DOI: https://doi.org/10.14710/j.gauss.12.3.340-351

CLUSTERING KARAKTERISTIK INDUSTRI KECIL DAN MENENGAH DI KOTA KENDARI MENGGUNAKAN ALGORITMA k-PROTOTYPES

*Khalifah Nadya Reihanah  -  Department of Statistics, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia
Di Asih I Maruddani  -  Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia
Tatik Widiharih  -  Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia
Open Access Copyright 2023 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Industri Kecil Menengah (IKM) have important roles in economic development. The large number of IKM cannot be separated from various problems. The basic problems faced by IKM in Kendari are limited capital, inadequate human resources, difficulty in obtaining raw materials, and the Indonesian economy which has slumped due to the impact of the COVID-19 pandemic. This research was conducted with the aim of classifying the characteristics of the IKM with the optimal number of clusters. The method used is k-Prototypes Clustering with values of k = 2, 3, 4, ..., and 10. The k-Prototypes method is a clustering method that maintains the efficiency of the k-Means algorithm in handling large data when compared to the hierarchical clustering method. This method can group mixed type data (consisting of numeric type data and categorical type data). Based on the analysis, the optimal number of clusters is five clusters, with a Silhouette Index value of 0.461. Cluster 5 is the best IKM cluster with the highest average number of workers and the highest average investment value, while cluster 2 has the lowest average investment value and IKM in this cluster is relatively new compared to IKM in other clusters.
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Keywords: IKM; Mixed-Type Data; Numerical-Typed Data, Categorical-Type Data); Cluster Analysis; k-Prototypes Clustering; Silhouette Index.

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  10. Nisbet, R., Elder, J., dan Miner, G. 2009. Handbook of Statistical Analysis and Data Mining Applications. Academic Press
  11. Salkind, N. J. 2007. Encyclopedia of Measurement and Statistics. California: SAGE Publications
  12. Kaufman, L., & Rousseeuw, P. J. (2005). Finding Groups in Data: An Introduction to Cluster Analysis. New Jersey: John Wiley & Sons
  13. Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of Statistical Analysis and Data Mining Applications. Academic Press
  14. Salkind, N. J. (2007). Encyclopedia of Measurement and Statistics. California: SAGE Publications

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