SENTIMENT ANALYSIS OF STUDENT SATISFACTION TOWARDS DISTANCE LEARNING USING MACHINE LEARNING METHOD - Andres, M; WanSen, Tjong; Roestam, Rusdianto (IT for Society, 2024)
The Covid-19 pandemic forces the entire societyto change their way of life. One of them is the process of face-to-face learning changing into distant learning. Various responsesarise from students during the implementation of this newsystem, both positive and negative, indicating the level of studentsatisfaction. The sentiment analysis of students' commentsduring distance learning was conducted using machine learningalgorithms and tools Rapid miner. Literature study shows thatthe Naive Bayes, K-NN, and Decision Tree algorithms have veryhigh accuracy, so this research uses those methods to get high-accuracy results. The research shows the following results;Naive Bayes is 93.80% and class precision for pred. Positive93.80% and pred. negative 100.00%. The K-NN algorithm is92.49% and class precision for pred. positive is 92.37%, pred.negative 100%. The Decision Tree method is 90.81% with astandard deviation of (+-) 0.58 and class precision for pred.positive 90.81% and class pred. negative 0.00%.