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.v5.1599

Cited by 3 publications

(7 citation statements)

References 12 publications

(5 reference statements)

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“…The stages of the Horspool Boyer-Moore algorithm were preprocessing, needle word-trigrams formation, bad-character table formation, and haystack-needle matching. The illustration of the different stages of the Rabin-Karp, Winnowing, and Horspool Boyer-Moore algorithms [4], [14], [24], [26], [29], [30], [31], [32] in identifying similarity based on word-level trigrams is presented in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, to avoid collisions, sub-strings should be converted into the hash, valuessing the Rolling Hash formula [37]. The Rolling Hash formula is written as follows [29], [38]:…”

Section: Hash Values Formationmentioning

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%

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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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Section: Methodsmentioning

confidence: 99%

“…Therefore, to avoid collisions, sub-strings should be converted into the hash, valuessing the Rolling Hash formula [37]. The Rolling Hash formula is written as follows [29], [38]:…”

Section: Hash Values Formationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Similarity Identification Based on Word Trigrams Using Exact String Matching Algorithms

2022

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“…Ulinnuha dkk. juga mengimplementasikan algoritma winnowing untuk mendetaksi plagiarisme dari nilai persentase kesamaan antara dokumen asli dengan dokuman-dokumen uji dan menghasilkan akurasi hingga 91% (Ulinnuha et al, 2019). Implementasi algoritma winnowing untuk mendeteksi plagiarisme juga digunakan oleh Arifin dkk.…”

Section: Pendahuluanunclassified

EVALUASI ESAI OTOMATIS DENGAN ALGORITMA NAZIEF & ADRIANI DAN WINNOWING

Rahim

2022

JTI

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Pemanfaatan teknologi informasi diharapkan dapat memudahkan aktifitas sehari-hari tidak terkecuali proses evaluasi hasil belajar mahasiswa olah dosen. Pemeriksaan hasil ujian esai secara manual tentu jauh dari kata efektif dan efisien sehingga penelitian ini memanfaatkan fingerprint yang diperoleh dari nilai hash kumpulan teks menggunakan algoritma Winnowing untuk selanjutnya dihitung nilai kesamaannya menggunakan Jaccard’s Similarity Coeficient. Sebelum proses perhitungan, jawaban dalam bentuk teks bahasa Indonesai melalui pre-prosesing dan menggunakan algoritma Nazief & Andriani sebagai stemmer sehingga dapat memperoleh hasil evaluasi 30 jawaban siswa yang dibandingkan dengan kunci jawaban dalam waktu 1,62 detik dengan nilai rata-rata kesamaan 81,20%.

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“…Pengukuran similaritas menggunakan Jaccard Coefficient. Hasil penelitian menunjukkan bahwa sistem mampu mendeteksi plagiarisme dengan sensitivitas 82%, spesifisitas 100%, dan akurasi 91% [10].…”

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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Implementation of Winnowing Algorithm for Document Plagiarism Detection

Cited by 3 publications

References 12 publications

Similarity Identification Based on Word Trigrams Using Exact String Matching Algorithms

Similarity Identification Based on Word Trigrams Using Exact String Matching Algorithms

EVALUASI ESAI OTOMATIS DENGAN ALGORITMA NAZIEF & ADRIANI DAN WINNOWING

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

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