Penerapan Data Mining Untuk Pengelompokan Pencari Kerja Dengan Menggunakan Metode Clustering K-Means
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Fanny Fadilah
*
STMIK Kaputama, Binjai,
Indonesia
(*) Corresponding Author
Abstract
A job seeker is someone who is unemployed and looking for work both at home and abroad. Therefore, job introductions and inter-work officers are required to explore the competencies possessed by job seekers who have registered with the local Manpower Office with the aim of making it easier to match job vacancies for job seekers so that the placement of prospective workers is in accordance with their talents, interests. and abilities of job seekers. This study aims to build a system for grouping registered job seekers based on variables determined using the clustering method. Data mining is a semi-automatic process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify potential and useful knowledge information stored in large databases. The author uses the clustering method because this method can group a set of data objects into several groups or clusters so that objects in a cluster have a high number of similarities, but are very different from objects in other clusters.
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