<![CDATA[Lecturer performance in the learning process directly affects student competence. Evaluating lecturers is essential to ensure optimal teaching and produce graduates ready for the job market. One way to assess teaching effectiveness is through sentiment analysis of student opinions. However, due to the large amount of data still processed manually, a more efficient approach is needed, namely AI-based sentiment analysis. This study implements the Naïve Bayes method to classify student sentiments as positive or negative and evaluate lecturer performance based on classification results. The process includes preprocessing and labeling. The Naïve Bayes algorithm is then applied for sentiment classification and evaluated using a confusion matrix. The results show that Naïve Bayes is highly effective, achieving 94% accuracy, 94% precision, 96% recall, and a 95% F1-score. Of the total data, 231 comments were positive, while 174 were negative. These findings confirm that sentiment analysis can be an efficient tool for assessing lecturers and improving teaching quality at universities.]]>
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