<![CDATA[Examination timetabling in vocational higher education is a combinatorial optimization problem involving courses, students, invigilators, classrooms, laboratories, and time slots. At AMIK Medicom, the schedule preparation process is still semi-manual, which may cause student conflicts, room conflicts, unbalanced invigilator assignments, and poor exam distribution. This study aims to design an examination timetabling optimization model by integrating Linear Programming (LP) and Simulated Annealing (SA). LP is used to formulate hard constraints to ensure schedule feasibility, while SA is applied to improve schedule quality based on soft constraints, such as exam spread and room utilization. The dataset consists of 29 lecturers, student data from academic years 2021/2022 to 2024/2025, and curricula from three study programs with 154 courses in total. The proposed design shows that the model can transform the examination timetabling problem into structured decision variables, objective functions, and mathematical constraints. In the prototype testing scenario, the LP-SA approach is directed to produce a timetable with zero hard-constraint conflicts and lower soft-constraint penalties compared with semi-manual preparation. The model can serve as a basis for developing a web-based automatic examination scheduling system to improve academic administrative efficiency at AMIK Medicom. ]]>
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