Evaluation of Support Vector Machines and Adaptive Boosting in Classifying the Compliance Levels of Property and Building Taxpayers Using Receiver Operating Characteristic (ROC)
DOI:
https://doi.org/10.30871/jaic.v10i4.13643Keywords:
Adaptive Boosting, Area Under Curve, Land and Building Tax, Receiver Operating Characteristic, Support Vector Machine.Abstract
Taxpayer compliance is a critical factor in increasing Property Tax (PBB) revenue. A low level of compliance can reduce local government revenue, making accurate classification methods essential for identifying taxpayer compliance. This study aims to compare the performance of Support Vector Machine (SVM) and Adaptive Boosting (AdaBoost) in classifying property taxpayer compliance. The dataset consisted of 58,998 property tax records collected from Lhokseumawe City, covering the districts of Banda Sakti, Blang Mangat, Muara Dua, and Muara Satu. The research stages included data preprocessing, label encoding, Min–Max normalization, data splitting using 80:20 and 70:30 scenarios, model training, and performance evaluation using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Under the 80:20 data split, SVM achieved an accuracy of 92.89%, precision of 93.12%, recall of 98.81%, F1-score of 95.87%, and AUC of 80.59%, while AdaBoost achieved an accuracy of 92.86%, precision of 93.11%, recall of 98.78%, F1-score of 95.86%, and AUC of 80.76%. Under the 70:30 data split, SVM achieved an accuracy of 93.02%, precision of 93.18%, recall of 98.90%, F1-score of 95.95%, and AUC of 80.32%, whereas AdaBoost achieved an accuracy of 92.99%, precision of 93.18%, recall of 98.87%, F1-score of 95.93%, and AUC of 80.97%. Overall, both methods demonstrated comparable classification performance, while AdaBoost exhibited slightly better discriminative capability based on the AUC values.
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