is essential in various research niche underlining many copies from writing abstract within academic projects. The method proposed in this paper combines n-gram matching with machine learning methods to improve plagiarism detection. The research proposes a two-stage approach that improves the accuracy of TEXT, through the use of n-grams for capturing patterns in text and machine learning algorithms to enhance the probability of detecting plagiarized material. The dataset is an open-source database and the system achieves higher precision, recall and F1-score than existing solutions when tested on a set of academic abstracts. The results show that the hybrid model is an effective approach for enhancing plagiarism detection systems in the educational context.
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