Dina Yulina H
Universitas Sarjanawiyata Tamansiswa

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PEMODELAN SISTEM OPTIMASI PENJADWALAN MATAKULIAH DENGAN ALGORITMA GENETIKA Eka Yulia Sari; Dina Yulina H; Titik Rahmawati
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 4 No. 1 (2023): Juni 2023
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v4i1.97

Abstract

Scheduling courses at each university is a complicated and complex job because there are many constraints and rules that must be considered. Scheduling is also crucial in the teaching and learning process because lecturer and student activities depend on the schedule that has been prepared. General problems that need to be considered when scheduling are the possibility that the lecturer will teach more than one course and the possibility that the number of courses and lecturers is not balanced. The preparation of class schedules at the Universitas Sarajanawiyata Tamansiswa, Faculty of Engineering is still done manually using excel so that the process of preparing the schedule takes a long time so it is less effective. This study aims to design a modeling system for optimizing course scheduling at the UST Faculty of Engineering with a Genetic algorithm in order to facilitate the development of a scheduling and optimize the time for preparing course schedules system. The data used is secondary data in the form of course scheduling data at the UST Faculty of Engineering even semester of the 2022/2023. The final result of this research is the scheduling optimization system modeling at the UST Faculty of Engineering. System process modeling is carried out using UML (Unified Modeling Language) which consists of 3 diagrams, namely Use Case Diagrams, Activity Diagrams, and Class Diagrams while modeling the analysis of data storage using ERD (Entity Relation Diagrams). In this study, 4 hard constraints and 3 soft constraints were applied which were the limitations in processing scheduling with genetic algorithms.