Bulletin of Informatics and Data Science
Vol 2, No 2 (2023): November 2023

Combination of Grey Relational Analysis (GRA) and Simplified Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA-S) in Determining the Best Staff

Setiawansyah, Setiawansyah (Unknown)
Sintaro, Sanriomi (Unknown)
Saputra, Very Hendra (Unknown)
Aldino, Ahmad Ari (Unknown)



Article Info

Publish Date
01 Mar 2024

Abstract

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%.

Copyrights © 2023






Journal Info

Abbrev

bids

Publisher

Subject

Computer Science & IT Electrical & Electronics Engineering Engineering

Description

The Bulletin of Informatics and Data Science journal discusses studies in the fields of Informatics, DSS, AI, and ES, as a forum for expressing research results both conceptually and technically related to Data ...