COMPARISON OF SVM AND NAÏVE BAYES CLASSIFIER ALGORITHMS ON STUDENT INTEREST IN JOINING MSIB
Abstract
Machine learning (ML) is a branch of artificial intelligence (AI) that deals with the development of systems capable of learning from data to make predictions or decisions without being explicitly programmed. In this study, we conducted an analysis of students' interest in the Internship and Certified Independent Study Program (MSIB) in the context of the Independent Campus Learning policy. The method used is a survey by distributing questionnaires to students of Amikom Purwokerto University in the MSIB batch 5 in year 2023. The results of this study can provide understanding and predictions about students' interest in the MSIB program based on relevant variables, such as study program, semester, cumulative grade point average (GPA), semester credit system (SKS), and previous work experience. The research results indicate that GPA and Study Program greatly influence students' interest in MSIB. The Naïve Bayes algorithm yielded an accuracy of 0.6875 on the training data and 0.25 on the testing data, with a confusion matrix of (0, 1, 0; 0, 1, 2; 0, 0, 0). Meanwhile, the Support Vector Machine (SVM) algorithm yielded an accuracy of 0.4375 on the training data and 0.75 on the testing data, with a confusion matrix of (0, 1; 0, 3). The machine learning model developed in this study is expected to help predict students interest based on new data provided, thus supporting decision-making in optimizing the MSIB program.
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