Measurement of Centroid Distance in Determining Stunting Clusters

Muhammad Taufik Hakim Lubis; Muhammad Siddik Hasibuan

doi:10.37899/journallamultiapp.v5i3.1479

Muhammad Taufik Hakim Lubis Ilmu Komputer, Fakultas Sains dan Teknologi, Universitas Islam Negeri Sumatera Utara, Medan Indonesia
Muhammad Siddik Hasibuan Ilmu Komputer, Fakultas Sains dan Teknologi, Universitas Islam Negeri Sumatera Utara, Medan Indonesia

DOI: https://doi.org/10.37899/journallamultiapp.v5i3.1479

Keywords: Stunting, K-Means, Clustering, Euclidean Distance, Manhattan Distance, Sum of Squared Errors, Mean Squared Error

Abstract

This study evaluates the effectiveness of distance measurement methods in the K-Means clustering algorithm for determining stunting clusters by comparing Euclidean and Manhattan distances. The goal is to obtain optimal cluster centroids and the closest distances within each cluster. The study uses a sample of 552 records with 3 attributes. The process begins with applying the K-Means algorithm, followed by distance measurement using Euclidean and Manhattan methods. Iterations are performed until optimal results are achieved. Evaluation is conducted using Sum of Squared Errors (SSE) to assess the total error within clusters and Mean Squared Error (MSE) to calculate the average nearest distance within clusters. The results indicate that both SSE and MSE methods are effective in identifying cluster quality and provide insights into the accuracy and effectiveness of Euclidean and Manhattan methods in clustering.

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