Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi


  • Irmanita Nasution * Mail STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Agus Perdana Windarto STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • M Fauzan STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • (*) Corresponding Author
Keywords: Proverty; Data Mining; K-Means; RapidMiner; Clustering

Abstract

Proverty is one of the problems that inhibits national and regional growth. This research uses data mining techniques. In this study tha data used were sourced from the 2012-2018 statistical center. The research uses data mining techniques. In the data processing using k-means method. K-means method is a method of grouping existing data into several groups where the data in one group has the same characteristics with each other and has different characteristics from the data in other groups. The number of records used is 34 provinces which are divided into 2 clusters namely high and low clusters. The purpose of this study is divided into 2 parts, namely the provincial group with a high proverty rate and the provincial group with the lowest proverty level. From the result of grouping there were 8 provinces of high cluster and 26 low clusters. It is hoped that this research can provide input to the government so that it can give more attention to provinces that are categorized as high in proverty

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Article History
Submitted: 2020-10-15 Published: 2020-12-10
How to Cite
Nasution, I., Windarto, A., & Fauzan, M. (2020). Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi. Building of Informatics, Technology and Science (BITS), 2(2), 76-83. https://doi.org/10.47065/bits.v2i2.492
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