<![CDATA[Assessment of lecturer performance is a critical element in ensuring academic effectiveness and productivity, as well as relevance to teaching, research, and commitment to society. The study applied the Multi Attribute Utility Theory (MAUT) in the decision support system (SPK) for the selection of the best lecturers at the School of Technology. SPK helped in decision-making on semi-structured problems by using models that can combine and process different types of data. MAUT's selection is based on its ability to integrate a wide range of assessment criteria such as formal education, functional departments, certification, number of publications, author's role in research, publication history, grant fund acquisition, amount of dedication, role in devotion, scope of devotedness, active role in inter-campus ministry, and Active role in external ministry. Of the 26 lecturers assessed on the basis of 12 criteria, the system successfully identified three lecturers with the highest score, showing the objectivity and effectiveness of MAUT in performance assessment. The lecturer with code A5 scored the highest score of 0.925, followed by A14 with 0.775, and A7 with 0.702. These results provide important insights for decision-making to the leadership of the School of Technology in giving awards and guiding the career development of lecturers.]]>
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