Evaluation of the accuracy of winnowing, rabin karp and knuth morris pratt algorithms in plagiarism detection applications

Widaningrum, Ida; Mustikasari, Dyah; Arifin, Rizal; Pratiwi, H. A.

doi:10.1088/1742-6596/1517/1/012093

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…The Cosine Similarity algorithm has also been used to check document similarity by producing a similarity level of 23% [39]. While the Knuth Morris Pratt algorithm provides an accuracy value of 9.20% to detect the similarity of two documents [25]. The comparison of the accuracy of the winnowing algorithm and several other algorithms used to check the similarity of the two documents can be seen in Table 9.…”

Section: Comparison Of Winnowing With Other Algorithmsmentioning

confidence: 99%

“…This algorithm can increase the efficiency of the document comparison process because of the concept of a window so that a hash value is formed, which is then used in the document matching process. In addition, the winnowing algorithm is also proven to provide a much greater accuracy value than Rabin Karp and Knuth Morris Pratt when used to detect plagiarism in documents [25].…”

Section: Introductionmentioning

confidence: 99%

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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: Comparison Of Winnowing With Other Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic essay assessment in e-learning using winnowing algorithm

Amalia

Lestari

Wijayaningrum

et al. 2022

IJEECS

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“…Jaccard Coefficient digunakan untuk menghitung nilai similaritas pada algoritma Winnowing dan Dice's Coefficient pada algoritma Rabin-Karp. Hasil penelitian menunjukkan algoritma Winnowing lebih unggul daripada algoritma Rabin-Karp dan algoritma KMP karena algoritma Winnowing dapat mendeteksi plagiarisme yang kompleks [14].…”

Section: Pendahuluanunclassified

Penerapan Algoritma Winnowing dan Word-Level Trigrams Untuk Mengidentifikasi Kesamaan Kata

Ramdhani

Fadlil²,

Sunardi³

2022

Jur. Ris. Kom.

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Identifying the same words in two or more texts is the first step in the process of detecting plagiarism. Plagiarism detection software are commercially available but relatively expensive. Although some software is offered for free, the features provided are very limited. Therefore, a word similarity detection system is needed to be used as an alternative for users that can be freely accessed. The application of the pattern matching method is one of the solutions that can be used to find the similarity of words between documents. There are several algorithms that can be used as a method to find the similarity of words in the text, including the Winnowing algorithm which is known to have good performance in detecting similarity of words. Winnowing is a hashing-approach based algorithm that applies hash-function and window formation to obtain fingerprints during pattern matching. Based on these fingerprints, the word similarity level can be calculated. Previous studies have only calculated the level of similarity of words based on the character (character-level), while the calculation of the level of similarity based on words (word-level) is still limited. This research was carried out with the aim of measuring the level of similarity of words using the Winnowing algorithm and word-level trigrams. The results showed that the Winnowing algorithm which was applied using word-level trigrams could detect similarities in the text of 76.84%, 52.29%, 37.40%, and 19.29%, respectively. From the results of the study, it can be concluded that the pattern matching method with the Winnowing algorithm and word-level trigrams can be used to measure the level of similarity of the text

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“…Meanwhile, in the Winnowing algorithm, fingerprints are determined by first grouping all hash values into windows based on a certain width. The rightmost minimum hash value obtained from each window is used as fingerprints [6], [7], [8], [9]. To prevent collisions in hash values, the Rolling Hash formula is used with the base number in the procedure using prime numbers [4], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

“…Winnowing algorithms as methods to identify word similarities between scientific work documents, the previous studies related to the application of both algorithms showed excellent results, either using in mono-language or multi-language forms and even using Chinese and Arabic letters [5], [9], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32].…”

Section: Introductionmentioning

confidence: 99%

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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Evaluation of the accuracy of winnowing, rabin karp and knuth morris pratt algorithms in plagiarism detection applications

Cited by 6 publications

References 5 publications

Automatic essay assessment in e-learning using winnowing algorithm

Automatic essay assessment in e-learning using winnowing algorithm

Penerapan Algoritma Winnowing dan Word-Level Trigrams Untuk Mengidentifikasi Kesamaan Kata

Similarity Identification Based on Word Trigrams Using Exact String Matching Algorithms

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