Clustering Pencari Kerja Menggunakan K-Means
DOI:
https://doi.org/10.54367/kakifikom.v8i2.6986Keywords:
K-Means, Clustering, Job Seekers, Education, Data MiningAbstract
Purpose: This study aims to cluster job seekers based on educational background at the Pematangsiantar City Manpower Office using the K-Means algorithm. Design/methods/approach: A quantitative data mining approach was applied to 1,491 valid records after selection, cleaning, transformation, encoding, and Min-Max normalization. The main variables were education level, age, and graduation duration. K-Means was implemented with k=3 using Euclidean Distance and Python in Google Colaboratory. Findings/results: The clustering process formed three groups: C1 work-ready with 231 records, C2 job seekers requiring training with 1,035 records, and C3 difficult-to-absorb job seekers with 225 records. Conclusions: K-Means can support data-based segmentation of job seekers and help the Manpower Office design job placement, training, and special assistance programs more precisely.References
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