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Pengembangan Sistem Pengelompokan Belajar Mahasiswa pada Matakuliah Struktur Data dengan Metode K-Means

Sciprofile linkYogi Yunefri, Eddisyah Putra Pane, Sutejo Sutejo
INTECOMS: Journal of Information Technology and Computer Science , Volume 2, pp 59-66; doi:10.31539/intecoms.v2i2.812

Abstract: The development of science and technology requires tertiary institutions as formal education institutions, to be able to produce qualified and competent graduates. Learning about higher education needs to be more innovative and creative in producing learning and responsive to labor needs. "Successful constraints of lecturers in teaching Data Structure subjects do not have learning models that approach students with abstract theories that are difficult for students to understand, to overcome these conflicts. learn with the Application of Cooperative Oriented Problems. However, in terms of grouping learning with the application of this method, it still takes a relatively long time to do individual testing several times to find a suitable group, so that the learning grouping is less than optimal. The method used in this study is K-Means Clustering, from the software that was built to help instructors in the subject of data structure in the process of grouping tutoring students. Grouping methods can be implemented to build valid student guidance grouping software. Keywords: Learning Grouping System, Clustering, K-Means
Keywords: models / software / data structure / students / optimal / build / means / learning grouping

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