Algoritma Clustering K-Nearest Neighbor Dalam Pengelompokan Masyarakat Kecamatan Medan Area Berdasarkan Tingkat Ekonomi Keluarga

 (*)Rizky Meliani Astri Hasibuan Mail (Universitas Budi Darma, Medan, Indonesia)
 Efori Bu’ulolo (Universitas Budi Darma, Medan, Indonesia)
 Sumiaty Adelina Hutabarat (Universitas Budi Darma, Medan, Indonesia)

(*) Corresponding Author

Abstract

An economy that is constantly changing and developing may result in increased economic difficulties. Current economic developments have decreased to 4.79, causing poverty in Indonesia to increase. To see the condition of the people's economy, it is necessary to collect detailed data so that people with a lower economy get assistance. The problems faced in this study are data collection and classification of the economic level of the population. The process is not very efficient because it is done manually. Classification procedures will only be carried out when necessary. Controlling the welfare of the population has not been carried out in detail. To overcome these problems, we need a system so that the grouping of people who have a classification from high to low economic level. This study uses the K-NN Clustring Algorithm which requires information training to classify objects that are very close. In the last process the Clustring K-NN algorithm is a method for calculating the ratio of old data to updated information. As well as looking for the highest data occurrence value that will be used as a reference as a result.

Keywords


Data Mining; Grouping; Poverty; K-Nearest Neighbor

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Copyright (c) 2022 Rizky Meliani Astri Hasibuan, Efori Bu’ulolo, Sumiaty Adelina Hutabarat

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