<![CDATA[The increase in carbon emissions due to individual activities and lifestyles has become one of the main contributors to global climate change. The emerging issue is how to accurately classify individual carbon impact levels based on lifestyle behavior data. This study aims to apply the Support Vector Machine (SVM) method in classifying carbon emission levels into three categories, namely High, Medium, and Low. The dataset used consists of 1,400 data points with 10 initial features, which include numerical and categorical variables. The preprocessing stage is carried out through standardization using StandardScaler and categorical transformation with OneHotEncoder within a Pipeline framework to prevent data leakage. The SVM model with a Radial Basis Function (RBF) kernel was optimized using GridSearchCV and produced the best parameters C =75 and γ = 0.0588. With an 80:20 data split, the model achieved an accuracy of 94%, and precision, recall, and F1-score values ranged from 90% to 95%. An AUC value of 98%–100% indicates a very good discriminatory ability. The study results conclude that the SVM method is effective and reliable for classifying carbon emission levels based on lifestyle behavior.]]>
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