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Classification Of Hypertension Using Methods Support Vector Machine Genetic Algorithm (SVM-GA)
Universitas Negeri Makassar, Indonesia
Universitas Negeri Makassar, Indonesia
Universitas Negeri Makassar, Indonesia
* Author to whom correspondence should be addressed.
ARRUS Journal of Mathematics and Applied Science 2025, 5(1), 11-16; https://doi.org/10.35877/mathscience3976
Submission received: 2025-06-04 Published: 2025-06-30
(This article belongs to the Section Articles)
Abstract
Support Vector Machine (SVM) is a machine learning method for classifying data that has been successfully used to solve problems in various fields. The risk minimization principle used can produce an SVM model with good generalization capabilities. The problem with the SVM method is the difficulty in determining the optimal SVM hyperparameters. This research uses Genetic Algorithm (GA) to optimize SVM hyperparameters. GA optimization on SVM is used to classify hypertension. From the result of classification analysis using GA, it shows good accuracy value performance, namely 100% compared to using only SVM.
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Fahmuddin S, M. ., Rais, Z., & Yuniar, E. C. (2025). Classification Of Hypertension Using Methods Support Vector Machine Genetic Algorithm (SVM-GA). ARRUS Journal of Mathematics and Applied Science, 5(1), 11–16. https://doi.org/10.35877/mathscience3976
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