ANALISIS SENTIMEN PELAYANAN AKADEMIK MENGGUNAKAN NAÏVE BAYES DAN SUPPORT VECTOR MACHINE
DOI:
https://doi.org/10.35969/z3jvb913Keywords:
Sentiment Analysis, Naïve Bayes, Support Vector Machine, Text Mining, Academic ServicesAbstract
Student satisfaction is one of the primary indicators used to evaluate the quality of academic services in higher education institutions. The rapid growth of social media and online communication platforms enables students to express opinions regarding academic services in large quantities. These opinions can be analyzed using sentiment analysis techniques to identify perceptions and satisfaction levels toward academic services. This study aims to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying student sentiment toward academic services. The research employed a text mining approach using 1,200 student reviews collected from online questionnaires and social media platforms. The research stages included data collection, preprocessing, feature extraction using TF-IDF, model training, and evaluation. The results indicate that Support Vector Machine achieved an accuracy of 92.4%, while Naïve Bayes obtained an accuracy of 87.6%. Furthermore, SVM demonstrated better precision, recall, and F1-score values. The findings indicate that SVM is more effective in classifying student sentiment regarding academic services. The proposed approach can assist universities in monitoring student satisfaction and improving service quality through data-driven decision making.
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