SYUKRON AFANDI - Event Project Manager

Application of the Differential Evolution (DE) Algorithm to Solve the University Course Timetabling Problem (UTCP): A Case Study at the Faculty of Science and Technology, Universitas Airlangga

This undergraduate thesis focuses on mathematical modeling and combinatorial optimization within the context of academic scheduling systems. The study addresses the University Course Timetabling Problem (UTCP), a complex NP-hard problem involving the allocation of courses, lecturers, classrooms, and time slots without conflicts.

The research develops a structured mathematical model incorporating hard and soft constraints into a well-defined objective function. To obtain optimal solutions, the study implements the Differential Evolution (DE) algorithm, a population-based metaheuristic optimization method.

Key contributions include:

• Formulating a comprehensive mathematical model consisting of sets, parameters, decision variables, constraints, and an objective function.
• Implementing the Differential Evolution algorithm through systematic stages of initialization, mutation, crossover, and selection.
• Developing a computational program (C++) to test algorithm performance across small, medium, and large datasets.
• Evaluating the influence of population size and iteration count on solution quality.

Findings indicate that increasing the number of iterations and population size significantly improves scheduling quality, while variations in crossover probability show minimal impact.

Conceptually, this thesis emphasizes structured objective-function design, analytical rigor, and quantitative decision support in solving complex optimization problems.

Full manuscript available upon request.

Analyzing the Effect of AI Awareness on Employee Turnover Intention: The Mediating Roles of Career Competency, Job Burnout, and Organizational Commitment among Banking Employees in Indonesia

This master's thesis is situated within the field of Human Resource Development (HRD) and examines the strategic implications of Artificial Intelligence (AI)-driven digital transformation in the Indonesian banking sector.

The study investigates how AI awareness influences employee turnover intention, considering the mediating mechanisms of:

• Career Competency
• Job Burnout
• Organizational Commitment

Using a quantitative explanatory approach with a cross-sectional survey of 250 banking employees, the research applies Partial Least Squares - Structural Equation Modeling (PLS-SEM) to test causal relationships and mediation effects simultaneously.

The findings reveal that:

• AI awareness increases job burnout and decreases organizational commitment and career competency.
• Job burnout positively influences turnover intention.
• Career competency and organizational commitment negatively influence turnover intention.
• The relationship between AI awareness and turnover intention occurs indirectly through career and psychological mechanisms.

Theoretically, this study extends Career Theory by positioning AI as a form of career shock that triggers employees' evaluation of career sustainability in the face of technological disruption.

Practically, the research provides strategic implications for organizations undergoing digital transformation, highlighting the importance of continuous competency development, burnout prevention, and strengthening organizational commitment to maintain workforce stability.

Overall, this thesis demonstrates that technological transformation is not merely a technical shift but a career-related phenomenon that shapes employees' long-term work decisions.

Full manuscript available upon request.