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All Journal Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON)
Daniel Oktodeli Sihombing
Institut Teknologi dan Bisnis Sabda Setia, Pontianak
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Journal : Jurnal Sistem Komputer dan Informatika (JSON)

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Implementasi Natural Language Processing (NLP) dan Algoritma Cosine Similarity dalam Penilaian Ujian Esai Otomatis Daniel Oktodeli Sihombing
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5374

Abstract

Evaluation of learning is an activity that is routinely carried out in the lecture process. The essay exam is a test in the form of questions that aim so that the answers given are in the form of descriptions based on student’s understanding in accordance with what they know. The results of the various answers are a separate consideration in correcting whether the answer is in accordance with the answer key or not. This resulted in each question on the essay exam having its own weight which would later be added up cumulatively to get a total score. This study implements Natural Language Processing (NLP) and Cosine Similarity algorithms to automatically assess essay exams. Document Similarity is one of the tasks in Natural Language Processing (NLP) to check the degree of document similarity. The algorithm used to check the level of similarity is Cosine Similarity which uses two vectors to measure the degree of similarity of documents with the results ranging from 0 to 1. Processing student answer data for three essay questions gets the expected results. The results of the Cosine Similarity calculation in question no 1 show that M3 students have answers with a similarity level of 90.58%. Whereas for question no 2 M1 students had answers with a similarity level of 87.71% and finally for question no 3 M1 students had answers with a similarity level of 76.70%.
Co-Authors Alex Cahyadi