Optimasi K-Means untuk Clustering Kinerja Akademik Dosen Menggunakan Algoritme Genetika - Budi Santoso; Imam Cholissodin; Budi Darma Setiawan (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2017)
Lecturers are teacher for students, besides teaching, lecturers also have many other activities by utilizing the expertise they have to develop the potential of the lecturer. Some of the characters that each lecturer are so different, such as education, research, dedication, administration, and support. The difficulties faced by the campus, one of them is related to the grouping of assignments to lecturers. The assignment is related to further studies, recommendations, structural related positions, filling an event, commission, etc.So that required a system that can classify the academic performance of lecturers optimally. In this study to classify the academic performance of lecturers using K-Means method is optimized with genetic algorithm. Genetic algorithm acts to optimize the cluster's initial center on K-Means.Data algorithm used in this research is the data of lecturers in UB's Computer Science faculty in 2016. The data obtained from GJM faculty of computer science of Universitas Brawijaya. The result of clustering test of academic performance of lecturer using GA-Kmeans algorithm has higher cluster quality that is 2,74% compared to K-Means algorithm without genetic algorithm, where the cluster quality obtained using Silhouette Coefficient method.