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.

Citation Format:
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.
Fulltext View|Download
Keywords: IKM; Mixed-Type Data; Numerical-Typed Data, Categorical-Type Data); Cluster Analysis; k-Prototypes Clustering; Silhouette Index.

Article Metrics:

Article Info
Section: Articles
Language : ID
Recent articles
PERBANDINGAN METODE LVQ DAN BACKPROPAGATION UNTUK KLASIFIKASI STATUS GIZI ANAK DI KECAMATAN SANGKUP PEMODELAN DATA LONGITUDINAL MENGGUNAKAN REGRESI POLINOMIAL LOKAL PADA KELOMPOK SAHAM PERUSAHAAN PENYEDIA JASA TELEKOMUNIKASI DENGAN GUI R PEMODELAN ANTAR VARIABEL EKONOMI SECARA SIMULTAN MENGGUNAKAN PENDEKATAN VECTOR ERROR CORRECTION MODEL (VECM) More recent articles
  1. Aschenbruck, R., dan Szepannek, G. 2020. Cluster Validation for Mixed-Type Data. https://doi.org/10.5445/KSP/1000098011/02
  2. Badan Pusat Statistik Provinsi Sulawesi Tenggara. 2020. Profil Industri Mikro dan Kecil Sulawesi Tenggara Tahun 2020. Kendari: Badan Pusat Statistik Provinsi Sulawesi Tenggara
  3. Gujarati, D. N. 2004. Basic Econometrics (4th Ed.). Singapore: McGraw Hill Inc
  4. Hair, J. F., Black, W. C., Babin, B. J., dan Anderson, R. E. 2010. Multivariate Data Analysis (7th Ed.). New York: Pearson Prentice Hall
  5. Han, J., Kamber, M., dan Pei, J. 2012. Data Mining: Concepts and Techniques (3rd Editio). Waltham: Morgan Kaufmann Publishers
  6. Huang, Z. 1997. Clustering Large Data Sets With Mixed Numeric and Categorical Values. In The First Pacific-Asia Conference on Knowledge Discovery and Data Mining, 21–34
  7. Huang, Z. 1998. Extensions to the k -Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery 2, 2(3), 283–304
  8. Johnson, R., dan Wichern, D. W. 2007. Applied Multivariate Statistical Analysis (6th Ed.). New Jersey: Pearson Prentice Hall
  9. Kaufman, L., dan Rousseeuw, P. J. 2005. Finding Groups in Data: An Introduction to Cluster Analysis. New Jersey: John Wiley & Sons
  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

Last update:

No citation recorded.

Last update:

No citation recorded.