Application of the Mean-Shift Method in Grouping the Influence of Labor Market Information on Labor Absorption in West Java Province - Khaerani Ulfah; Fitrah Rumaisa (Brilliance: Research of Artificial Intelligence, 2025)
The imbalance between the number of job seekers and the availability of jobs is a challenge in the labor market in West Java Province. This study aims to group districts/cities based on the influence of labor market information on labor absorption using the Mean-Shift algorithm. Data were obtained from BPS for the 2019–2023 period, covering the number of job seekers, vacancies, and job placements. Data were processed through cleaning, transformation, normalization, and aggregation of a five-year average. Clustering was carried out using the Mean-Shift algorithm with an optimal bandwidth of 0.474611, resulting in two clusters with a Silhouette Score of 0.4943. The first cluster consists of areas with low labor absorption rates, characterized by the number of job seekers that are not comparable to vacancies and job placements. The second cluster includes areas with higher and more balanced labor absorption. The results of the study show that the Mean-Shift algorithm is able to group regions based on labor market characteristics. These findings suggest that labor market information can be used to map regions based on labor absorption rates in a more targeted manner, as well as support the formulation of data-based employment policies.