Analysis of student sentiment towards the quality of final project guidance using the Support Vector Machine Algorithm - Wijaya, Andi; Anan, M. Rifqi; Maulidi, Minan Fikri; Maulana, M. Farhan; Purnomo, Hadi; Arifin, Zainal (Institute of Computer Science (IOCS), 2024)
This research aims to analyze student sentiment towards thesis guidance services at Nurul Jadid University (UNUJA) using sentiment analysis methods with the Support Vector Machine algorithm. Thesis guidance services play a crucial role in shaping high-quality and competitive human resources within the university environment. However, students' sentiment assessments of these services are often complex and may differ from the perspectives of their advisors. The research approach used is quantitative analysis by collecting student feedback data through questionnaires and interviews. The text data from student responses is then processed to clean and format the data before being implemented with the Support Vector Machine algorithm. This algorithm will classify the sentiment into positive, negative, or neutral groups based on the information contained in the text responses. Based on the results of the conducted study, using the Support Vector Machine (SVM) method for sentiment analysis of thesis guidance quality at Nurul Jadid University, this study achieved an accuracy of 87%, precision of 88%, recall of 87%, and an F1 score of 86%.