Implementasi Metode MAUT dalam Analisis Penentuan Tenaga Pengajar Non ASN Terbaik - Maulana, Imam; Irmayani, Deci; Suryadi, Sudi (Building of Informatics, Technology and Science (BITS), 2025)
The need for quality teaching staff is becoming increasingly important along with the development of technology and globalization, including in educational institutions such as SDN 115467 Kanopan Ulu. In addition to teaching staff from ASN, this school also relies on non-ASN staff who play a significant role in supporting the quality of education. However, the process of determining the best non-ASN teaching staff is often faced with the challenges of subjectivity and differences in assessment standards. To overcome this, this study proposes the implementation of a Decision Support System (DSS) based on the Multi Attribute Utility Theory (MAUT) method. The MAUT method allows for more objective, transparent, and fair decision-making by considering various assessment criteria, such as competence, experience, and contribution of teaching staff. In this study, non-ASN teaching staff data were analyzed using the Microsoft Excel application and DSS software during the research period in October 2024. Based on the application of this method, Tuti Alawiyah (A15) was ranked first with the highest score, namely 0.731. These results indicate that Tuti Alawiyah has the best performance according to the criteria used in the MAUT method, reflecting her superiority over other candidates. The results of the study indicate that the MAUT method is able to provide accurate and consistent evaluation results, thus supporting a more rational and in-depth decision-making process. This study not only provides theoretical contributions to the development of the DSS system, but also provides practical benefits for educational institutions to improve the motivation of non-ASN teaching staff and, overall, the quality of education. This topic is relevant to the needs of modern education in Indonesia, especially in efforts to improve the transparency and accuracy of teaching staff assessments.