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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INOVTEK Polbeng - Seri Informatika
Kurniawati
Malikussaleh University
Computer Science & IT Engineering

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Application of Genetic Algorithm and Or-Tools for Cloud-Based Course Scheduling Optimization Salamul Jabbar; Safwandi; Kurniawati; Eva Darnila; Wahyu Fuadi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/qymmt569

Abstract

Course scheduling in higher education institutions is a complex combinatorial optimization problem involving numerous constraints such as lecturer availability, room capacity, time slots, and course distribution across semesters. Manual scheduling practices often result in conflicts, inefficient resource utilization, and prolonged preparation time. This study proposes a hybrid course scheduling system that integrates a genetic algorithm (GA) and constraint programming using the CP-SAT solver from OR-Tools. The GA is employed in the first phase to generate optimal course sections based on student enrollment, lecturer workload, and capacity constraints. The best solution produced by the GA is then refined using CP-SAT to generate a conflict-free timetable that satisfies all hard constraints, including lecturer, room, and time conflicts, while also optimizing selected soft constraints. The proposed system is implemented as a web-based application deployed on Microsoft Azure, enabling scalability and accessibility. Experimental results using real academic data demonstrate that the hybrid approach successfully produces feasible schedules with zero conflicts and significantly reduces scheduling time compared to manual methods. The results confirm that the integration of GA and CP-SAT provides an effective and flexible solution for university course scheduling problems.
KLASIFIKASI PENYAKIT MATA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN MODIFIED BALANCED RANDOM FOREST: CLASSIFICATION OF EYE DISEASES USING THE SUPPORT VECTOR MACHINE AND MODIFIED BALANCED RANDOM FOREST METHODS Muhammad Arief; Munirul Ula; Kurniawati
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6655

Abstract

Eye diseases such as refractive, implant, cataract, and other disorders can reduce the quality of life of sufferers if not diagnosed and treated properly. Limited access to medical examinations is one of the obstacles in early treatment, so an accurate and efficient technology-based decision support system is needed. This study aims to compare the performance of the Linear Support Vector Machine (SVM) and Modified Balanced Random Forest (MBRF) algorithms applied to handle class imbalance in classifying four categories of eye diseases using medical records of PIM (Prima Inti Medika) Hospital patients consisting of 2,805 data with 30 features. The research process includes preprocessing, model training, and performance evaluation using 5-Fold Cross Validation as well as testing on a test set of 20% of the data. Evaluation metrics used include accuracy, precision, recall, and F1-score. The test results show that MBRF excels in all metrics, with an average F1-Macro cross-validation of 85.67% (3.33% higher than SVM) and a test set accuracy of 87.32% (3.21% higher). Analysis per category shows an average F1 improvement of 3.6%, with the highest improvement in the Refractive class (+5.0%). MBRF also has a 21% faster training time than Linear SVM and provides clinically relevant feature importance information. Based on these results, MBRF is recommended as the main model in a decision support system for diagnosing eye diseases on a similar dataset.  
Co-Authors Eva Darnila Muhammad Arief Munirul Ula Safwandi Salamul Jabbar Wahyu Fuadi