Development Of Smart Lecturer Assessment SPK Using Saw Based On Student Evaluation Data

Abstract

Lecturer performance assessment is a crucial effort in ensuring the quality of higher education. However, the processing of student evaluation data is often still performed manually, a time-consuming approach that is prone to subjectivity and lacks effectiveness in generating objective decisions. This study aims to develop an intelligent Decision Support System (DSS) to identify the top-performing lecturer based on student evaluation data using the Simple Additive Weighting (SAW) method. The SAW method was selected for its ability to conduct multi-criteria assessment through weighting, value normalization, and preference value calculation, thereby producing an objective ranking of alternatives. The study employs seven assessment criteria: mastery of subject matter, ability to convey material, discipline, interaction and communication, teaching methods, assessment objectivity, and the ability to motivate students. Data regarding 15 lecturer alternatives were processed through stages involving criteria selection, weighting, decision matrix construction, normalization, and preference value calculation. The results demonstrate that the SAW method successfully generates a ranking of alternatives in a systematic, transparent, and measurable manner. Alternative V10 achieved the highest preference score of 0.90 and was consequently recommended as the best lecturer. The findings confirm that applying the SAW method enhances objectivity and efficiency in the lecturer assessment process and supports more accurate decision-making within the higher education environment.