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PENERAPAN DATA MINING DENGAN MENGGUNAKAN K-MEANS UNTUK MENDUKUNG STRATEGI PEMASARAN DI BAGIAN MARKE
Titi Wahyuni ; Hendra Marcos
STMIK Amikom Purwokerto is a private university located in Navan, which is categorized as still new, because since its establishment in 2005 until 2016 Amikom to ± 4,500 students admitted to Amikom. The author has been analyzing the data registries last 5 years, in terms of the data over the past 5 years, for a percentage of the amount of data of new students each year who go to STMIK Amikom Purwokerto, either the registration or registration through a phase of rise and decline every year, so the need for a strategy to maintain the percentage of registrants and registration can always be increased for each year, conducted marketing strategy will determine the number of applicants due to the increasing level of competition between institutions. This study aims to perform data grouping students STMIK Amikom Purwokerto by utilizing data mining process by using clustering techniques. The algorithm used is the k-means algorithm. K-means clustering is one method that can group students' data into several clusters based on the similarity of the data, so that the student data that have the same characteristics are grouped into one cluster and that have different characteristics grouped in another cluster. Implementation using RapidMiner Studio 7.3.1. The attribute used is the district, home schools, majors, study program and resources. Clusters of students formed is three cluster, the first cluster 143 students with the results for the distribution of the district is dominated Banyumas, and media information are brochures, the second cluster 159 students and a third cluster number of 429 students gave similar results for the distribution of the district is dominated Banyumas and media information were brochures. Results from this study was used as a basis for a decision to determine campaign strategy based on clusters formed by the marketing Amikom.

Keyword : Promotion Strategies ; Data Mining ; Algorithm K-Means