CLASSTERIZATION OF TRACKING STUDY DATA ABOUT CAREER AND EMPLOYMENT OF HIGHER GRADUATES USING K-MEANS ALGORITHM
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Joko Sutrisno(1)
Universitas Budi Luhur
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Arief Wibowo(2*)
Universitas Budi Luhur
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Bayu Satria Pratama(3)
Universitas Budi Luhur
(*) Corresponding Author
Abstract
Higher education has a responsibility to produce quality graduates. One indicator of the quality of graduates is the status of getting a job, the condition of the suitability of the field of work with the educational program pursued, and the waiting period to get the job. What is being done to find out these conditions is to conduct a tracer study for graduates. This study analyzes data from a college graduate tracking study about careers and jobs using a data mining clustering algorithm, namely K-Means. The results showed that the analysis of the tracking study data formed several graduate clusters with an evaluation value of the Davies-Bouldin Index (DBI) reaching 0.287 in the first trial and 0.291 in the second trial. The clusters formed consist of groups of graduates with status still needing to be working or currently working. The profile of graduates from each cluster can be identified in the form of a relatively short waiting period of less than six months to get a first job or a relatively slow waiting period of more than one year. Another cluster specification that is formed is about the profile of graduates with the level of compatibility between the education attained and the field of work carried out. The results of this study serve as feedback for study program managers to measure the quality of graduates and the improvements in the educational process that need to be made.
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