Algoritma K-Means Dengan Menggunakan Metode Pengukuran Jarak dan Densitas Untuk Peningkatan Strategi Promosi Penerimaan Mahasiswa Baru (PMB)

riana safitri

riana safitri STMIK Widya Utama

Abstract

In a company the activity of promoting a product or service is the most needed aspect to support success, so promotion is one way for companies or organizations to develop, maintain, and advance the business that is being managed. In order for the promotion to be carried out in accordance with the expected target, it is necessary to establish market segmentation in which there are many targeted targets, because the targeted target is very dispersed and scattered and varied in the demands of their needs and desires. Data mining is an activity in the database process to find certain previously unknown / hidden patterns that are useful and meaningful and can be processed. One algorithm that can be used for clustering is the k-means algorithm. In order to maximize the k-means algorithm, we use distance and density measurement methods. Grouping data using k-means is done by calculating the closest distance from a data to a centroid point. In this study, the method of calculating the distance on k-means between Euclidean and density will be combined. The test will be carried out using the execution time and the bouldin index davies. From the tests that have been done, the calculation of eulidean distance and density has the most efficient accumulation of time and the value of Davies Index.

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