Klasifikasi dan Rekomendasi Jurusan Kuliah Bagi Pelajar SMA Menggunakan Algoritme Naive Bayes-WP - Restu Fitriawanti; Imam Cholissodin; Ratih Kartika Dewi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Each year high school students will be faced with a final choice to determine what direction will be selected for future education. Each choice will determine the future of the voter, and this is something that is difficult enough to be determined by most high school students, because they do not have information and images related to education in college. In addition, the child is still not aware of the interests and abilities on him. Based on the problems above the selection of majors as early as possible should start to be considered because choosing faculty and majors with precisely very difficult, if one chose the department will result in learner in the learning process in lectures, because less comfortable with the materials in the lecture and probably a lot of less-liked material. This will affect the child's achievement index (IP) that can be below the standard and worse the discharge of the student (DODrop Out) because it is declared not able to follow the education that followed. So the classification and recommendation of college majors for high school students who based on academic grades wrote can help high school students to determine the proper choice. The calculation of the study is calculated separately for the Naive Bayes algorithm used to classify student learner data into the faculty class and Weighted Product (WP) is used to help determine the exact majors based on the majors in the faculty predetermined by the Naive Bayes algorithm. By using the Naive Bayes-WP algorithm, the system's average accuracy reaches 82%.