Implementation of K-Means Method with Purity Clustering as Centroid Center Point in MSME Data Grouping

Ikhsan Maulana, Lisna Zahrotun

Abstract


Micro, Small, and Medium Enterprises (MSMEs) have a strategic role in driving economic growth, especially in Yogyakarta City. Although the local government has made various efforts to improve and promote local MSME products, constraints in data clustering are still an obstacle in the effective decisionmaking process. This research aims to produce an accurate and efficient clustering model to support the development of MSMEs. The method used is the K-Means algorithm with the Clustering Purity approach as the centroid center point determinant. Evaluation is done using the Davies-Bouldin Index (DBI) as an indicator of cluster validity. The results showed that the method successfully formed two main clusters. The first cluster consists of 186 MSMEs with wide marketing coverage and utilization of digital media such as WhatsApp, Instagram, and Shopee. The second cluster includes 1,150 MSMEs that mostly operate locally and 66% have turnover below IDR10 million and rely on WhatsApp and Facebook as their marketing media. This method produces a DBI value of 1.367 and an execution time of 1.21 seconds, showing good performance in clustering MSME data.

Keywords


K-Means Algorithm; Clustering; Purity; MSME; Data Mining; Davies Bouldin Index (DBI)

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References


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DOI: https://doi.org/10.29100/jipi.v11i2.7898

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