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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INOVTEK Polbeng - Seri Informatika
Safwandi
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.
SISTEM PAKAR DIAGNOSIS PENYAKIT PARU MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DAN RULE BASED SYSTEM: EXPERT SYSTEM FOR LUNG DISEASE DIAGNOSIS USING CONVOLUTIONAL NEURAL NETWORK AND RULE-BASED SYSTEM Putri Syifa; Safwandi; Zahratul Fitri
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6548

Abstract

Lung disease remains a major health problem in Indonesia, accounting for 25.8% of respiratory-related deaths according to the Ministry of Health. Data from Muhammad Ali Kasim Gayo Lues Regional Hospital, Aceh, shows approximately 3,600 cases recorded since 2022. This study designs an artificial intelligence-based diagnostic system combining 224x224 pixel chest X-ray image analysis with clinical parameter evaluation using a rule-based system. The Rule-Based System implements standardized weighting where each clinical manifestation contributes proportionally to the total diagnostic score through normalization to a 100-point scale.The dataset consists of 983 images (786 training, 197 validation) collected during the 2022-2025 period, covering tuberculosis (300 cases), pneumonia (300 cases), and pneumothorax (383 cases) with 25 disease symptoms. Evaluation results show the system achieves 94.97% accuracy with 93.2% sensitivity and 95.4% specificity. The F1-scores for each disease were 0.9375 (Tuberculosis), 0.9125 (Pneumonia), and 0.987 (Pneumothorax). Therefore, this system can assist the diagnostic process and support clinical decision-making through both radiographic image analysis and patient symptom evaluation.
Co-Authors Eva Darnila Kurniawati Putri Syifa Salamul Jabbar Wahyu Fuadi Zahratul Fitri