DATA MINING MNGGUNAKAN ALGORITMA K-MEANS CLUSTERING UNTUK PENGELOMPOKAN PRODUK YANG PALING TIDAK LAKU TERJUAL PADA KOPERASI MAHASISWA UNIVERSITAS NEGERI YOGYAKARTA(KOPMA UNY)
Abstract
Data mining is extracting large amounts of data to gain knowledge or information for its users. In this application is used clustering method by using K-Means algorithm. From the data processed with sample data products that exist in the Student Cooperative State University of Yogyakarta (Kopma UNY),then produce two types of data groups that are sales data products sold and unsold behavior so that the products in the warehouse does not accumulate. Based on the results of research using K-Means algorithm clustering products that are categorized as the best-selling sold with the average sales of 2545.36 and products categorized unsold that is sold with the average sales of 231.2.
References
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UNY, A. K. (2017, 07 31). Tentang Kopma UNY. Retrieved 06 Kamis, 2017, from Koperasi Mahasiswa Universitas Negeri Yogyakarta: http://www.kopmauny.com/en/profile/
Gunawan, A. d. (2014). Sistem Pendukung Keputusan Pemasaran Produk Menggunakan Data Mining Dengan K-Means Clustering. Bandung: SESINDO.
Han, J. K. (2011). Data Mining: Concept and Techniques. Second Edition. Waltham: Morgan Kaufmann Publishers.
Metisen, B. M., & Sari, H. (2015). ANALISIS CLUSTERING MENGGUNAKAN METODE K-MEANS. Jurnal Media Infotama, Vol. 11, No. 2.
Putri, D. E. (2015). Metode Non Hierarcy Algoritma K-Means Dalam Mengelompokan Tingkat Kelarisan Barang. (Studi Kasus : Koperasi Keluarga Besar Semen. Prosiding Seminar Ilmiah Nasional Teknologi Komputer.
UNY, A. K. (2017, 07 31). Tentang Kopma UNY. Retrieved 06 Kamis, 2017, from Koperasi Mahasiswa Universitas Negeri Yogyakarta: http://www.kopmauny.com/en/profile/
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Published
2019-02-12
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