<![CDATA[This study aims to classify stress levels based on electroencephalography (EEG) signals using the Support Vector Machine (SVM) algorithm. The data used in this study came from 21 subjects with a total of 379 datasets, which included the main variables of Subject, Electrode Channel (E), Theta, Beta 1, and Beta 2. Preprocessing was done to ensure data quality, including blank data elimination, normalization, and feature engineering. One of the main features developed was the Beta Average, which was obtained by calculating the average between Beta 1 and Beta 2, and stress level classification, which was determined based on the comparison between the Beta Average and Theta. The SVM algorithm was applied to build a stress classification model with an initial stage of manual calculation to understand the basic concepts, followed by the Python programming language implementation. The evaluation results show that the developed model has an accuracy of 92.76%, with the highest precision, recall, and f1-score values reaching 100% and the lowest value of 85%. The confusion matrix analysis showed that the model could classify low stress with 100% accuracy, while it reached 87.8% for high stress. The findings of this study prove that the SVM algorithm effectively classifies EEG signal-based stress levels. This model can be the basis for further development of stress detection methods, especially in mental health and neuroinformatics applications.]]>
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