Application of the Gradient Boosting Algorithm for Classification of Zakat Recipient Groups in Supporting the Digital Transformation of Zakat Management
Keywords:
zakat, mustahik, asnaf, gradient boosting, machine learning, classification, socio-economic data, digital transformationAbstract
Background: Accurate distribution of zakat is one of the main challenges in modern zakat management. The manual process of identifying and classifying zakat recipients (mustahik) has the potential to lead to inaccuracy, subjectivity, and inefficiency. In the context of digital transformation, a machine learning-based approach can be a solution to support a more objective and efficient classification process.
Purpose: This study aims to develop a classification model for zakat recipients based on eight categories of asnaf (obligatory zakat) using the Gradient Boosting algorithm and compare its performance with the Decision Tree and K-Nearest Neighbor algorithms.
Methods: This study uses a quantitative approach using socio-economic data obtained from the Kaggle platform. The variables used include demographic, economic, and social aspects. The research stages include data pre-processing, dividing training and test data, model training, and parameter optimization to improve classification performance. Model evaluation is conducted using accuracy as the main indicator, accompanied by an analysis of the features that most influence the classification results.
Results: The research results show that the Gradient Boosting algorithm achieved an accuracy of 81.5% and performed better than Decision Tree and K-Nearest Neighbor. Furthermore, monthly income and employment were identified as the most influential features in determining the classification of zakat recipients.
Conclusions: These findings demonstrate that the application of machine learning, specifically Gradient Boosting, has significant potential to improve the accuracy of mustahik classification. This approach can support the digital transformation of zakat management to be more objective, efficient, and data-driven.
Research Contribution: This research contributes to the development of digital innovation in zakat management through the application of machine learning-based classification methods. Practically, the research results can serve as a reference for zakat management institutions in improving the quality of zakat fund distribution to ensure more targeted and accountable distribution.
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