<![CDATA[This research focuses on the development of sentiment analysis software designed to evaluate students’ comments at SMKS Methodist 8 Medan concerning teaching and learning activities through a text mining approach. The software aims to support educational evaluation by automatically identifying the emotional tone and opinions expressed by students in their feedback. In this study, text mining techniques are employed to preprocess and analyze comment data collected from online learning platforms and school surveys. The preprocessing stages include tokenizing, stopword removal, and stemming to prepare clean textual data for analysis. Subsequently, the Naïve Bayes classification algorithm is implemented to categorize the comments into three sentiment classes: positive, negative, and neutral. The results of experimental testing demonstrate that the developed system can accurately identify sentiment tendencies with satisfactory precision and reliability. Moreover, the visualization of sentiment results enables educators to better understand students’ perceptions and engagement levels in the learning process. This research contributes to the field of educational technology by providing a data-driven tool that helps schools evaluate teaching effectiveness, identify areas of improvement, and enhance the overall quality of learning experiences through objective analysis of student feedback.]]>
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