A Decision Tree Model with Grid Search Optimization for Scholarship Recipient Classification - Suprapti, Tati; Nurhakim, Bani; Warni Ayu Hermina, Bintang; Syahputra Simbolon, Vrendi Amro (Jurnal Teknik Informatika (Jutif), 2025)
This study aims to classify scholarship recipients using the Decision Tree algorithm implemented in RapidMiner. The dataset consists of 1.404 records with socioeconomic and academic attributes. Preprocessing was conducted using two Replace Missing Value operators, where categorical attributes such as No. BANTUAN, No. KKS, and Prestasi were filled with "Tidak Punya," while Kepemilikan Rumah was imputed using the average value. The model was built using a Decision Tree algorithm, optimized with the Optimize Parameters (Grid) operator to determine the best values for maximal depth and confidence. Evaluation was performed using 10-fold Cross Validation to ensure reliability. The results show that the optimized Decision Tree model achieved a high accuracy of 97.72%, with strong precision, recall, and F1-score values in both the "Eligible" and "Not Eligible" classes. These findings demonstrate that the Decision Tree algorithm, when properly optimized and validated, can effectively support decision-making processes in scholarship eligibility classification. The model provides an interpretable and robust tool for educational institutions to evaluate student applications based on critical socioeconomic features, This research contributes to educational data mining by offering a validated and interpretable model that enhances fairness, transparency, and efficiency in the scholarship selection process.