Recommendation System for College Major Selection Based on Academic Analysis and Student Interests at SMAN 5 Berau Using Naïve Bayes

Abstract

Choosing a college major is one of the most critical decisions faced by high school students before graduation, as it significantly influences their academic path and future career. However, many students experience difficulties in selecting a major that aligns with their academic abilities and personal interests, which often leads to mismatches during their university studies. This research aims to design and implement a recommendation system for college majors tailored to students of SMAN 5 Berau by applying the Naïve Bayes algorithm. The dataset used in this study consists of students’ academic records and interest survey results, which are processed to generate appropriate recommendations. The Naïve Bayes method is chosen due to its simplicity, efficiency, and effectiveness in handling probabilistic classification problems. The system is developed to provide objective and data-driven recommendations by integrating both academic performance and student interests. Based on the analysis, the system is able to produce relevant recommendations that assist students in making more informed and accurate decisions regarding their future majors. Furthermore, this system also supports guidance counselors in providing appropriate academic advice. Therefore, the implementation of this recommendation system is expected to improve decision-making quality and reduce the risk of mismatched major selection among students.