ANALISIS SENTIMEN LAYANAN AKADEMIK MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER PADA KOMENTAR MAHASISWA - Jemmy Edwin Bororing; Feri Faeruzah (Universitas Janabadra, 2020)
Sentiment analysis is the process of classifying comments and in this study is divided into three parts, namely positive, negative and neutral classes. Classification is very important to know student satisfaction with lecturer performance. Comment data can be obtained from the Janabadra University Quality Assurance Agency website.The Naïve Bayes Classifier method is a method based on bayesin's probability and theorem. This method is used to classify the results of student comment data written on the student satisfaction form towards the lecturer so as to produce the desired automatic classification.The results of this study are used to determine the classification of student comment data so that the performance of lecturers can be easily identified from the comments given by students. The Analysis Sentiment results contained emotion category consisting of Unknown 1935, Joy 31 and Sadness 3. While the Polarity Analysis results from the comments of Janabadra University students consisted of negative values 23 and positive 1999. Keywords: Student Comments, Naive Bayes Classifier Method, Sentiment Analysis.