Analisis Komparatif Metode MOORA dan MAUT untuk Rekomendasi Pengangkatan Tenaga Pendidik - Oktavia, Petricia; Frindo, Muhamad Meky (TIN: Terapan Informatika Nusantara, 2025)
The objective and data-driven recruitment of educators is a strategic step toward improving the quality of education. This study aims to develop a recommendation system for selecting prospective educators by comparing two multi-criteria decision-making methods: MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) and MAUT (Multi-Attribute Utility Theory). Five key criteria were used in the evaluation: Grade Point Average (GPA), mastery of didactic and methodological knowledge, teaching experience, age, and distance from home to school. Primary data were collected directly from educational institutions and prospective educators, involving six alternatives that were analyzed through normalization, weighting, and final score calculation. The analysis showed that the MOORA method identified candidate A2 as the best with a score of 0.3143, while the MAUT method ranked candidate A6 highest with a score of 0.644. The difference in rankings stems from the distinct evaluation principles of the two methods: MOORA relies on normalized ratios relative to the maximum value, while MAUT applies an aggregated utility approach. Despite this variation, both methods consistently identified the top three candidates. The developed recommendation system not only enhances transparency and accountability but also outperforms conventional intuition-based approaches by providing a structured, measurable, and replicable framework. This system has the potential to be adopted by educational institutions to ensure a fairer and more objective educator recruitment process.