Optimalisasi Model Machine Learning Dalam Upaya Penyesuaian Kompetensi Tenaga Kerja Indonesia terhadap Tuntutan Pasar Kerja Digital di Era Peralihan Revolusi Industri 4.0 Menuju Society 5.0
Abstract
The transition from the Fourth Industrial Revolution (Industry 4.0) to Society 5.0 has significantly shifted the skills demanded in the labor market, urging Indonesian workers to adapt to more relevant digital competencies. This study aims to develop an optimized machine learning model to map the skills gap between job seekers and the demands of the digital labor market. Clustering using the K-Means algorithm was applied to group applicants based on demographic profiles and skills, followed by an analysis of skill gaps in each cluster. The results identified two primary clusters: experienced applicants needing reskilling and younger applicants requiring upskilling. Training recommendations were formulated based on the most in-demand skills not widely possessed by applicants, such as JavaScript, Django, and UI/UX. These findings serve as a foundation for formulating more precise, adaptive, and data-driven digital human capital development policies.
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