ANALISIS POLA DAN PEMETAAN PROFESI DI KOTA BATAM MENGGUNAKAN K-MEANS CLUSTERING DAN FP-GROWTH

Abstract

This study explores the structure of job markets and professional distributions in Batam City by leveraging online job vacancy data as a source of digital labor market information. The research is motivated by the limited availability of systematic insights into how professions and skill requirements are distributed across different areas, which often leads to inefficiencies in job matching and workforce planning. The objective of this study is to analyze professional groupings and identify recurring skill patterns using data mining techniques. Professional clustering was conducted using the K-Means algorithm based on similarities in job titles and job descriptions, while the FP-Growth algorithm was utilized to uncover frequent combinations of skills appearing within the same job postings. The results indicate that employment opportunities in Batam are segmented into multiple distinct clusters corresponding to major employment sectors, each associated with characteristic sets of skills. By integrating clustering results with association rule analysis, this study generates a digital mapping of professions that illustrates both occupational groupings and underlying skill demands, offering valuable insights for data-driven labor market analysis and workforce development.