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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi
Caca Natasya Sugiana
Politeknik Negeri Bali
Computer Science & IT Economics, Econometrics & Finance

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Prediksi Kepatuhan Wajib Pajak PBB-P2 Berbasis Kecerdasan Buatan dengan Algoritma Gradient Boosting pada Badan Pendapatan Daerah Kabupaten Badung Caca Natasya Sugiana; I Putu Oka Wisnawa; Ni Gusti Ayu Putu Harry Saptarini
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp234-243

Abstract

Rural and Urban Land and Building Tax (PBB-P2) is an important source of Regional Original Income (PAD). However, suboptimal taxpayer compliance often results in under-targeted regional tax revenues. Conventional taxpayer compliance management encourages the need for Artificial Intelligence (AI) and machine learning-based approaches to support more proactive, data-driven decision-making. This study uses the Extreme Gradient Boosting (XGBoost) algorithm to predict PBB-P2 taxpayer compliance based on historical tax data. The research steps include data preprocessing, splitting the training and testing datasets with a 70:30 ratio, model training, hyperparameter tuning, and model evaluation using precision, recall, F1-score, and confusion matrix. The results showed that the best model produced an F1-Macro value of 0.7108 with a learning rate of 0.2, max depth of 12, n_estimators of 400, min_child_weight of 1, subsample of 0.8, and gamma of 0. The most influential variables in the prediction included the District Code, Principal Payment Amount, and Village Code. The XGBoost model was able to provide quite good classification performance in supporting the identification of PBB-P2 taxpayer compliance more effectively.
Co-Authors I Putu Oka Wisnawa Ni Gusti Ayu Putu Harry Saptarini