Explainable Ensemble Learning for Transparent and Efficient Zakat Scholarship Selection
DOI:
https://doi.org/10.59188/eduvest.v6i2.52639Keywords:
Decision Support System, Scholarship Selection, Machine Learning, Low-code/No-code, Explainable AIAbstract
Education financial assistance funded by Zakat, Infaq, and Waqf holds significant potential to support sustainable higher education. Islamic Trust Fund Ar-Raniry faces challenges in the scholarship selection process due to manual processing. This condition leads to operational inefficiency and raises concerns of subjectivity due to a lack of transparency in rejection or acceptance decisions. This study aimed to improve efficiency, objectivity, and transparency of the selection process with a Decision Support System. The study conducted a Decision-Oriented Diagnosis and Feasibility Study to understand the decision-making process. The design of a Decision Support System employed Unified Modeling Language. A prototype created using Low-code/No-code development tools following the Rapid Application Development methodology, while model development adhered to the Cross-Industry Standard Process for Data Mining. The best classification model is a Soft Voting Ensemble of Naïve Bayes, K-Nearest Neighbors, and Support Vector Machine (SVM) with Synthetic Minority Oversampling Technique (SMOTE). The model achieved an Accuracy of 75.63%, a Macro Precision of 72.80%, Minority Class Precision of 65.96%, Macro Recall of 71.16%, Minority Class Recall of 57.41%, F1-score of 71.79%, and AUC ROC of 69.57% in Holdout Testing. Local Interpretable Model-Agnostic Explanation explained the classification results. The main factor affecting classification for the Accepted class is the Fee Level. The implementation resulted in integrated data and automated business processes. Thereby supporting the acceleration of an efficient, objective, and transparent selection process. An implementation strategy was formulated, including parallel conversion, computer-based training, and system support and maintenance.
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