Please use this identifier to cite or link to this item:
http://dspace.unimap.edu.my:80/xmlui/handle/123456789/41827
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Muhammad Syahir, Shah Kholl Ajam | - |
dc.date.accessioned | 2016-06-02T07:49:06Z | - |
dc.date.available | 2016-06-02T07:49:06Z | - |
dc.date.issued | 2015-06 | - |
dc.identifier.uri | http://dspace.unimap.edu.my:80/xmlui/handle/123456789/41827 | - |
dc.description | Access is limited to UniMAP community. | en_US |
dc.description.abstract | The vast increase of available documents in the World Wide Web (WWW) and the ease access to these documents has lead to a serious problem of using other’s works without giving credits. Although many methods have been developed to detect some instances of plagiarism such as changing the structure of sentences or when slightly replacing words by their synonyms, it is often hard to reveal plagiarism when the copied sentences are deliberately modified. This project proposes an algorithm for plagiarism detection by using syntactic plagiarism detection using 1-gram and 2-gram. Jaccard similarity coefficient is applied to detect similarity between documents of English corpus in engineering field by using C programming language. From the value of the results which is precision, recall and f-measure, we considered 2-gram showed the great potential for the plagiarism detection method. The 2-gram extraction achieved values 0.983 for precision, 0.380 for recall and 0.548 for f-measure compared to 1-gram extraction. Jaccard similarity coefficient incorporation with N-gram method is suitable sufficiently to be employed in the word similarity measurement. In efficiency measurement, the program performance can deal appropriately with high stability to calculate the word similarity. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Universiti Malaysia Perlis (UniMAP) | en_US |
dc.subject | Plagiarism | en_US |
dc.subject | N-Gram model | en_US |
dc.subject | Plagiarism detection | en_US |
dc.subject | Plagiarism detection -- Methods | en_US |
dc.title | Plagiarism detection using N-Gram model | en_US |
dc.type | Learning Object | en_US |
dc.contributor.advisor | Dr. Nik Adilah Hanin Zahri | en_US |
dc.publisher.department | School of Computer and Communication Engineering | en_US |
Appears in Collections: | School of Computer and Communication Engineering (FYP) |
Files in This Item:
File | Description | Size | Format | |
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Abstract,Acknowledgement.pdf | 2.66 MB | Adobe PDF | View/Open | |
Introduction.pdf | 3.64 MB | Adobe PDF | View/Open | |
Literature Review.pdf | 885.59 kB | Adobe PDF | View/Open | |
Methodology.pdf | 6.07 MB | Adobe PDF | View/Open | |
Results and Discussion.pdf | 3.77 MB | Adobe PDF | View/Open | |
Conclusion and Recommendation.pdf | 1.57 MB | Adobe PDF | View/Open | |
Refference and Appendics.pdf | 3.1 MB | Adobe PDF | View/Open |
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