<![CDATA[HIV classification using machine learning is often challenged by severe class imbalance, which may reduce predictive reliability and bias classification models toward the majority class. This study aims to compare the effectiveness of two hybrid oversampling techniques, namely SMOTE-DBSCAN and RN-SMOTE, in improving HIV classification performance using an Indonesian HIV dataset. The dataset was obtained from a Non-Governmental Organization (NGO) and initially consisted of 707,389 records with 90 attributes. After data preprocessing, 148,408 validated records with 30 attributes were retained, comprising 135,739 non-reactive and 12,668 reactive HIV cases, indicating a substantial class imbalance. Two feature configurations derived from previous HIV studies were evaluated using SMOTE-DBSCAN and RN-SMOTE. To prevent data leakage, the dataset was partitioned using a stratified 80:20 train-test split before oversampling, while model validation was performed using 10-fold cross-validation on the training data. The balanced datasets were subsequently evaluated using eight classification algorithms. Experimental results demonstrate that RN-SMOTE consistently outperformed SMOTE-DBSCAN across both feature configurations, while Random Forest achieved the best overall predictive performance. Although SMOTE-DBSCAN required less computational time during the balancing process, RN-SMOTE produced more robust classification performance by generating cleaner and more representative minority-class samples. These findings demonstrate the effectiveness of noise-aware hybrid oversampling strategies for improving HIV classification on highly imbalanced datasets and provide practical insights for developing reliable machine learning models to support HIV surveillance and public health decision-making]]>
