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All Journal Jurnal Software Engineering and Computational Intelligence Jurnal Sistem Informasi Komputer dan Teknologi Informasi
Prakarsya, Agustian
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Computer Science & IT

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Journal : Jurnal Software Engineering and Computational Intelligence

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Utilization of the Whale Optimization Algorithm in Finite State Automata Design for Advanced Pattern Recognition Systems Septian, Firza; Prakarsya, Agustian
Jurnal Software Engineering and Computational Intelligence Vol 2 No 02 (2024)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v2i02.4844

Abstract

This research explores the application of the Whale Optimization Algorithm (WOA) in designing Finite State Automata (FSA) for advanced pattern recognition systems. Pattern recognition plays a crucial role in various fields, requiring high accuracy and efficiency. Traditional approaches to FSA design often face limitations in adaptability and optimization. By integrating WOA, a nature-inspired metaheuristic algorithm, this study aims to optimize FSA structures to improve recognition capabilities. The research process involves implementing WOA within the FSA design framework, testing it on multiple artificial pattern recognition tasks to assess effectiveness, and comparing results with other optimization methods. The findings reveal that after 10 iterations, the WOA achieved a best score of 14.01% error, indicating initial progress but room for further improvement. At 50 iterations, the performance plateaued, maintaining a score of 9.43% error, suggesting a need for additional exploration of the parameter space. However, by 100 iterations, the WOA produced a significantly improved score of 0.0022% error, demonstrating a highly optimized solution as the parameters converged closely to their target values. After 100 iterations, the error value did not decrease any further, indicating that the effective iteration count for optimization is 100 iterations. These results highlight the effectiveness of WOA in enhancing FSA performance, showcasing its potential as a robust solution for complex pattern recognition needs. This study contributes to the development of intelligent recognition systems, advancing the state of the art in pattern recognition technology.
Co-Authors Effendi, Muhammad Junius Firza Septian M.Pd, Firdaus , PRAMBAYUN, ARIF Selvy Megira, Selvy Yusi Nurmalasari