K-Means Clustering in Relevance Grouping of Undergraduate Informatics Jobs: Case Study at the Informatics Engineering Department, Universitas Muhammadiyah Malang, Malang, Indonesia - Dikky Cahyo Hariyanto; Sri Harini; Totok Chamidy (Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia, 2024)
Higher education is one of the levels of study expected to produce graduates competent in the field of knowledge taken. The large number of graduates from tertiary institutions with many job opportunities causes many graduates to work in ways that do not match their majors, so there is a need to evaluate the level of success of graduates learning achievements in tertiary institutions. This research aims to analyze data on the relevance of the work of undergraduate graduates in Informatics Engineering to what they have learned by the learning outcomes in the Informatics Engineering study program at the University of Muhammadiyah Malang using K-Means clustering. Using data from questionnaires measuring graduate learning outcomes and measuring job suitability for 137 respondents who had been tested for validity, reliability, and multicollinearity, the results of this research showed that the data was formed into three clusters with the analysis that 29.92% of UMM Informatics Engineering graduates were able to meet graduate learning outcomes and obtain jobs that are relevant to what they studied, 49.63% of other graduates also got jobs that were relevant to their major even though they lacked mastery of specific skills as measured by graduate learning outcomes, and 20.45% of other graduates got jobs that were less relevant to the field of Informatics engineering.