Klasifikasi Tingkat Prestasi Mahasiswa Pada Mata Kuliah Penambangan Data Menggunakan Naïve Bayes

This study aims to classify the academic achievement levels of students in Data Mining courses using the Naïve Bayes algorithm and to evaluate the performance of the resulting classification model. This study uses a quantitative approach with academic data from 190 students, including assignment scores, mid-term exam scores, and final exam scores. The classification process was carried out by applying the Naïve Bayes algorithm, while model evaluation was performed using accuracy metrics, classification reports, and confusion matrices. The test results showed that the Naïve Bayes model produced an accuracy rate of 73.68%. Based on the classification report, classes B and B+ showed the best performance with recall values of 1.00 and f1-scores of 0.87 and 0.95, respectively. Confusion matrix analysis showed that most of the data in classes B and B+ were classified correctly. The results of this study indicate that the Naïve Bayes algorithm is quite effective in classifying students' academic achievement levels and has the potential to be used as an academic evaluation tool in learning decision-making.