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Machine Learning-based Prediction of Building Permit Levy: A Comparative Study of Regression Algorithms

by Aolia Ikhwanudin, Agianto Syam Halim, Tubagus Toifur, Mumahamad Yusuf, Ibnu Masud, Tb Ai Munandar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 125
Year of Publication: 2026
Authors: Aolia Ikhwanudin, Agianto Syam Halim, Tubagus Toifur, Mumahamad Yusuf, Ibnu Masud, Tb Ai Munandar
10.5120/ijcaebdb557ce0b6

Aolia Ikhwanudin, Agianto Syam Halim, Tubagus Toifur, Mumahamad Yusuf, Ibnu Masud, Tb Ai Munandar . Machine Learning-based Prediction of Building Permit Levy: A Comparative Study of Regression Algorithms. International Journal of Computer Applications. 187, 125 ( Jul 2026), 33-44. DOI=10.5120/ijcaebdb557ce0b6

@article{ 10.5120/ijcaebdb557ce0b6,
author = { Aolia Ikhwanudin, Agianto Syam Halim, Tubagus Toifur, Mumahamad Yusuf, Ibnu Masud, Tb Ai Munandar },
title = { Machine Learning-based Prediction of Building Permit Levy: A Comparative Study of Regression Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2026 },
volume = { 187 },
number = { 125 },
month = { Jul },
year = { 2026 },
issn = { 0975-8887 },
pages = { 33-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number125/machine-learning-based-prediction-of-building-permit-levy-a-comparative-study-of-regression-algorithms/ },
doi = { 10.5120/ijcaebdb557ce0b6 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-07-29T00:34:13.741263+05:30
%A Aolia Ikhwanudin
%A Agianto Syam Halim
%A Tubagus Toifur
%A Mumahamad Yusuf
%A Ibnu Masud
%A Tb Ai Munandar
%T Machine Learning-based Prediction of Building Permit Levy: A Comparative Study of Regression Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 125
%P 33-44
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The building permit levy is a critical source of regional own-source revenue for local governments in Indonesia. Accurate prediction of levy amounts is essential for fiscal planning, transparency, and administrative efficiency. This study applies seven machine learning regression algorithms — Elastic Net, Random Forest, XGBoost, LightGBM, Gradient Boosting, Decision Tree, and k-Nearest Neighbors — to predict IMB levy values using building-related features derived from 14,971 permit application records. A comprehensive exploratory data analysis (EDA) pipeline was implemented, including missing-value audit, outlier analysis (IQR and Z-score), Pearson and Spearman correlation analysis, and data-leakage auditing. The evaluation combines a held-out test set, 5-fold cross-validation, bootstrap confidence intervals, feature ablation, raw-versus-log target comparison, randomized hyperparameter tuning, and a scenario-based analysis across levy magnitude, building function, designation, and building size segments. Random Forest achieves R² = 0.6875, MAE = Rp 5,002,478, and MAPE = 11.18% on the held-out set, while Gradient Boosting attains the best hold-out R² (0.8490); cross-validation shows the two ensembles are statistically close under the heavy-tailed target, and Elastic Net fails catastrophically (R² = −160.65), confirming the non-linear levy–feature relationship. Feature importance and ablation identify total building area (total_luas) as the overwhelmingly dominant predictor (83.76% importance), consistent with the multiplicative structure of the IMB levy formula. Scenario analysis reveals that relative errors concentrate in non-residential permits and at the extremes of the levy distribution, motivating a human-in-the-loop deployment for those segments. These findings establish tree-based ensembles as robust candidates for automated levy estimation in regional government digital services.

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Index Terms

Computer Science
Information Sciences

Keywords

Building Permit Levy Retribusi IMB Random Forest XGBoost LightGBM Elastic Net Regression Predictive Modeling Regional Revenue Indonesia PAD

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