Decision Support System for the Selection of Outstanding Students Using the MAUT Method

2026-08-15T09:50:41+00:00

The selection of outstanding students is one of the efforts of universities in giving awards to students who have the best academic and non-academic achievements. The problem that often occurs in the selection process of outstanding students is that assessments still focus on one main criterion, namely the Cumulative Achievement Index (GPA), so that the results obtained are less objective and do not reflect the overall ability of students. Therefore, this study aims to design and implement a Decision Support System (DSS) in the selection of outstanding students by considering various assessment criteria. The method used in this study is Multi Attribute Utility Theory (MAUT), which is one of the multicriteria decision-making methods. The criteria used in the study included GPA, foreign language skills, organizational activeness, number of scientific papers, and history of taking remedial exams. The MAUT method is used to calculate the utility value of each alternative based on the weight and value of each criterion, so that student rankings are obtained objectively. The results of the study show that the implementation of DSS using the MAUT method can help the selection process of outstanding students in a more systematic and transparent manner. Based on the calculation results, the best alternative was obtained in alternative A5 on behalf of "Loli" students with a final utility value (Ui) of 2.2196, so that it was designated as an outstanding student. The resulting system is expected to be a supporting solution in making decisions about the selection of outstanding students in the university environment.

Comparative Evaluation of Cloud Service Providers for Enterprise IT Migration using CRITIC–CoCoSo and Entropy?TOPSIS

2026-08-15T11:44:20+00:00

The growing number of public cloud computing service providers offering overlapping but non-identical combinations of availability, cost, security, scalability, technical support, and deployment speed has turned cloud provider selection into a genuine multi-criteria decision-making (MCDM) problem for organizations planning enterprise IT infrastructure migration. This study proposes a comparative decision support model that integrates Criteria Importance Through Intercriteria Correlation (CRITIC) for objective criteria weighting with the Combined Compromise Solution (CoCoSo) method for alternative ranking, and validates the resulting recommendation against an independent Entropy–TOPSIS pipeline. Five major cloud providers, AWS, Microsoft Azure, Google Cloud, Oracle Cloud, and Alibaba Cloud, were evaluated against six criteria: availability, monthly cost, security features, scalability, technical support, and deployment time. The CRITIC method identified security features (weight 0.203) as the most influential criterion, while CoCoSo ranked Google Cloud first with a score of 2.874, followed by AWS and Microsoft Azure. The independent Entropy–TOPSIS validation produced an identical ranking, with Google Cloud obtaining the highest closeness coefficient (0.864). A Spearman rank-order correlation of ? = 1.000 between the two independent method pairs confirms full ranking consistency, indicating that the recommendation is robust to the choice of weighting and ranking technique within this illustrative case; because the underlying decision matrix is illustrative rather than independently audited vendor data, this consistency demonstrates the robustness of the method rather than a certified procurement recommendation. The proposed CRITIC–CoCoSo model, cross-validated with Entropy–TOPSIS, offers a transparent and reproducible framework for evidence-based cloud service provider selection.

International Journal of Informatics and Data Science

Decision Support System for the Selection of Outstanding Students Using the MAUT Method

Comparative Evaluation of Cloud Service Providers for Enterprise IT Migration using CRITIC–CoCoSo and Entropy?TOPSIS