Performance Analysis of Sorting Algorithm in Student Data Processing

Abstract

The arrangement of large amounts of student data typically presents a challenge for administrators in completing student administrative tasks. Several sorting algorithms are used to solve this problem; however, with the evolution of technology, various new sorting algorithms have emerged. This study examines the performance of four widely used sorting algorithms—Bubble Sort, Insertion Sort, Merge Sort, and QuickSort—in sorting data based on student ID numbers, names, and GPAs. The study involves testing the algorithms across several scenarios, including pre-sorted data, random data, and data with duplicate elements, by measuring execution time and the number of computational operations as indicators of efficiency. The results show that Bubble Sort and Insertion Sort perform well on small datasets but are less effective on large datasets. Conversely, Merge Sort and Quick Sort are highly effective on complex datasets. While Merge Sort is more stable for large datasets with specific structures, Quick Sort operates faster on random data. These results provide practical guidance for selecting the best sorting algorithm based on data size and complexity. These results can support data management efficiency in academic environments. With the results obtained, this research can serve as an important reference for developing more efficient academic information systems and data management in educational institutions.