Optimasi K-Means Untuk Clustering Dosen Berdasarkan Kinerja Akademik Menggunakan Algoritme Genetika Paralel - Endah Utik Wahyuningtyas; Rekyan Regasari Mardi Putri; Sutrisno Sutrisno (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
The main task of a lecturer is to produce quality human resources and solve problems that exist in the wider community through research, dedication, and so forth. Competence owned by a lecturer determines the quality of the implementation of the college tridharma. So it is necessary to evaluate the academic performance of lecturers conducted periodically by the quality assurance team. Evaluation of academic performance of lecturers aims to maintain the quality of institutions, facilitate the decision-making, and provide appropriate treatment for improving the quality of lecturers. Each lecturer can have different competencies with each other. Therefore, there needs to be a grouping of data related to the academic performance of lecturers optimally. In this research, a lecturer clustering system will be built based on academic performance using k-means clustering method. Given that the method has the disadvantage of often getting different clusters because the initialization of the centroid is done randomly, therefore there is a need for centroid optimization on the k-means algorithm. The parallel genetic algorithm can be used to optimize the cluster center on the k-means algorithm. The result of clustering shows that cluster center optimization using parallel genetic algorithm get better result than only k-means method.