Analysis of the Decision Support System for the Selection of the Best Teaching Staff Using The AHP and Topsis Methods

  • Fajar Agung Nugroho Universitas Pamulang
Keywords: Analysis AHP Method TOPSIS Method Selection of the Best Teaching Staff Decision Support System

Abstract

The selection of the best teaching staff is very important to improve the quality of education at SMK Al - Amanah. However, the selection process carried out by SMK Al – Amanah is still subjective and less structured, so this can cause difficulties in determining the best teaching staff because of the many criteria that must be considered. In addition, the existing selection methods are not systematic and measurable, so they have the potential to cause bias and inconsistency in decision-making. This research aims to analyze and develop a decision support system that can help in the selection of the best teaching staff objectively and efficiently. The proposed solution is the development of a system that integrates the Analytical Hierarchy Process (AHP) method to determine the weight of the assessment criteria, as well as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to rank teaching staff based on this weight. The method used for collecting data on assessment criteria for the selection of the best teachers is through interviews and applying the AHP method in determining the weight of the criteria, as well as the application of the TOPSIS method for ranking in the selection of the best teaching staff. From the results of the research, it was obtained that the creation of a Decision Support System can simplify and accelerate the assessment process carried out by the leadership of each teaching staff and also the assessment of each teaching staff to be more accurate and minimize errors in making decisions.

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References

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Published
2025-08-13
How to Cite
[1]
Fajar Agung Nugroho, “Analysis of the Decision Support System for the Selection of the Best Teaching Staff Using The AHP and Topsis Methods”, JI, vol. 10, no. 2, pp. 322-330, Aug. 2025.