Comparison of the Performance of Support Vector Machines and Naïve Bayes in Analyzing Sentiment Regarding Money Politics in the 2024 General Election - Olgraciella Manitik; Irene R.H.T Tangkawarow; Alfiansyah Hasibuan (Academic Bright Collaboration, 2026)
Purpose – This study aims to compare the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms in classifying public sentiment on money politics in the 2024 General Election in Indonesia, as well as to determine which algorithm has the highest accuracy, precision, recall, and F1-score. Methods – A total of 1,280 Indonesian-language tweets from the X platform containing the keyword “money politics” were collected and preprocessed. Of these, 384 tweets were manually labeled by two annotators (Cohen’s Kappa = 0.9493). TF-IDF and SMOTE feature extraction were applied before splitting the data into training (70%) and testing (30%) sets. Model evaluation used accuracy, precision, recall, F1-score, ROC-AUC, paired t-test, and bootstrap confidence intervals. Findings – Naïve Bayes (α=0.1) achieved accuracy 79.31%, precision 70.73%, recall 70.73%, F1-score 0.7073, ROC-AUC 0.8387. SVM (C=1, kernel RBF) achieved 77.59%, 85.71%, 43.90%, 0.5806, 0.8410. Paired t-test (p=0.8326) showed comparable performance across 10-fold CV. Naïve Bayes demonstrated better stability (bootstrap CI [0.5974–0.8101] vs. SVM [0.4193–0.7294]). Research implications – The methodological framework (preprocessing, SMOTE, TF-IDF, tuning, 10-fold CV, paired t-test, bootstrap CI) serves as a reference for election-related text classification in Indonesia. Results offer initial insights for election monitoring agencies (e.g., Bawaslu). Originality – This study provides a comparison of SVM and Naïve Bayes specifically for detecting money politics sentiment in the 2024 Indonesian election, a topic with limited prior research. It applies structured pipeline including manual labeling (high inter-annotator agreement), SMOTE, and statistical validation (paired t-test, bootstrap CI).