Optimizing The Support Vector Machine Method Using Recursive Feature Elimination For Monkeypox Symptom Classification - Yuniar Farida; Iftitakhun Ni'mah; Putroue Keumala Intan (Jurnal Sisfokom (Sistem Informasi dan Komputer), 2026)
Regional outbreaks of monkeypox highlight the need for accurate and efficient symptom-based classification to support early detection. This study aims to improve the classification performance of monkeypox symptoms using a Support Vector Machine (SVM) optimized with Recursive Feature Elimination (RFE). The dataset consists of 1,000 cases, which were preprocessed via encoding and normalization, followed by feature selection using RFE and classification with SVMs using various kernel functions. Model performance was evaluated using accuracy, precision, sensitivity, and specificity. The results show that RFE successfully identified eight key features—Rectal Pain, Sore Throat, Penile Swelling, Oral Lesions, Swollen Tonsils, Single Lesions, HIV Infection, and Sexually Transmitted Infections—as the most influential variables. The optimized SVM, validated using a confusion matrix, achieved 77% accuracy, 84% precision, 66% sensitivity, and 88% specificity, representing a modest improvement over the baseline SVM (75%). The polynomial kernel demonstrated the best performance, indicating the presence of nonlinear relationships among symptoms. Although the improvement is relatively small, integrating RFE enhances feature relevance and model stability. These findings suggest that feature selection is an effective strategy for refining classification performance, while further validation and comparison with alternative methods are recommended to ensure robustness and generalizability.