Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method
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 Cr...
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| Format: | Article |
| Language: | English |
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State Islamic University Sunan Kalijaga
2023-01-01
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| Series: | IJID (International Journal on Informatics for Development) |
| Subjects: | |
| Online Access: | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/3743 |
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| author | Yudha Riwanto Muhammad Taufiq Nuruzzaman Shofwatul Uyun Bambang Sugiantoro |
| author_facet | Yudha Riwanto Muhammad Taufiq Nuruzzaman Shofwatul Uyun Bambang Sugiantoro |
| author_sort | Yudha Riwanto |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b2a0251b8ed54e81af8ee30bd13c31b2 |
| institution | OA Journals |
| issn | 2252-7834 2549-7448 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | State Islamic University Sunan Kalijaga |
| record_format | Article |
| series | IJID (International Journal on Informatics for Development) |
| spelling | doaj-art-b2a0251b8ed54e81af8ee30bd13c31b22025-08-20T02:17:40ZengState Islamic University Sunan KalijagaIJID (International Journal on Informatics for Development)2252-78342549-74482023-01-0111222223110.14421/ijid.2022.37433369Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid MethodYudha Riwanto0Muhammad Taufiq Nuruzzaman1https://orcid.org/0000-0002-4348-6552Shofwatul Uyun2Bambang Sugiantoro3Dept. of Informatics UIN Sunan Kalijaga YogyakartaUIN Sunan Kalijaga YogyakartaUIN Sunan KalijagaUIN Sunan Kalijaga YogyakartaHigh 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.https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/3743multiple crossover geneticsgenetics with increments valuesearch technoquedirect approachdata search |
| spellingShingle | Yudha Riwanto Muhammad Taufiq Nuruzzaman Shofwatul Uyun Bambang Sugiantoro Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method IJID (International Journal on Informatics for Development) multiple crossover genetics genetics with increments value search technoque direct approach data search |
| title | Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method |
| title_full | Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method |
| title_fullStr | Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method |
| title_full_unstemmed | Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method |
| title_short | Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method |
| title_sort | data search process optimization using brute force and genetic algorithm hybrid method |
| topic | multiple crossover genetics genetics with increments value search technoque direct approach data search |
| url | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/3743 |
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