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All Journal Jurnal Krisnadana
I Made Dwi Asana
Program Studi Informatika, Fakultas Teknologi dan Informatika, Institut Bisnis Dan Teknologi Indonesia, Denpasar, Bali, Indonesia
Computer Science & IT Control & Systems Engineering

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AI-Based Model for Predicting On-Time Graduation of INSTIKI Students Using K-NN and Particle Swarm Optimization Made Leo Radhitya; I Made Dwi Asana; Ni Luh De Sri Chandra Purahita; I Made Subrata Sandhiyasa; I Nyoman Tri Anindia Putra
Jurnal Krisnadana Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i1.930

Abstract

This study aims to enhance the accuracy and generalization capability of student on-time graduation prediction by integrating the K-Nearest Neighbor (K-NN) algorithm with Particle Swarm Optimization (PSO) for parameter tuning. Historical academic records from INSTIKI were used as the primary dataset, and a 10-fold cross-validation technique was applied to ensure robust evaluation. The PSO algorithm was employed to determine the optimal k value for K-NN, with optimization parameters set to c1 = 0.5, c2 = 0.6, inertia weight w = 0.9, swarm size = 90 particles, and 100 maximum iterations. The optimized model achieved an optimal k = 23, resulting in a validation accuracy of 77.84%, outperforming the baseline K-NN’s 72.43%. In addition, improvements were observed in precision, recall, F1-score, and AUC, with the latter increasing from 0.56 to 0.68, indicating better discrimination capability. These results demonstrate that PSO effectively mitigates overfitting and enhances model stability compared to conventional K-NN. The proposed approach offers a reliable and scalable predictive model for academic early-warning systems, enabling institutions to identify at-risk students earlier and implement targeted interventions. Future work may involve incorporating non-academic features, addressing class imbalance, and exploring ensemble learning for further performance gains.
Co-Authors I Made Subrata Sandhiyasa I Nyoman Tri Anindia Putra Made Leo Radhitya Ni Luh De Sri Chandra Purahita