Penerapan Metode Clustering dengan Algoritma K-Means pada Pengelompokkan Data Calon Mahasiswa Baru di Universitas Muhammadiyah Yogyakarta (Studi Kasus: Fakultas Kedokteran dan Ilmu Kesehatan, dan Fakultas Ilmu Sosial dan Ilmu Politik)
DOI:
https://doi.org/10.18196/st.211211Keywords:
data mining, k-means, clustering, penmaru, WEKAAbstract
The increasing new prospective students in a University to make the stack more and more data, departing from it then conducted a search for new knowledge with data mining. Grouping data for prospective new students will be made by the method Clustering and used the algorithm k-means. In this penmaru there are 5 data attributes are used i.e., hometown, gender, status to qualify for selection, driveways, and majors. This analysis is performed using WEKA software and the source data taken from admissions data (penmaru) in the form of a data warehouse. Class from the use of this method is the attribute of the majors. Iteration performed as many as 3 times and the number of a cluster at the Faculty of medicine and health sciences, i.e. 4 clusters, Faculty of social and political science 3 clusters. Method Clustering can be applied to the classification of data for prospective new students. Another thing that can be analyzed from the results of the grouping candidate data, promotion strategies from each Department to increase the quantity and quality.
References
Aranda, J., Natasya, WAG. 2016. “Penerapan Metode K-Means Cluster Analysis Pada Sistem Pendukung Keputusan Pemilihan Konsentrasi Untuk Mahasiswa International Class STMIK AMIKOM Yogyakarta” dalam Jurnal Karya Ilmiah Teknik Informatika. Volume 4, No 1.
Asroni., Adrian, R. 2015. “ Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang” dalam Jurnal Ilmiah Semesta Teknika. Volume 18. No 1.
Fadlika Dita Nurjanto. 2013. Tahap-tahap K-Means Clustering. https://fadlikadn.wordpress.com/2013/06/14/tahap-tahap-k-means-clustering/, 24 Agustus 2016.
Hermawati, F. A. Data Mining. 2013. Andi: Yogyakarta.
Kusrini, E. T. L. (2009). Algoritma Data Mining. Yogyakarta: Andi Offset.
Narwati. 2010. “Pengelompokkan Mahasiswa Menggunakan Algoritma K-Means’’ dalam jurnal Dinamika Informatika. Volume 2, No 2.
Nasari, F., & Darma, S. (2013). Penerapan K-Means Clustering pada Data Penerimaan Mahasiswa Baru (Studi Kasus: UNIVERSITAS POTENSI UTAMA). SEMNASTEKNOMEDIA ONLINE, 3(1), 2-1.
Ong, J. O. (2013). Implementasi Algoritma K-Means Clustering Untuk Menentukan Strategi Marketing President University.
Prasetyo, E. Data Mining: Konsep Dan Aplikasi Menggunakan MATLAB. 2012. Penerbit ANDI. Yogyakarta.
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