# Enhancing Scholarship Allocation Fairness Using GRU and Synthetic Minority Oversampling Technique
> Yasmine A.S.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105036001428
Jurnal / Konferensi: Beyond Technology Summit on Informatics International Conference Bts I2c 2025
Tahun terbit: 2025
DOI: https://doi.org/10.1109/BTS-I2C67944.2025.11399465
Citations: 0

## Authors
- Yasmine A.S.

## Abstract
Fair scholarship distribution plays a crucial role in advancing educational equity; however, manual selection processes often suffer from subjectivity, inconsistency, and potential bias. To address these limitations, this study proposes a predictive framework that integrates the Gated Recurrent Unit (GRU) neural network with the Synthetic Minority Oversampling Technique (SMOTE) for classifying scholarship eligibility among high school students. The dataset consists of 367 student records obtained from SMAN 2 Makassar, encompassing academic and socio-economic attributes. Data preprocessing involved cleaning, categorical encoding, normalization, and stratified data splitting to ensure balanced representation. The baseline GRU model trained on imbalanced data achieved a validation accuracy of 91.38% and a test accuracy of 79.45%, revealing overfitting and bias toward majority classes. After the application of SMOTE to balance the minority class, the retrained model achieved improved performance with 96.55% validation accuracy and 83.56% test accuracy, accompanied by a significant reduction in test loss. These findings demonstrate that SMOTE effectively enhances the GRU model’s generalization capability and predictive fairness. Overall, the proposed GRU–SMOTE framework offers a robust, data-driven, and efficient approach for fair scholarship eligibility prediction. It can serve as a reliable decision-support system in educational data mining, promoting objectivity and transparency in scholarship selection processes.

## Keywords
- Oversampling
- Machine learning
- Artificial intelligence
- Overfitting
- Scholarship
- Computer science
- Preprocessor
- Data pre-processing
- Data mining
- Classifier (UML)
- Feature selection
- Resampling
- Context (archaeology)
- Artificial neural network
- Selection (genetic algorithm)
- Categorical variable
- Test data
- Generalization
- Transparency (behavior)
- Accountability
- Boosting (machine learning)
- Test set
- Norm (philosophy)

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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