Implementation of the XGBoost Algorithm for Predicting Monthly Regional Revenue Performance in Bandung City
DOI:
https://doi.org/10.37859/coscitech.v6i2.9578
Abstract
Abstract
Local Own-Source Revenue (PAD) is a key pillar in financing regional development. In Bandung City, discrepancies between revenue targets and actual realization remain a challenge to effective fiscal planning. This study aims to develop a predictive model for monthly PAD achievement using the XGBoost algorithm, known for its strength in handling non-linear and complex data. The dataset, obtained from the Bandung Revenue Agency (Bapenda), includes various types of regional taxes from 2018 to 2024. The research process involved data cleaning, feature engineering, data splitting, model training, and performance evaluation using MAE, RMSE, and R² metrics. The evaluation on test data resulted in MAE of IDR 5.6 billion, RMSE of IDR 9.3 billion, and R² of 73%. Meanwhile, 5-fold cross-validation yielded MAE of IDR 3.49 billion, RMSE of IDR 6.65 billion, and R² of 86%. These results demonstrate high accuracy and generalization capability. XGBoost proves to be a reliable decision-support tool for data-driven fiscal planning.
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References
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