Aplikasi Sentiment Analysis Terhadap Pelaksanaan Pembelajaran Jarak Jauh Universitas Kristen Petra Dengan Metode Naive Bayes Classifier - Kezia Sekarayu Setyawati; Andreas Handojo; Henry Novianus Palit (Jurnal Infra, 2021)
In maintaining student satisfaction during online learning period, Petra Christian University conducted a survey to know student’s response. The result then processed manually and takes a long time to gather an information. Therefore, a sentiment analysis application is needed which will help to collect information from survey in a shorter time by classifying sentiments and topics related to online learning. The method used to classify topics and sentiments is the Naive Bayes Classifier. Data will be prepared through preprocessing, by eliminating the same sentence, changing abbreviated words, stemming, and stop word removal.The classification model produced and used in this application can classify student survey data related to the implementation of online learning based on positive and negative sentiments, also based on the topic, material, lecturers, learning media, and supporting facilities. The accuracy of the classification model is 89% for sentiment classification and 80% for topic classification.