Clustering Job Seekers in Bojonegoro Using K-Means and Fuzzy K-Means

Abstract

Background: Job seekers are part of the labor force who are unemployed and actively looking for work. One of the efforts to address the rising number of job seekers is by expanding job openings or employment opportunities. Employment is an essential need for individuals to meet various aspects of life, ranging from basic needs to education and housing. Objective: This paper aims to analyze the frequency distribution of job seeker attributes in Bojonegoro, compare the K-Means and Fuzzy K-Means methods in clustering sub-districts, determine the best clustering method, and describe frequency distribution for each formed cluster. Methods: The methods used are K-Means and Fuzzy K-Means, both known for their ease of implementation and effectiveness in clustering large datasets by minimizing the average distance between data points within each cluster. Results: The majority of job seekers in Bojonegoro in 2022 are aged 15–24, unmarried, and senior high school graduates, with males comprising 59.6% of the total. The clustering analysis, with an optimal k equal to 5, reveals five balanced groups with distinct variations in age, gender, and marital status, suggesting a range of employment needs among subgroups. Conclusion: The findings indicate that most job seekers in Bojonegoro are young, male, unmarried, and secondary school graduates. The clustering process identified five relatively even groups, with K-Means slightly outperforming Fuzzy K-Means in cluster cohesion.