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All Journal Journal Software, Hardware and Information Technology MDP Student Conference Jurnal Software Engineering and Computational Intelligence Journal of Intelligent Systems and Information Technology Jurnal Sistem Informasi Komputer dan Teknologi Informasi
Firza Septian
Universitas Serelo Lahat
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Journal : Jurnal Software Engineering and Computational Intelligence

 1 
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.
Identification of Determinants of Inclusive Economic Growth Using the Metaheuristic Whale Optimization Algorithm Approach Septian, Firza; Putriani, Nina Dwi
Jurnal Software Engineering and Computational Intelligence Vol 3 No 01 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i01.5396

Abstract

Inclusive economic growth demands the identification of key factors that drive equitable improvements in regional welfare. However, the complex interrelationships among social, economic, and demographic variables make traditional approaches insufficient for handling high-dimensional data. This study introduces an innovative approach by combining the Whale Optimization Algorithm (WOA) for feature selection with a Random Forest Regressor model to predict Gross Regional Domestic Product (GRDP) per capita as the main indicator of regional prosperity. The dataset consists of 210 regional observations and 18 independent variables. Feature selection using WOA was guided by minimizing the mean squared error (MSE), resulting in the identification of the 8 most relevant features. The retrained Random Forest model on the selected features achieved a high prediction performance, with an R² value of 0.9938 and a low RMSE. Furthermore, GRDP values were categorized into three regional welfare classes (Low, Medium, High), and the classification yielded 97.92% accuracy with high precision, recall, and F1-scores across all classes. These findings demonstrate that combining metaheuristic optimization and machine learning enables efficient and accurate identification of the key determinants of inclusive economic growth. The results provide valuable insights for formulating more targeted regional development policies.
Build Up Aplikasi Verifikasi Kemurnian Balok Karet dengan Whale Optimization Algorithm Septian, Firza; Sulkhan Nurfatih, Muhammad
Jurnal Software Engineering and Computational Intelligence Vol 2 No 01 (2024)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v2i01.4146

Abstract

The rubber industry requires precise quality control of rubber blocks to maintain product consistency and customer satisfaction. This study develops an application to verify the purity of rubber blocks using the Whale Optimization Algorithm (WOA). The application aims to provide an accurate, efficient, and automated solution for detecting impurities. Inspired by the bubble-net hunting strategy of humpback whales, WOA is effective in solving complex optimization problems. In this research, WOA optimizes parameters for impurity detection, enhancing verification accuracy. The application integrates image processing techniques and machine learning algorithms. Images of rubber blocks are captured and processed to extract relevant features, which are then analyzed using WOA to identify impurities. Extensive testing demonstrated that the application achieves high accuracy in impurity detection, outperforming traditional methods. The use of WOA significantly reduces processing time, making the application suitable for real-time industrial verification. This study highlights the potential of the Whale Optimization Algorithm to improve quality control processes in the rubber industry. The developed application offers a reliable and efficient tool for ensuring rubber block purity, thereby enhancing product quality and operational efficiency.
Penerapan Whale Optimization Algorithm dalam Pengoptimalan Portofolio Investasi Menggunakan Model Prediktif Artificial Intelligence Mediansyah, Iski; Septian, Firza; Zikry, Arief
Jurnal Software Engineering and Computational Intelligence Vol 2 No 01 (2024)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v2i01.4147

Abstract

The optimization of investment portfolios has become a primary focus in the management of dynamic financial markets. The Whale Optimization Algorithm (WOA) and Artificial Intelligence (AI) have emerged as potential solutions to tackle market complexities. WOA offers an efficient approach to finding optimal solutions, while AI models such as Artificial Neural Networks (ANN) and Machine Learning (ML) algorithms are effective in predicting market behaviors. The integration of WOA and AI holds promise for better outcomes in optimizing investment portfolios by considering complex factors and market volatility. However, the development of this technology requires interdisciplinary collaboration, increased financial and technological literacy, and consideration of social and environmental aspects. With a sustainable, inclusive, and responsible approach, we can create a more sustainable financial future that positively impacts society and the environment.
Co-Authors Dwi Putriani, Nina Ema Utami Iski Mediansyah Kusrini , Kusrini Mubarok, M. Husni Muhammad Sulkhan Nurfatih Prakarsya, Agus Prakarsya, Agustian Putriani, Nina Dwi TONNY HIDAYAT Yusi Nurmalasari