Application of Bubble Sort Optimization in New Student Admission Selection Using Brute Force Algorithm

Abstract

This study investigates the application of a Brute Force algorithm optimized with Bubble Sort for new student admission selection. The Brute Force algorithm, while guaranteeing accurate results, suffers from exponential time complexity with increasing data size, posing a challenge for large applicant pools. To address this limitation, this research integrates Bubble Sort optimization to reduce the execution time complexity of the Brute Force algorithm. This study goes beyond solving the student admission selection problem; it explores optimizing the Brute Force algorithm by leveraging the simplicity and efficiency of Bubble Sort. This approach aims to determine the extent to which the Brute Force algorithm can be optimized for student selection, particularly regarding execution time complexity. The integration of Bubble Sort is hypothesized to significantly improve the performance of the Brute Force algorithm by reordering data before processing, thereby minimizing unnecessary comparisons. This paper presents a comparative analysis of execution times between the traditional Brute Force approach and the optimized version. Preliminary results indicate a substantial improvement in efficiency, suggesting that this hybrid approach could be a valuable solution for similar combinatorial problems with time complexity constraints. Further research could explore the applicability of this optimized algorithm in other domains where time complexity is a critical factor.