Abstract
Abstract - The Faculty of Information Technology currently carries out performance evaluations at the end of each semester and involves students as sources of data evaluation. The evaluation activity took place online on the website ss.fti.uajm.ac.id. With the number of active students, the number of evaluations that need to be read and the number read by faculty stakeholders also increases. This is inversely proportional to the time that stakeholders need time to read, evaluate, and categorize comments entered by students as part of the performance evaluation. In this study, a multi-classification of student comments related to evaluations at the Faculty of Information Technology UAJM will be carried out. Text pre-processing will use the Sastrawi library which includes stopword removal, stemming, and transformation of text into TFIDF form. The results of the pre-processing text will be used as input on Naive Bayes and using three scenarios to evaluate the classifier model. The average accuracy values of the Naive Bayes algorithm for category and sentiment labels are 79% and 81%, respectively. Furthermore, the expected result of this research is to reduce the time for FTI UAJM stakeholders to read and comment/suggest faster because the evaluation results are obtained in real-time.
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