<![CDATA[The audit and reconciliation of Local Government-Owned Assets (BMD) constitute a critical governance process that remains heavily reliant on manual verification, rendering it susceptible to inefficiency and human error, particularly within datasets exhibiting extreme heterogeneity in acquisition values. This study proposes an intelligent analytical framework for asset depreciation anomaly detection using the Extreme Gradient Boosting (XGBoost) algorithm, optimized via a Bayesian Optimization approach through the Optuna framework. From an initial raw population of 98,526 records, the data underwent preprocessing to yield 42,241 clean records with unique profiles. To address the disparity in price ranges, the dataset was divided into three strata using the Equal Frequency quantile method, with the prediction target transformed into a depreciation ratio. The evaluation demonstrated highly precise performance, consistently achieving a coefficient of determination (R²) above 0.99. Bayesian optimization reduced the Weighted Average Percentage Error (WAPE) to a range of 0.54% to 1.32%. Using a 10% deviation threshold, the system automatically extracted 101 anomalous records (1.20%) from 8,450 test samples as red flags. The results confirm that this framework is highly viable as a decision-support instrument for public asset audits, in compliance with regional regulations.]]>
Copyrights © 2026
