PCA-counseled k-means and k-medoids with dimension reduction for improved in determining optimal aid clustering

Authors

  • Achmad Jauhari University of Trunojoyo Madura, Indonesia
  • Ika Oktavia Suzanti University of Trunojoyo Madura, Indonesia
  • Devie Rosa Anamisa University of Trunojoyo Madura, Indonesia
  • Fadhila Tangguh Admojo Universiti kuala lumpur, Malaysia

DOI:

https://doi.org/10.21107/kursor.v13i1.460

Keywords:

Aid data, Clustering, K-Means, K-Medoids, Principal Component Analysis

Abstract

Assuring effective allocation requires targeted distribution of aid, which makes aid clustering a crucial component. For the purpose of using data-driven segmentation based on important characteristics to determine effective help targeting, accuracy in clustering is essential. The study explores the combination of Principal ComponentAnalysis (PCA), k-means, and k-medoids to enhance aid clusters, with the goal ofincreasing aid distribution accuracy and efficiency. The information gathered consists of 1600 records with 13 attributes. In order to standardized the data having two processes in it, preprocessing is first applied. When used with PCA, it makes measuring variance easier and preserves 80% of the variation by choosing five components. Thenumber of clusters may be determined with the use of PCA, k-medoids, and the k-means approach. Greater PCA-k-means silhouette coefficients, which indicate betterclustering ability, are highlighted by comparative analysis. This analysis shows thatPCA-k-means is an effective technique for creating accurate and unique clusters withina data set's structure.The clustering results using the PCA-k-means algorithm have produced the greatest accuracy in the silhouette score of 0.49 and the DBI score is 0.84.

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Published

2025-07-30

Issue

Vol. 13 No. 1 (2025)

Section

Articles

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Copyright (c) 2025 Achmad Jauhari, Ika Oktavia Suzanti, Devie Rosa Anamisa, Fadhila Tangguh Admojo

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