Perbandingan Metode Naive Bayes Classifier Dan Decision Tree C4.5 Dalam Mencari Pola Minat Pemilihan Jurusan Di Madrasah Aliyah (Studi Kasus:MA El-Bayan Majenang) - Dede Yusuf; Zulfikar Yusya Mubarak; Annisa Rahayu Pangesti; Nuni Wulansari; Rizki Zulqornain (Himpunan Penggiat Teknologi Informasi Abrar Indonesia, 2023)
Selection of student majors is one of the decision-making rules in determining majors based on interests and talents, this aims to understand one's potential and existing opportunities. In determining student majors in the 2013 curriculum, it is carried out at the beginning of the first semester, namely in class 10 (X) of senior high school. The implementation of the 2013 curriculum aims to support the adaptation of educational programs by capturing the characteristics and potential of students. [1]. The implementation of the 2013 curriculum had an impact on one of the schools, especially the counseling teacher, who did not know the talents, interests, character and financial capabilities of the students' families to choose certain subjects, so the counseling teacher had to really be able to recommend majors that matched the interests and talents of students. Based on these problems, the application of data mining using the Naïve Bayes algorithm and the Decesion Tree C4.5 algorithm is carried out for classifying student majors at MA El-Bayan Majenang. The Naïve Bayes algorithm [2] and the Decesion Tree algorithm C4.5 [2] are algorithms with a decision tree classification pattern that are used because they have several advantages compared to other algorithms. In this study, the authors compared the two methods, starting with data collection, then cleaning the data and continuing to process data testing and training data. After all the data has been processed, it will proceed to the classification process for each algorithm.