Predicting Prospective Student Interests Using the C4.5 Algorithm and Naive Bayes

Students are individuals pursuing higher education at a university with the goal of enhancing their knowledge, skills, and character to succeed in the professional world and contribute to society. The purpose of this study is to analyze the factors that influence prospective students' interest in continuing their education using the C4.5 Algorithm and the Naïve Bayes Method. The importance of understanding prospective students' interest patterns is expected to help universities formulate more effective strategies. The purpose of this study is to determine how well the two methods classify data and understand the factors that most influence prospective students' decisions. The C4.5 Algorithm is known to be effective in building decision trees that are easy to interpret, while the Naïve Bayes Method has the advantage of handling datasets with independent attributes. This study uses the stages of data selection, data pre-processing, algorithm application, and model evaluation. The classification results obtained from the C4.5 Algorithm show that 132 data are included in the interest category and 8 data are not interested, while the Naïve Bayes Method produces 131 data of interest and 9 data are not interested. In conclusion, both methods have good accuracy levels, but the Naïve Bayes Method shows superiority in Recall value, while the C4.5 Algorithm excels in interpretation of results and clarity of classification patterns.