IMPLEMENTASI DATA MINING UNTUK PREDIKSI KELULUSAN MENGGUNAKAN METODE KLASIFIKASI NAIVE BAYES - Muhammad Sidik (JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI, 2020)
Current Use of technology is very rapid, especially in the field of education. The process of using technology so fast can make it easier to process the data. Students 'grades are very complex in the form of students' 'leger' grade data and academic data. Stacking of student data repeatedly has an impact on the search for that data information. By using data mining process This research aims to classify the data of students of Hisba Buana Senior High School vocational class of 2017. The mining process is done through: business understanding, data understanding, data processing, modeling, evaluation and development. Using the Naive Bayes Algorithm is expected to calculate the probability of predicting passing, variable data that are not interrelated to whether or not other features are in the same data. Implement Rapit Miner to help manage accurate and accurate predictions. This study uses the attributes of subjects tested on the graduation exam from the beginning to the last semester. The result of this research with the accuracy level of 83.06% resulted in graduation group with value 0,506 and group did not pass with value 0,494. Decisionmaking is obtained from the Data and used as a basis for determining school policy.