Improved Naive Bayes Algorithm with Particle Swarm Optimization to Predict Student Graduation - Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita (Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa, 2024)
Timely graduation is very important for educational institutions such as universities, especially for students. Because it can prove that the University and students are able to undergo the learning process theoretically and practically. But many students do not pay attention to graduation, especially those who are already working or married. Therefore, analysis is needed to predict student graduation so that solutions can be found by the University. Data mining was chosen as a method to process data to get new information. The algorithm used in data mining is Naïve Bayes. The research stages include loading data into excel, cleaning empty data, selecting databases related to graduation and taking data from 300 students majoring in Informatics Engineering. The next stage is data transformation by categorizing student data, namely personal data attributes (gender, age, marital status, job status) and academic data (grade). Data testing, application of Naïve Bayes algorithm and accuracy testing were carried out with Rapis Miner software version 10.3.001. The results of data processing with Rapid Miner using the Naïve Bayes algorithm are shown with the Confusion Matrix and ROC Curve. The results of confusion matrix from data processing with Naïve Bayes in the form of accuracy, precision, and recall have the same result of 100%. The percentage of the Confusion Matrix indicates that the model created can classify correctly and accurately. The ROC curve depicted with AUC yields a value of 1, which means that the test showed excellent results