Sistem Pendukung Keputusan Penerimaan Beasiswa Menggunakan Metode Bayesian Probability Untuk Meningkatkan Akurasi Seleksi - Wahyudi; Jajang Mulyana (Informatics Engineering, Faculty of Engineering and Computer Science, Universitas Buana Perjuangan Karawang, 2026)
Scholarship selection processes in higher education institutions commonly involve multiple evaluation criteria, making manual assessment time-consuming, inconsistent, and prone to subjective bias. This study proposes a web-based Decision Support System (DSS) for scholarship recipient selection, built upon Bayesian Probability inference integrated with Laplace Smoothing to address the zero-probability limitation inherent in conventional Naive Bayes classification. The system processes five standardized selection criteria — cumulative GPA, parental income, residential status, active semester, and non-academic achievement — and produces real-time eligibility recommendations alongside normalized posterior probability visualizations to enhance decision transparency. A dataset of 150 historical scholarship application records from a university in Karawang, covering two academic years (2022/2023 and 2023/2024), was used to train and evaluate the model. Performance assessment was conducted through 5-fold cross-validation, yielding a mean accuracy of 91.67%, precision of 90.00%, recall of 93.33%, and F1-score of 91.61%. Comparative analysis against conventional Naive Bayes, TOPSIS, and Bayesian-AHP hybrid methods demonstrated consistent superiority across all evaluation metrics. The 13.67% accuracy improvement over conventional Naive Bayes was primarily attributed to the elimination of zero-probability events through Laplace Smoothing with α = 1, as well as structured posterior normalization. User acceptance testing confirmed that the probability distribution visualization feature significantly improved operator comprehension of the system's reasoning. These findings establish Bayesian Probability with Laplace Smoothing as a technically sound and practically effective foundation for automated, transparent, and auditable scholarship decision support systems.