Pengelompokan Kabupaten/Kota Di Jawa Barat Berdasarkan Indikator Ketenagakerjaan Menggunakan Metode K-Means Dan Fuzzy C-Means Dengan Evaluasi Rasio SW/SB Sebagai Validasi Klaster

Abstract

Background: The high dynamics of the labor market in Indonesia make employment one of the main challenges of sustainable development. Changes in economic structure, an increase in the workforce, and regional disparities in employment opportunities require adaptive strategies. This condition is particularly evident in densely populated areas with diverse economic sectors, such as West Java Province. Objective: This study aims to group 27 districts/cities in West Java Province based on five employment indicators, namely TPAK, TPT, the number of workers in micro and small businesses, the average monthly net income of informal workers according to education, and the percentage of employment in the workforce by comparing the K-Means and Fuzzy C-Means methods. Methods: This study uses two clustering methods, namely K-Means and Fuzzy C-Means. The best method is selected by comparing the SW and SB values. Results: The results indicate that the K-Means method is the best method, as seen from the smaller Sw/Sb ratio of 0.076, compared to Fuzzy C-Means, which is 0.153. Conclusion: The results of the K-Means method clustering show that the districts/cities in West Java are divided into 7 clusters, namely cluster 1 with 3 regions, cluster 2 with 8 regions, clusters 3 and 4 with 1 region, cluster 5 with 4 regions, cluster 6 with 3 regions, and cluster 7 with 7 regions. These results can serve as a basis for determining the priority of regional employment interventions.