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Safwandi
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Application of Genetic Algorithm and Or-Tools for Cloud-Based Course Scheduling Optimization Jabbar, Salamul; Safwandi; Kurniawati; Eva Darnila; Fuadi, Wahyu
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.
Student Graduation Prediction System In the MBKM Program Using The Mamdani Fuzzy Method Muthiah Riani Harahap; Safwandi; Rini Meiyanti
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

This study aims to develop a graduation prediction system for the MBKM Program using the Fuzzy mamdani method. The system is designed to process various academic criteria such as GPA, internship experience, and other supporting documents to provide an accurate projection of graduation probability. The implementation was carried out using data from 61 students of the Informatics Engineering Department at Universitas Malikussaleh. The Fuzzy mamdani method was applied through stages of fuzzification, rule formation, fuzzy inference, and defuzzification to produce the final prediction. The test results show that this method is effective in handling uncertainty and provides a high prediction accuracy, where 67% of students were predicted to graduate, and 33% were not. This system can be used by academic staff to evaluate student performance and provide more precise guidance, as well as to help students plan their studies to achieve graduation in the MBKM Program.
Co-Authors Agustinar, Agustinar Anwar Asnita, Dessy Eliza, Zulfa Eva Darnila Fuadi, Wahyu Jabbar, Salamul Kurniawati M. Yahya Maryana Muarif Setiawan Muhammad Amin Muhammad Zaki Muhammad, Alfian Muthiah Riani Harahap Nisak, Fazlun Nurjannah Rini Meiyanti Riza Pahlevi, Riza Safwan Kamal Saiman . Sarita Ulfia Sugama, Yoga Sumarni, Mutia Ulya, Zikriatul Zefri Maulana ZUBIR Zulhilmi Zuliani, Rafiza