PENERAPAN ALGORITMA NAÏVE BAYES UNTUK KLASIFIKASI SENTIMEN KEPUASAN MAHASISWA TERHADAP LAYANAN SISTEM DAN TEKNOLOGI INFORMASI UMSU

Abstract

The development of information systems and technology in higher education requires service evaluation that is fast, objective, and measurable. At UMSU, these services are managed by BSTI and are intensively used by students, making student feedback an important source for service quality improvement. However, open- ended questionnaire responses are unstructured, so manual analysis tends to be time-consuming and may lead to inconsistent interpretations. This study aims to apply the Multinomial Naïve Bayes algorithm to classify student satisfaction sentiment toward information systems and technology services at UMSU into three categories: positive, neutral, and Negative. The data were collected from students’ written responses through Google Forms, with a minimum sample of 377 respondents selected using stratified proportionate sampling. The research stages include manual Labeling, text preprocessing, TF-IDF feature extraction, and sentiment classification. The model is evaluated using an 80:20 stratified train-test split with accuracy, precision, recall, F1-score, and Confusion Matrix as evaluation metrics. The output of this study is a simple system capable of managing feedback data, classifying sentiment, and presenting the results in a concise Dashboard as a basis for evaluating BSTI UMSU services.