Analysis of Student Satisfaction Sentiment Towards Lecturer Performance using the Naive Bayes Classifier Method (Case Study: STMIK KAPUTAMA) - Azizhil Hakim, (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2025)
Student satisfaction with lecturer performance is an essential indicator in assessing the quality and competitiveness of higher education institutions. This study aims to analyze student sentiment regarding lecturer performance using the Naïve Bayes Classifier method. The research data were collected from student satisfaction surveys conducted during the 2022–2025 academic years, consisting of open-ended comments about lecturer performance. The research follows the CRISP-DM methodology, including text preprocessing (case folding, cleaning, tokenizing, stopword removal, normalization, and stemming), word weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) method, and sentiment classification into positive, negative, and neutral categories using the Naïve Bayes Classifier algorithm. The implementation was carried out using the Python programming language through Google Colab. The results show that the Naïve Bayes Classifier model achieved an accuracy of 76.9% in classifying student opinions, providing a reliable representation of students’ perceptions. These findings are expected to serve as a basis for evaluation and strategic decision-making to improve teaching quality and lecturer performance in higher education institutions.