Text Classification of 2024 Regional Head Elections Logistics Distribution in Online News Using Support Vector Machine - Murni Kassa; Irene Realyta Haldy Trosi Tangkawarow; Audy Aldrin Kenap (Journal of Embedded Systems, Security and Intelligent Systems, 2026)
Purpose – This study aims to classify online news related to logistics distribution issues in the 2024 Indonesian Regional Head Elections using Support Vector Machine and imbalance handling strategies. Design/methods/approach – A total of 1,355 online news articles were collected from nine national news portals through web scraping. The research workflow involved data preprocessing, rule-based weak supervision, manual validation, TF–IDF feature extraction, oversampling using SMOTE and ADASYN, class-weighted learning, and SVM classification with Linear, RBF, Polynomial, and Sigmoid kernels. Model performance was evaluated using macro-averaged F1-score, 5-fold cross-validation, classification report, and confusion matrix analysis. Findings - The results show that Linear and RBF kernels produced more consistent performance than Polynomial and Sigmoid kernels for sparse TF–IDF representations. The RBF kernel with class-weighted learning achieved the highest hold-out macro F1-score of 0.641, although cross-validation results showed only marginal differences among top-performing configurations. The model performed well in classifying “No Issues” and “Damaged” categories but still struggled with the minority “Late” class. Research implications/limitations – The findings indicate that machine learning can support preliminary election logistics monitoring, but the model should not yet be used as a fully automated early-warning system due to minority-class limitations and weak-labeling constraints. Originality/value – This study contributes empirical evidence on SVM-based imbalanced text classification for election logistics news monitoring in the Indonesian Pilkada context.