Optimization of Naïve Bayes Algorithm Parameters for Student Graduation Prediction at Universitas Dirgantara Marsekal Suryadarma - Muryan Awaludin; Verdi Yasin; Mega Wahyuningsih (Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta, 2022)
The Information Systems Study Program at Unsurya is a new department and only a few graduate students. Based on data obtained from graduates of the 2018/2019 academic year, 41 students graduated, including 26 students who experienced delays in taking their studies. A system that can predict student graduation is needed so that the Information Systems department can produce more student graduations than before. By optimizing the parameters of the Naïve Bayes algorithm, it can be applied in predicting graduation by utilizing previous student graduation data, the attributes used are gender, age, sks, gpa, and student status. The results of research testing using Rapid Miner 9.8 with 41 training data and 25 testing data, yielding 96% accuracy, 90.91% recall, and 100% precision.