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Journal : IJID (International Journal on Informatics for Development)

Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method Riwanto, Yudha; Nuruzzaman, Muhammad Taufiq; Uyun, Shofwatul; Sugiantoro, Bambang
IJID (International Journal on Informatics for Development) Vol. 11 No. 2 (2022): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2022.3743

Abstract

High accuracy and speed in data search, which are aims at finding the best solution to a problem, are essential. This study examines the brute force method, genetic algorithm, and two proposed algorithms which are the development of the brute force algorithm and genetic algorithm, namely Multiple Crossover Genetic, and Genetics with increments values. Brute force is a method with a direct approach to solving a problem based on the formulation of the problem and the definition of the concepts involved. A genetic algorithm is a search algorithm that uses genetic evolution that occurs in living things as its basis. This research selected the case of determining the pin series by looking for a match between the target and the search result. To test the suitability of the method, 100-time tests were conducted for each algorithm. The results of this study indicated that brute force has the highest average generation rate of 737146.3469 and an average time of 1960.4296, and the latter algorithm gets the best score with an average generation rate of 36.78 and an average time of 0.0642.