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Lubis, Muhammad Taufik Hakim
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering

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Measurement of Centroid Distance in Determining Stunting Clusters Lubis, Muhammad Taufik Hakim; Hasibuan, Muhammad Siddik
Journal La Multiapp Vol. 5 No. 3 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i3.1479

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.
Co-Authors Muhammad Siddik Hasibuan