Optimasi K-Means untuk Pengelompokan Data Kinerja Akademik Dosen menggunakan Particle Swarm Optimization - Dian Werdiningsih Dwi Rahmawati; Imam Cholisoddin; Nurudin Santoso (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Lecturers are teachers and tutors for students. In addition to teaching and guiding, lecturers are also required to assist in developing science as well as potential and expertise in itself. Variables or criteria that exist in the lecturer include Education, research, devotion, administration, and support. One of the difficulties faced by universities is the ideal grouping of assignments to lecturers. Assignment of lecturers related to the committee, further study, positions, filler of an event held from internal and external universities and others. So it takes a system that can classify the academic performance of lecturers using K-Means algorithm combined with Particle Swarm Optimization algorithm. The Particle Swarm Optimization algorithm is useful in optimizing the centroid on the K-Means algorithm. The data used is data from the Faculty of Computer Science (FILKOM), Universitas Brawijaya 2016. The data is obtained from GJM FILKOM. The results of this study obtained K-Means algorithm combined with Particle Swarm Optimization algorithm better 3.28% compared with stand-alone K-Means algorithm, where the cluster quality is determined using Silhouette Coefficient method.