Implementasi Data Mining untuk Clustering Lowongan Pekerjaan Menggunakan Metode Algoritma K-Means
DOI:
https://doi.org/10.35870/jtik.v9i2.3438Keywords:
Data Mining, Job Vacancies, K-Means, Clustering, CRISP-DMAbstract
The development of digital technology has transformed the way businesses recruit employees online. This study aims to create an interactive dashboard that facilitates job seekers and companies, using clustering methods with the K-Means algorithm to analyze job posting data in the United States. The data from the Kaggle LinkedIn Job Postings 2023 dataset, consisting of 33,000 records, is processed using the CRISP-DM phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The clustering analysis results in four job categories: low-mid-level general jobs, high-level executive jobs, time-based jobs, and mid-high-level professional jobs. Model evaluation shows good clustering quality with a Silhouette Coefficient of 0.78 and a Davies-Bouldin Index of 0.55. The developed dashboard helps companies plan recruitment and job seekers find positions matching their skills and salary expectations. The practical contribution of this study is modernizing the recruitment process, assisting companies and recruitment agencies in screening candidates more efficiently, and improving job matching through deeper data analysis.
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