Classification of Scholarships for Students in Schools Using the Naïve Bayes Method - Rizki Siregar, Awal; Furqan, Mhd. (Journal of Computer Networks, Architecture and High Performance Computing, 2025)
This research addresses the challenge faced by educational institutions in selecting scholarship recipients by implementing the Naïve Bayes algorithm. The objective of this study is to simplify and improve the accuracy of the scholarship selection process at MTs As-Syarif Kuala Beringin, using data from 50 students. The background highlights the importance of scholarships in providing equal educational opportunities, particularly for students with financial challenges. The research method involves the use of Naïve Bayes to calculate the probability of eligibility based on academic performance, economic background, and student activity. The results show that seven students met the scholarship criteria, demonstrating the efficiency and objectivity of the algorithm. The practical implications include the development of a user-friendly application that facilitates data input, scholarship criteria determination, and clear evaluation results. This system enhances transparency and reliability in decision-making. In conclusion, the Naïve Bayes algorithm proves to be an effective and efficient tool for scholarship selection, enabling a more equitable opportunity for students. Further research could focus on integrating additional data points or comparing the algorithm's performance with other classification methods to enhance system reliability.