Kansei Analysis using Analytical Hierarchy Process
- https://doi.org/10.2991/aebmr.k.200108.050How to use a DOI?
- Decision Support System, Kansei, AHP, Helpdesk
In determining a choice that is not based on specifications, it is important to know the aspects of feelings in a product. Kansei is a technology that translates feelings into product design. The application of Kansei in the development of decision support systems can help facilitate decision making based on feelings. Decision support system development certainly involves a decision support system method. One method that is often applied in decision support systems is the Analytical Hierarchy Process (AHP), as many decision support systems have been applied in the industrial world referring to the evaluation of a number of criteria to evaluate a number of existing criteria used the AHP method which able to approach the assessment of qualitative and quantitative criteria. AHP method is the right solution for the case of product selection based on feelings that have the same specifications. This research aims to choose IT Helpdesk based on feelings. For now, there are several recommended options for a helpdesk that can serve properly. The result of this research is to produce the best alternative recommendations with the criteria that influence. The results obtained from this research are to produce the selection of helpdesk with the highest weight, which the C-Desk alternative with a value of 0.2119 and influenced by Classical criteria with a value of 0.0242 as the main factor.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Chandra Nuur Huda AU - Ana Hadiana PY - 2020 DA - 2020/01/13 TI - Kansei Analysis using Analytical Hierarchy Process BT - Proceedings of the International Conference on Business, Economic, Social Science, and Humanities – Economics, Business and Management Track (ICOBEST-EBM 2019) PB - Atlantis Press SP - 218 EP - 223 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.200108.050 DO - https://doi.org/10.2991/aebmr.k.200108.050 ID - Huda2020 ER -