Product Detail Search Effectiveness Analysis Based on Name Using Linear, Binary, and Hash Algorithms

Abstract

Fast and accurate product data search is a major requirement in the development of e-commerce applications and inventory systems. The effectiveness of the search is greatly influenced by the algorithm used, especially in terms of speed, accuracy, and memory usage efficiency. This research aims to analyze and compare the effectiveness of three search algorithms, namely linear search, binary search, and hashing, in finding product details by name. The research method used is a computational experiment with the implementation of the three algorithms on a dataset containing at least 500 products using the Python programming language. Each algorithm is tested based on search time, accuracy rate, and memory usage. The results showed that the hashing algorithm gave the best performance in terms of search speed and memory efficiency, while binary search also performed well on sorted data. Linear search, although simple, tends to be less efficient for large amounts of data. These findings can be used as a reference in selecting the optimal search algorithm according to the needs and scale of the system being developed.