COMPARATIVE PERFORMANCE STUDY OF SEARCH ALGORITHMS ON LARGE-SCALE DATA STRUCTURES
DOI:
https://doi.org/10.33480/jitk.v11i1.6592Keywords:
amazon reviews, large-scale data structures, performance analysis, search algorithms, time complexityAbstract
— In the era of big data, searching for information in big data sets is a big challenge that requires efficient search algorithms. This study compares the performance of three classic search algorithms, namely linear search, binary search, and hash search. This study uses large-scale datasets, namely Amazon Product Reviews and Amazon Customer Reviews. Evaluations were conducted based on the complexity of time for each search method. The results of the experiment showed that linear search had the slowest performance with O(n) time complexity, making it inefficient for large data sets. Binary search performs better with O(log n) complexity, but requires pre-sorted data. Hash searches provide the most optimal results in best-case and average with O(1) complexity, but can be reduced to O(n) in the worst case when there are too many collisions in the hash function. Hash search consistently outperforms linear and binary searches in terms of execution speed. Binary search remains highly efficient for sorted data, while linear search is clearly the least efficient, especially for large-scale datasets. Linear search has high execution times and is inconsistent, while binary and hash search are more efficient and stable. The algorithm's performance did not differ significantly between datasets, suggesting the data structure did not affect performance as long as the search type was the same.
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