Comparison between the Stemmer Porter Effect and Nazief-Adriani on the Performance of Winnowing Algorithms for Measuring Plagiarism

Stemming in each language has a different process and is determined according to the structure of the language. Stemming is mostly used as a complete step in the processing of words and phrases. There are many stemming algorithms available, and some used as a process for word processing. One function of stemming is to detect word errors in Indonesian. In this study, researchers created the Indonesian words error detection system using Nazief and Adriani algorithm. In the trials conducted, the system will accept text input obtained from the user. Then the system will preprocess the text. In this study, there are three stages of preprocessing, namely tokenization, case folding, and filtering. After the stages in preprocessing are finished, the system will call each word for the process of stemming. The results of the stemming will be compared with the base words available in the database. If it does not match, then the word is highlighted and is considered an error word. The first finding is the Nazief Adriani's algorithm can be able to detect words error until 100%. The second finding is the Nazief Adriani's algorithm also detect non-words error, the accuracy of detecting is 97.464%.

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“…If the algorithm is failed in doing all steps above, then the first word is assumed to be the base word. So the process is complete [10].…”

Section: ) Recordingmentioning

confidence: 99%

Indonesian Words Error Detection System using Nazief Adriani Stemmer Algorithm

Yudhana¹,

Fadlil²,

Rosidin³

2019

IJACSA

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“…Their work consisted of 5 stages from pre-processing to similarity detection. Rahmatulloh et al [14] demonstrated a difference between the Nazief-Adriani and stemmer porter in measuring plagiarism. This study indicates Nazief-Adriani performs superior to the stemmer porter.…”

Section: Introductionmentioning

confidence: 99%

An improved extrinsic monolingual plagiarism detection approach of the Bengali text

Ahnaf

Hasan

Sworna

et al. 2023

IJECE

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Plagiarism is an act of literature fraud, which is presenting others’ work or ideas without giving credit to the original work. All published and unpublished written documents are under the cover of this definition. Plagiarism, which increased significantly over the last few years, is a concerning issue for students, academicians, and professionals. Due to this, there are several plagiarism detection tools or software available to detect plagiarism in different languages. Unfortunately, negligible work has been done and no plagiarism detection software available in the Bengali language where Bengali is one of the most spoken languages in the world. In this paper, we have proposed a plagiarism detection tool for the Bengali language that mainly focuses on the educational and newspaper domain. We have collected 82 textbooks from the National Curriculum of Textbooks (NCTB), Bangladesh, scrapped all articles from 12 reputed newspapers and compiled our corpus with more than 10 million sentences. The proposed method on Bengali text corpus shows an accuracy rate of 97.31%

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“…Research by A. Rahmatulloh et al [5] discuss the comparative performance of Porter and Nazief & Adriani stemming on the Winnowing algorithm for plagiarism detection. The results of the study concluded that testing the Winnowing algorithm without stemming had the results of 70.7% plagiarism similarity with processing speed of 0.711 s, Winnowing algorithm testing with stemming Porter had the results of plagiarism similarity of 65.7% with processing speed of 0.221 s, testing of the Winnowing algorithm with Nazief & Adriani stemming had results plagiarism similarity 70.5% with processing speed 0.476 s. Stemming Porters reduce the level of plagiarism similarity results very significantly but speed up processing while Nazief & Adriani stemming results are close to the same as without using stemming and also speed up processing.…”

Section: Introductionmentioning

confidence: 99%

Selection of the Best K-Gram Value on Modified Rabin-Karp Algorithm

Hidayat¹,

Utami²,

Sunyoto³

2022

Indonesian J. Comput. Cybern. Syst.

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The Rabin-Karp algorithm is used to detect similarity using hashing techniques, from related studies modifications have been made in the hashing process but in previous studies have not been conducted research for the best k value in the K-Gram process. At the stage of stemming the Nazief & Adriani algorithm is used to transform the words into basic words. The researcher uses several variations of K-Gram values to determine the best K-Gram values. The analysis was performed using Ukara Enhanced public data obtained from the Kaggle with a total of 12215 data. The student essay answers data totaled to 258 data in the group A and 305 in the group B, every student essay answers data in each group will be compared with the answers of other fellow group member. Research results are the value of k = 3 has the best performance which has the highest some interpretations of 1-14% (Little degree of similarity) and 15-50% (Medium level of similarity) compared to values of k = 5, 7, and 9 which have the highest number of interpretation results 0%-0.99% (Document is different). However, if the students essay answers compared have 100% (Exactly the same) interpretations, the k value on K-Gram does not affect the results.

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Comparison between the Stemmer Porter Effect and Nazief-Adriani on the Performance of Winnowing Algorithms for Measuring Plagiarism

Cited by 10 publications

References 10 publications

Indonesian Words Error Detection System using Nazief Adriani Stemmer Algorithm

Indonesian Words Error Detection System using Nazief Adriani Stemmer Algorithm

An improved extrinsic monolingual plagiarism detection approach of the Bengali text

Selection of the Best K-Gram Value on Modified Rabin-Karp Algorithm

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