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All Journal Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Judanti Cahyaning
Universitas Pembangunan Nasional Veteran Yogyakarta, Yogyakarta, Indonesia
Computer Science & IT

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Essay auto-scoring using N-Gram and Jaro Winkler based Indonesian Typos Herlina Jayadianti; Budi Santosa; Judanti Cahyaning; Shoffan Saifullah; Rafal Drezewski
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 2 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2473

Abstract

Writing errors on e-essay exams reduce scores. Thus, detecting and correcting errors automatically in writing answers is necessary. The implementation of Levenshtein Distance and N-Gram can detect writing errors. However, this process needed a long time because of the distance method used. Therefore, this research aims to hybrid Jaro Winker and N-Gram methods to detect and correct writing errors automatically. This process required preprocessing and finding the best word recommendations by the Jaro Winkler method, which refers to Kamus Besar Bahasa Indonesia (KBBI). The N-Gram method refers to the corpus. The final scoring used the Vector Space Model (VSM) method based on the similarity of words between the answer keys and the respondent’s answers. Datasets used 115 answers from 23 respondents with some writing errors. The results of Jaro Winkler and N-Gram methods are good in detecting and correcting Indonesian words with the accuracy of detection averages of 83.64% (minimum of 57.14% and maximum of 100.00%). In contrast, the error correction accuracy averages 78.44% (minimum of 40.00% and maximum of 100.00%). However, Natural Language Processing (NLP) needs to improve these results for word recommendations.
Co-Authors Jayadianti, Herlina Rafal Drezewski Saifullah, Shoffan Santosa, Budi