<![CDATA[The rapid development of technology has greatly facilitated various daily activities, including communication. Several social media platforms enable users to connect with one another easily. However, this progress often comes with negative impacts. One such impact is cyberbullying, which has become a growing concern. To address this issue, many researchers have proposed solutions for detecting cyberbullying. Among these, the Support Vector Machine (SVM) method is commonly used because it delivers more accurate results compared to other algorithms. However, determining the optimal kernel parameters for SVM remains a challenge. To overcome this, various search algorithms have been suggested to optimize SVM parameters. This study utilizes a genetic algorithm to find the optimal values for the C and gamma parameters. The results demonstrate an accuracy improvement, with the genetic algorithm achieving an accuracy of 86%. This highlights the effectiveness of genetic algorithms in optimizing SVM parameters for cyberbullying detection.]]>
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