Analisis Sentimen Mahasiswa Terhadap Perkuliahan Dalam Jaringan Menggunakan Metode Naïve Bayes Classifier - Bagus Aziz Rahmatullah; Imam Sujarwo; Erna Herawati (Jurnal Riset Mahasiswa Matematika, 2023)
Since the pandemic of Covid-19 was happened in Indonesia, the government shared the letter of The Ministry of Education and Higher Culture Education Directorate No.1, year 2020 about prevention of the spread of Corona Virus Disease (Covid-19) in higher education. Through the letter, The Ministry of Education and Culture gave an instruction for college to organize online learning and suggested students to study at their home. Online learning which was considered as a strategy then became a controversy because it needed adaption. This sudden change from normal learning to online learning caused many responses from students. The aim of this research was to analyze student sentiment or responses on online learning in this pandemic era of Covid-19 in Indonesia by using data which had been collected using questionnaire and processed using naïve bayes classifier method. This research was case study descriptive quantitative research. The research was done by collecting the data first. The data was collected through questionnaire with the question about their opinion on online learning in this pandemic Covid-19 era. The data was 157 student’s data opinion on online learning. After the data was collected, the data was cleaned first from question mark and the words which didn’t give an effect in sentiment analysis. After the data was cleaned, then the result of the classification will be showed as well as the accuracy which the model earned. The result showed that online learning had negative sentiment more than positive sentiment. The height of negative sentiment was caused by discomfort of student in online learning. The word which frequently showed was ‘tidak efektif’, ‘susah’, and ‘tugas’. The accuracy of this model was 75% when the result of this accuracy was good result in classification.