<![CDATA[This study aims to implement the Weighted Product (WP) method in a Decision Support System (DSS) to evaluate and determine lecturer performance objectively, systematically, and measurably. Lecturer performance evaluation is an essential component of higher education quality assurance to maintain the quality of teaching, research, and lecturers' contributions to their institutions. The WP method was selected because it supports multi-criteria decision-making by assigning weights to each evaluation criterion according to its level of importance. This study employs five evaluation criteria: Functional Academic Credit Score (PAK), attendance, JAD, supervision assessment, and student evaluation. The implementation process includes collecting lecturer performance data, determining the weights of each criterion, and calculating the final scores using the WP method. The system was developed as a web-based application using HTML, CSS, JavaScript, Bootstrap, Chart.js, and MySQL. System testing was conducted to evaluate its ability to produce accurate, objective, consistent, and balanced assessment results. The findings indicate that the WP method provides measurable information regarding lecturer performance and effectively supports the decision-making process. Based on the evaluation of ten lecturers, Andri Fahmi achieved the highest V vector value of 0.102. This study is expected to serve as a reference for higher education institutions in developing transparent, fair, and effective Decision Support Systems to support lecturer performance evaluation and career development.]]>
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