Implementation of a Favorite Course Search System Based on Students’ Average Grades Using the A* Algorithm

Abstract

Optimal selection of elective courses plays an important role in supporting students’ academic success and ensuring alignment between learning interests and final project preparation. This study aims to develop a favorite course search system based on the A-Star (A*) algorithm by utilizing students’ average grades as the main evaluation variable. The system was implemented using the Java NetBeans platform, supported by datasets consisting of course names, credit weights (SKS), and grade distributions. The A* algorithm was adapted through the integration of heuristic components, including Standard Deviation and Relative Credit Load, to improve accuracy in identifying optimal course recommendations. Experimental results demonstrate that the system is capable of generating recommendations with an accuracy rate of 95%, verified through comparison between system outputs and manual calculations. The results also show that the Mitigation course ranked highest with a score of 6.1, indicating strong student performance in that subject. Overall, the system provides a practical and efficient solution for academic decision-making, enabling students to select elective courses more strategically based on data-driven insights. This study contributes to the development of computational methods in educational recommendation systems and opens opportunities for further enhancement through integration with real academic databases.