K-Means Clustering Based on Expertise on Applicant Data at Vocational High School Job Fairs Using the R Language Program

This research discusses the implementation of the K-Means Clustering method to group applicant data based on their expertise at the job fair in Vocational High Schools (SMK). The purpose of this research is to assist the job fair agenda in grouping applicants effectively so that the selection and job placement process can be carried out more efficiently. The clustering process is performed using the R program, which offers various statistical and data visualization functions to support the analysis. The applicant dataset was analyzed by considering variables such as field of expertise, basic skills, soft skills and academic ability. The results of this study show that the application of K-Means Clustering on job applicant data of SMK graduates can identify several groups with different characteristics. After conducting the analysis, it was found that there are three (3) main clusters formed based on the applicants' skills and job preferences. The first cluster consists of applicants who have high technical expertise, such as Light Vehicle Engineering, while the second cluster is dominated by applicants who have expertise in Computer, namely Network and Computer Engineering majors. The third cluster includes applicants who have an educational background in Multimedia, these results indicate a significant variation in expertise and preferences in the industrial world, which can help the industry in recruiting SMK graduates as needed.