Clustering Menggunakan Algoritma K-Medoids Untuk Menentukan Strategi Promosi Sekolah Tinggi Teknologi Ronggolawe Cepu

Abstract

Efforts to create an effective and efficient marketing management strategy require a detailed and objective understanding of the market in which they operate. In analyzing this problem, the field of marketing management often overlaps the field of strategic planning. Marketing strategy consists of making decisions about the company's marketing costs, marketing mix, and marketing location. Marketing management must decide what costs need to be spent on marketing and how to allocate the entire marketing budget to various tools in the marketing mix. New student data in the Cluster using the K-medoids Algorithm method with the help of Rapid Minner, as well as knowing the level of correlation with the Davies Bouldin Index (DBI). The results of this research are from 118 data, using 2 clusters produces 0 clusters of 72 and 1 cluster of 46 and 3 clusters produce 0 clusters with 72 members, 1 cluster with 3 members and 2 clusters with 43 members. The DBI value with 2 clusters is 1.04 and 3 clusters is 1.01.