Implementation of Winnowing Algorithm for Document Plagiarism Detection

Ulinnuha, Nurissaidah; Thohir, Muhammad; Novitasari, Dian Candra Rini; Asyhar, Ahmad Hanif; Arifin, Ahmad Zaenal

doi:10.11591/eecsi.v5i5.1599

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…This input will be matched with the answer key text document to obtain a level of similarity, which will generate the correct answer value for the answer key automatically. The input of this algorithm is a text document that is processed to produce output in the form of a collection of hash values called fingerprints [31]. This fingerprint is used as a basis for comparison between the entered text file and the teacher's answer key so that the level of similarity of the text is obtained.…”

Section: Methodsmentioning

confidence: 99%

Automatic essay assessment in e-learning using winnowing algorithm

Amalia

Lestari

Wijayaningrum

et al. 2022

IJEECS

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<span>The pandemic has caused almost all educational institutions to use online learning media to support learning activities. E-learning is a technology that is widely used because it can accommodate all learning activities. However, in general, e-learning can only perform automatic assessments for multiple choice answers but not for essay answers, so that manual assessment by the teacher becomes difficult and takes a long time. In this study, the winnowing algorithm was applied to the automatic assessment process on students' essay answers by measuring their similarity to the teacher's answer key. The stages in the automatic assessment using the winnowing algorithm begin with forming a series of k-grams, calculating the hash value, forming a window from the hash value, calculating the fingerprint value, and calculating the Jaccard Coefficient to obtain the percentage of text similarity results. The test results show that the winnowing algorithm can provide good performance when the answers to questions are in the form of short entries with the number of hashes not smaller than the window value. Meanwhile, on questions with long answers, the winnowing algorithm can still work well with an average difference of 5.2% from the results of the assessment carried out by the teacher.</span>

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Section: Methodsmentioning

confidence: 99%

Automatic essay assessment in e-learning using winnowing algorithm

Amalia

Lestari

Wijayaningrum

et al. 2022

IJEECS

View full text Add to dashboard Cite

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Similarity Identification Based on Word Trigrams Using Exact String Matching Algorithms

2022

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Several studies regarding excellent exact string matching algorithms can be used to identify similarity, including the Rabin-Karp, Winnowing, and Horspool Boyer-Moore algorithms. In determining similarities, the Rabin-Karp and Winnowing algorithms use fingerprints, while the Horspool Boyer-Moore algorithm uses a bad-character table. However, previous research focused on identifying similarities using these algorithms based on character n-gram. In contrast, identification based on the word n-gram to determine the similarity based on its linguistic meaning, especially for longer strings, had not been covered yet. Therefore, a word-level trigram was proposed to identify similarities based on the word trigrams using the three algorithms and compare each performance. Based on precision, recall, and running time comparison, the Rabin-Karp algorithm results were 100%, 100%, and 0.19 ms, respectively; the Winnowing algorithm results with the smallest window were 100%, 56%, and 0.18 ms, respectively; and the Horspool algorithm results were 100%, 100%, and 0.06 ms. From these results, it can be concluded that the performance of the Horspool Boyer-Moore algorithm is better in terms of precision, recall, and running time.

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Winnowing vs Extended-Winnowing: A Comparative Analysis of Plagiarism Detection Algorithms

Shrestha¹,

Shakya²,

Gautam³

2023

JTCSST

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Plagiarism is the main problem in the digital world, as people use others’ content without giving prior credit to the creator. Therefore, there should be proper and efficient algorithms to find plagiarized content on the Internet. This research proposes two algorithms: the winnowing algorithm and the extended winnowing algorithm. The winnowing algorithm can only calculate the similarity rate between documents, whereas the extended algorithm can mark the plagiarized text segment in the compared records along with their similarity rates. The similarity rate in both algorithms has been calculated using the Jaccard Coefficient. Although the extended algorithm is beneficial as it provides a text marking feature, it consumes more computation power, which is discussed in this study. There are research works done previously using this approach, but none has compared the algorithms’ performance on small texts. Thus, this research utilizes the Twitter form of data to test these algorithms’ performance, as it contains a maximum of 280 characters. The application proposed to detect plagiarism in tweets has been developed using Python as the backend and React as the front-end technology.

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Implementation of Winnowing Algorithm for Document Plagiarism Detection

Cited by 3 publications

References 6 publications

Automatic essay assessment in e-learning using winnowing algorithm

Automatic essay assessment in e-learning using winnowing algorithm

Similarity Identification Based on Word Trigrams Using Exact String Matching Algorithms

Winnowing vs Extended-Winnowing: A Comparative Analysis of Plagiarism Detection Algorithms

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