结合灰色关联分析(GRA)和简化支点成对相对标准重要性评估(PIPRECIA-S)确定最佳员工

Setiawansyah Setiawansyah, Sanriomi Sintaro, Very Hendra Saputra, A. A. Aldino
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引用次数: 0

摘要

遴选最佳工作人员的问题往往涉及复杂的挑战,如难以找到表现良好的候选人。选拔优秀员工所面临的问题仅仅是基于对工作人员所执行的纪律和工作效率的评估,因此评估过程中没有使用被认为对选拔优秀员工很重要的标准方面。 本研究旨在根据预先确定的标准确定最佳员工,在确定最佳员工的选择时使用灰色关系分析(GRA)决策模型,在确定标准权重时使用简化支点成对相对标准重要性评估(PIPRECIA-S)模型,这样得出的标准权重就不会以决策者的假设为基础。使用灰色关系分析法和简化支点成对相对标准重要性评估加权法得出的最佳员工评 估排名结果,即 Denis Irawan 获得的排名 1,最终灰色关系分析值为 0.243014。对 TRITAM 模型最佳人员选拔应用测试的数据处理结果进行了调整,得出了 TRITAM 模型技术接受标准总体结果的结论,结果良好,为 82.56%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combination of Grey Relational Analysis (GRA) and Simplified Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA-S) in Determining the Best Staff
Problems in selecting the best staff often involve complex challenges such as difficulty finding candidates with good performance. The problems faced in the selection of the best are only based on the assessment of discipline and productivity of performance carried out by the staff, so the assessment process does not use aspects of criteria that are considered important in selecting the best staff.  This study aims to determine the best staff based on predetermined criteria and in determining the selection of the best staff using the Gray Relational Analysis (GRA) decision model while in determining the weight of criteria using the Simplified Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA-S) model so that the weight of the resulting criteria is not based on assumptions from decision makers. The results of the best staff assessment ranking using the Gray Relational Analysis method and the Simplified Pivot Pairwise Relative Criteria Importance Assessment weighting method obtained the results, namely for Rank 1 obtained by Denis Irawan with a final Gray Relational Analysis value of 0.243014. The results of data processing in the TRITAM Model test for the best staff selection application were adjusted to the conclusion of the overall results of the TRITAM Model criteria for technology acceptance, the results were good at 82.56%.
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