Plagiarism Detection in Students' Theses Using The Cosine Similarity Method
The main requirement for graduation from students is to make a final scientific paper. One of the factors determining the quality of a student's scientific work is the uniqueness and innovation of the work. This research aims to apply data mining methods to detect similarities in titles, abstra...
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Politeknik Ganesha Medan
2021-05-01
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doaj-843cabd86c2e425392a72b0fa58a570a2021-05-01T16:43:25ZengPoliteknik Ganesha MedanSinkron2541-044X2541-20192021-05-015230531310.33395/sinkron.v5i2.109091173Plagiarism Detection in Students' Theses Using The Cosine Similarity MethodOppi Anda RestaAddin AdityaFebry Eka PurwiantonoThe main requirement for graduation from students is to make a final scientific paper. One of the factors determining the quality of a student's scientific work is the uniqueness and innovation of the work. This research aims to apply data mining methods to detect similarities in titles, abstracts, or topics of students' final scientific papers so that plagiarism does not occur. In this research, the cosine similarity method is combined with the preprocessing method and TF-IDF to calculate the level of similarity between the title and the abstract of a student's final scientific paper, then the results will be displayed and compared with the existing final project repository based on the threshold value to make a decision whether scientific work can be accepted or rejected. Based on the test data and training data that has been applied to the TF-IDF method, it shows that the percentage level of similarity between the training data document and the test data document is 8%. This shows that the student thesis is still classified as unique and does not contain plagiarism content. The findings of this study can help the university in managing the administration of student theses so that plagiarism does not occur. Furthermore, it is necessary to study further adding methods to increase the accuracy of system performance so that when the process is run the system will work faster and optimally.https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10909plagiarismtext miningcosine similaritytf-idfstudent theses |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Oppi Anda Resta Addin Aditya Febry Eka Purwiantono |
spellingShingle |
Oppi Anda Resta Addin Aditya Febry Eka Purwiantono Plagiarism Detection in Students' Theses Using The Cosine Similarity Method Sinkron plagiarism text mining cosine similarity tf-idf student theses |
author_facet |
Oppi Anda Resta Addin Aditya Febry Eka Purwiantono |
author_sort |
Oppi Anda Resta |
title |
Plagiarism Detection in Students' Theses Using The Cosine Similarity Method |
title_short |
Plagiarism Detection in Students' Theses Using The Cosine Similarity Method |
title_full |
Plagiarism Detection in Students' Theses Using The Cosine Similarity Method |
title_fullStr |
Plagiarism Detection in Students' Theses Using The Cosine Similarity Method |
title_full_unstemmed |
Plagiarism Detection in Students' Theses Using The Cosine Similarity Method |
title_sort |
plagiarism detection in students' theses using the cosine similarity method |
publisher |
Politeknik Ganesha Medan |
series |
Sinkron |
issn |
2541-044X 2541-2019 |
publishDate |
2021-05-01 |
description |
The main requirement for graduation from students is to make a final scientific paper. One of the factors determining the quality of a student's scientific work is the uniqueness and innovation of the work. This research aims to apply data mining methods to detect similarities in titles, abstracts, or topics of students' final scientific papers so that plagiarism does not occur. In this research, the cosine similarity method is combined with the preprocessing method and TF-IDF to calculate the level of similarity between the title and the abstract of a student's final scientific paper, then the results will be displayed and compared with the existing final project repository based on the threshold value to make a decision whether scientific work can be accepted or rejected. Based on the test data and training data that has been applied to the TF-IDF method, it shows that the percentage level of similarity between the training data document and the test data document is 8%. This shows that the student thesis is still classified as unique and does not contain plagiarism content. The findings of this study can help the university in managing the administration of student theses so that plagiarism does not occur. Furthermore, it is necessary to study further adding methods to increase the accuracy of system performance so that when the process is run the system will work faster and optimally. |
topic |
plagiarism text mining cosine similarity tf-idf student theses |
url |
https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10909 |
work_keys_str_mv |
AT oppiandaresta plagiarismdetectioninstudentsthesesusingthecosinesimilaritymethod AT addinaditya plagiarismdetectioninstudentsthesesusingthecosinesimilaritymethod AT febryekapurwiantono plagiarismdetectioninstudentsthesesusingthecosinesimilaritymethod |
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