Implementasi Algoritma Naïve Bayes dalam Memprediksi Tingkat Kelulusan Siswa pada Sertifikasi Mikrotik Certified Network Associate (MTCNA)
DOI:
https://doi.org/10.35870/jimik.v5i3.1012Keywords:
Naïve Bayes Algorithm, Graduation Prediction, Networking Technology, ClassificationAbstract
The development of technology has increased the importance of certification in computer networking, such as the MikroTik Certified Network Associate (MTCNA). This certification is crucial for students who want to validate their skills and enhance their career prospects. However, predicting student graduation rates for the MTCNA certification presents various challenges, such as prior knowledge, study habits, and the complexity of the exam content. These factors can significantly affect the accuracy of predictions regarding student graduation rates. This study aims to address these issues by implementing the Naïve Bayes algorithm to predict student graduation rates for the MTCNA certification. The Naïve Bayes algorithm, known for its simplicity and effectiveness in classification problems, is expected to provide a more accurate predictive model. This study uses historical data on student performance and other relevant attributes. The results show that the Naïve Bayes model has an adequate prediction accuracy, with an accuracy rate of 91.70%. These findings are expected to provide valuable insights for educators and students, thereby improving preparation strategies and success rates in the MTCNA certification exam.
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