Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement
Advancements in machine learning have enabled the development of more accurate and efficient health prediction models. This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) meth...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2025-02-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6203 |
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| author | Sri Rossa Aisyah Puteri Baharie Sugiyarto Surono Aris Thobirin |
| author_facet | Sri Rossa Aisyah Puteri Baharie Sugiyarto Surono Aris Thobirin |
| author_sort | Sri Rossa Aisyah Puteri Baharie |
| collection | DOAJ |
| description | Advancements in machine learning have enabled the development of more accurate and efficient health prediction models. This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. SVM is a robust machine learning algorithm for classification and regression. Still, its performance depends significantly on selecting appropriate hyperparameters such as regularization (C), kernel coefficient (γ), and polynomial kernel degree (d). The HGD-GWO method synergizes gradient descent for local optimization and the Gray Wolf Optimizer for global solution exploration. Using the Pima Indians Diabetes dataset, the process includes normalization, hyperparameter optimization, data division, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The optimized SVM achieved an accuracy of 81.17%, with precision, recall, and F1-score values of 75.00%, 57.45%, and 65.06%, respectively, at a data ratio of 80%:20%. These findings highlight the potential of HGD-GWO in enhancing predictive models, particularly for early diabetes detection. |
| format | Article |
| id | doaj-art-e25f5f87f81c41e6aca85e85a1baecaf |
| institution | OA Journals |
| issn | 2580-0760 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-e25f5f87f81c41e6aca85e85a1baecaf2025-08-20T02:06:16ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602025-02-019114615210.29207/resti.v9i1.62036203Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance EnhancementSri Rossa Aisyah Puteri Baharie0Sugiyarto Surono1Aris Thobirin2Universitas Ahmad Dahlan Universitas Ahmad DahlanUniversitas Ahmad DahlanAdvancements in machine learning have enabled the development of more accurate and efficient health prediction models. This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. SVM is a robust machine learning algorithm for classification and regression. Still, its performance depends significantly on selecting appropriate hyperparameters such as regularization (C), kernel coefficient (γ), and polynomial kernel degree (d). The HGD-GWO method synergizes gradient descent for local optimization and the Gray Wolf Optimizer for global solution exploration. Using the Pima Indians Diabetes dataset, the process includes normalization, hyperparameter optimization, data division, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The optimized SVM achieved an accuracy of 81.17%, with precision, recall, and F1-score values of 75.00%, 57.45%, and 65.06%, respectively, at a data ratio of 80%:20%. These findings highlight the potential of HGD-GWO in enhancing predictive models, particularly for early diabetes detection.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6203hybrid gradient descent grey wolf optimizerhyperparameter optimizationdiabetes predictionmachine learningsupport vector machine (svm) |
| spellingShingle | Sri Rossa Aisyah Puteri Baharie Sugiyarto Surono Aris Thobirin Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) hybrid gradient descent grey wolf optimizer hyperparameter optimization diabetes prediction machine learning support vector machine (svm) |
| title | Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement |
| title_full | Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement |
| title_fullStr | Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement |
| title_full_unstemmed | Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement |
| title_short | Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement |
| title_sort | hybrid gradient descent grey wolf optimizer for machine learning performance enhancement |
| topic | hybrid gradient descent grey wolf optimizer hyperparameter optimization diabetes prediction machine learning support vector machine (svm) |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6203 |
| work_keys_str_mv | AT srirossaaisyahputeribaharie hybridgradientdescentgreywolfoptimizerformachinelearningperformanceenhancement AT sugiyartosurono hybridgradientdescentgreywolfoptimizerformachinelearningperformanceenhancement AT aristhobirin hybridgradientdescentgreywolfoptimizerformachinelearningperformanceenhancement |