<![CDATA[The current digital era demands a more innovative approach in predicting student campuses considering that campuses are not only important for students but also for lecturers, student guardians and higher education institutions. Previous studies have used various machine learning methods such as Decision Trees, Neural Networks, Support Vector Machines, etc. in these predictions. The problem that occurs is that even though various machine learning methods have been used, there are still limitations in the accuracy and efficiency of predicting student admissions, The problem in question can be given a real example of a case that occurred. So with this problem the aim is to develop a more effective methodology in predicting student permits, with recommendations from an intelligent combination of two computational techniques Naive Bayes (NB) and Particle Swarm Optimization (PSO). This research methodology includes data collection, NB model development and model partnership with PSO. Student graduation data is used in model testing with evaluation based on metrics such as accuracy and Area Under the Curve (AUC). The results showed a significant increase in accuracy to 86.94% from 83.30% and AUC value from 0.860 to 0.884 when using the combination of NB and PSO compared to NB without either. The integration of NB and PSO has been proven to increase effectiveness in classifying student graduation prediction cases. This research opens up opportunities for the practical application of technology in the education sector and emphasizes the importance of using effective optimization and feature selection techniques in improving prediction results.]]>
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