Penerapan Algoritma Genetika Pada Aplikasi Optimasi Penentuan Kelompok KKM Reguler UIN Maliki Berbasis Web
DOI:
https://doi.org/10.30865/jurikom.v13i3.9736Keywords:
Optimization, Genetic Algorithm, KKM Regular, Group Optimization, Web ApplicationAbstract
A genetic algorithm was implemented in a web-based application to optimize the formation of Regular KKM groups at UIN Maulana Malik Ibrahim Malang. The main contribution of this study was reflected in the formulation of constraint rules that were aligned with the requirements of KKM group assignment, so that a fitness function different from those used in previous studies was established [15]. Group formation was carried out by considering four constraints, namely the presence of at least one HTQ member in each group, a low ratio of duplicated majors within a group, a gender proportion aligned with the data distribution, and an even number of members across groups. In addition, the algorithm was integrated into a web-based application so that the group formation process was not only optimized, but also supported by a more interactive system with a high level of usability. The system interface was developed using Laravel on the front-end side. The computational process was executed using Python on the back-end side. The relatively long computation time of the genetic algorithm was handled by applying a flagging-process mechanism in the database so that request timeouts could be avoided. Parameter testing was conducted on Popsize, Generation, Crossover Rate, and Mutation Rate to obtain the best configuration. The test results showed that the best solution was produced at the configuration of Popsize 70, Generation 400, Crossover Rate 0.5, and Mutation Rate 0.5, with a average fitness value of 0.983684211. The evaluation results showed that the number of groups fulfilling all criteria was increased from 82 groups to 177 groups after optimization. Thus, a more optimal, structured, and institutionally appropriate KKM group formation was achieved through the implementation of an interactive web-based system using a genetic algorithm.
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