Decision Support System for Permanent Lecturer Recruitment at STMIK Mulia Darma Using the Simple Additive Weighting (SAW) Method - Muhammad Halmi Dar; Deci Irmayani; Elvitrianim Purba; Muhammad Sayuthi; Indra Sidabutar (Pascal: Journal of Computer Science and Informatics, 2026)
Selecting qualified academic personnel is critical for higher education institutions to maintain academic excellence and accreditation standards. At STMIK Mulia Darma, the recruitment process for permanent lecturers historically faced challenges related to multi-criteria evaluation complexity, administrative delays, and potential subjective bias when handled manually. This study aims to develop and implement a web-based Decision Support System (DSS) utilizing the Simple Additive Weighting (SAW) method to streamline and objective candidate screening. The evaluation incorporates five key criteria: Educational Background C1, Microteaching Score C2, Publication and Research Record C3, Interview Performance C4, and Expected Salary C5. Through matrix normalization and weighted aggregation, the system calculates final preference scores to rank applicants. In empirical trials, Candidate A3 achieved the highest preference score V3 = 0.940, demonstrating the method's effectiveness in balancing academic competencies against cost parameters. System testing via Black-Box Testing yielded a 100% functional success rate, while algorithmic accuracy verification confirmed 100% alignment between automated system outputs and manual calculations. Ultimately, the developed system effectively minimizes subjective bias, enhances decision-making efficiency, and provides a reliable framework for faculty recruitment at STMIK Mulia Darma.