Major Recommendation System for New Students at SMK Muhammadiyah 1 Lamongan with Naive Bayes Algorithm - Muzaqi, Wildan Irsyad; Rohman, M. Ghofar; Reknadi, Danang Bagus (Jurnal Sisfokom (Sistem Informasi dan Komputer), 2025)
Students' majors in Vocational High Schools (SMK) are very important in determining the direction of their education and career, but the process carried out so far is often subjective and does not consider academic grades and interests objectively. To overcome this, this study develops a website-based major recommendation system at SMK Muhammadiyah 1 Lamongan using the Naive Bayes algorithm that is able to provide accurate major recommendations based on student data. This system is designed using a structured Waterfall Model software development method, starting from needs analysis, design, implementation, to testing. The Naive Bayes algorithm was chosen because of its simplicity and ability to work with relatively small datasets, such as new student data at the school. Of the total 675 student data collected, 60% or 405 data were used as training data to train the Naive Bayes algorithm, while the remaining 40% or 270 data were used as test data to measure the accuracy level of the recommendation system. The test results show that the system achieves an average accuracy of 90.91%, with precision above 0.73 for each major, recall above 0.80 except for the Office Management major which reaches 0.75, and an average F1 score of 81.72%. These findings indicate that the website-based major recommendation system with the Naive Bayes algorithm is effective and can help students determine majors that suit their potential and interests objectively and accurately, thus supporting a more precise and targeted major selection process.