SENTIMENT ANALYSIS OF STUDENT SATISFACTION TOWARDS DISTANCE LEARNING USING MACHINE LEARNING METHOD

Abstract

The Covid-19 pandemic forces the entire society
to change their way of life. One of them is the process of face-to-
face learning changing into distant learning. Various responses
arise from students during the implementation of this new
system, both positive and negative, indicating the level of student
satisfaction. The sentiment analysis of students' comments
during distance learning was conducted using machine learning
algorithms and tools Rapid miner. Literature study shows that
the Naive Bayes, K-NN, and Decision Tree algorithms have very
high 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. Positive
93.80% and pred. negative 100.00%. The K-NN algorithm is
92.49% and class precision for pred. positive is 92.37%, pred.
negative 100%. The Decision Tree method is 90.81% with a
standard deviation of (+-) 0.58 and class precision for pred.
positive 90.81% and class pred. negative 0.00%.