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All Journal International Journal of Informatics and Data Science
Muhammad khoiruddin Harahap
Politeknik Ganesha, Medan
Computer Science & IT

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Comparative Evaluation of Cloud Service Providers for Enterprise IT Migration using CRITIC-CoCoSo and Entropy-TOPSIS Asyahri Hadi Nasyuha; Ananda Hadi Elyas; Muhammad khoiruddin Harahap
International Journal of Informatics and Data Science Vol. 3 No. 1 (2025): December 2025
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v3i1.569

Abstract

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 p = 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.
Beyond Vendor Hype: An Objective IDOCRIW-MARCOS Model for Selecting AI-Based Workforce Mental Health Platforms Asyahri Hadi Nasyuha; Ananda Hadi Elyas; Muhammad Khoiruddin Harahap
International Journal of Informatics and Data Science Vol. 3 No. 2 (2026): June 2026
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v3i2.570

Abstract

The growing adoption of artificial intelligence (AI) in workforce mental health monitoring has produced a wide range of competing platforms that differ in prediction accuracy, response time, scalability, implementation cost, and user satisfaction, so that HR and occupational-health decision-makers currently choose among them largely on the basis of vendor marketing claims rather than a structured, evidence-based comparison, making platform selection a genuine multi-criteria decision-making (MCDM) problem. This study proposes a decision support model that integrates the Integrated Determination of Objective Criteria Weights (IDOCRIW) method with the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method to give organizations an objective, reproducible alternative to subjective AHP/TOPSIS-style vendor evaluations for AI-based workforce mental health platforms. IDOCRIW combines Entropy and Criterion Impact Loss (CILOS) to derive objective criteria weights directly from vendor-reported technical specifications, removing the need for expert pairwise comparisons, while MARCOS ranks alternatives based on their utility degree relative to an ideal and an anti-ideal solution. The proposed model was demonstrated using an illustrative case study of five AI platform alternatives evaluated against five criteria: prediction accuracy, response time, scalability, implementation cost, and user satisfaction. The IDOCRIW results indicate that scalability (weight 0.260) is the most influential criterion, followed by response time (0.220). The MARCOS ranking identifies SmartWell as the top-ranked alternative with a final utility function value of 0.981, ahead of MentalCare AI (0.973) and WorkSense AI (0.969); a follow-up TOPSIS comparison computed on the same weighted data confirms SmartWell's top position but reverses the third- and fourth-ranked alternatives, showing that the ranking method itself, and not only the criteria weights, materially affects the recommendation. A sensitivity analysis, including combined-criterion perturbation scenarios, confirms that the ranking of the top alternative remains stable under moderate weight changes. For HR practitioners, the model offers a transparent and reproducible basis for AI-vendor procurement decisions that reduces reliance on unverified accuracy claims in vendor marketing materials.
Co-Authors Asyahri Hadi Nasyuha Elyas, Ananda Hadi