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David, Jonathan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Sentiment Analysis of University X Students: Comparing Naive Bayes and BERT Approaches David, Jonathan; Saputra, Kie Van Ivanky; Panjaitan, Andry Manodotua; Samosir, Feliks Victor Parningotan
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.4034

Abstract

Student satisfaction with university facilities and services requires in-depth analysis to ensure improvements in unsatisfactory facilities or services while maintaining those that meet expectations. This study aims to analyze sentiment in student satisfaction surveys using Natural Language Processing (NLP) methods. Survey data collected from 2022 to 2024 were analyzed using two main approaches: Naive Bayes (NB) with n-grams (n=1,2,3) employing feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW), and Bidirectional Encoder Representations from Transformers (BERT). The analysis results indicate that BERT outperforms NB in terms of sentiment prediction accuracy, although the difference is not highly significant. This study also identified keywords for both positive and negative sentiments. These keywords were then analyzed across 11 categories of facilities and services to provide focused insights into aspects that need to be maintained or improved. This study concludes that sentiment analysis provides significant contributions to universities in evaluating and enhancing the quality of facilities and services according to student preferences.
Co-Authors Feliks Victor Parningotan Samosir Kie Van Ivanky Saputra Panjaitan, Andry M.