Development of a Web-Based Decision Support System for the Selection of Computer Laboratory Assistant Candidates Using Grey Relational Analysis

Abstract

Selecting computer laboratory assistant candidates is a multi-criteria decision problem because the decision must simultaneously consider academic performance, subject mastery, technical competence, interpersonal communication, and teaching ability. This study aims to develop a web-based Decision Support System using Grey Relational Analysis (GRA) to produce an objective, transparent, and traceable candidate ranking. The study applies a quantitative-computational approach and prototype development. Five criteria are included: grade point average with a weight of 0.20, relevant course grade 0.25, competency or technical test 0.25, interview 0.15, and microteaching 0.15. The analysis consists of decision matrix construction, normalization, ideal reference sequence determination, deviation calculation, Grey Relational Coefficient, Grey Relational Grade, ranking, and sensitivity analysis of the distinguishing coefficient. The final ranking places A3 first with a GRG of 0.7536, followed by A5 at 0.7086, A1 at 0.6518, A2 at 0.6371, and A4 at 0.4333. A3 is the main recommendation because it has the highest weighted closeness to the ideal profile. The sensitivity analysis reported in the source manuscript indicates that A3 remains in first place when the distinguishing coefficient changes, suggesting that the primary recommendation is stable. The study contributes an integrated decision model that combines academic, technical, interpersonal, and pedagogical dimensions and a web-based system design that supports decision traceability. A complete numerical audit still requires the raw decision matrix and system calculation logs.