EFEKTIFITAS METODE IDENTIFIKASI KATA KANSEI: REVIEW

Widyastuti Widyastuti, Alifta Dicasani

= https://doi.org/10.26753/jitin.v2i2.1289
Abstract views = 126 times | views = 14 times

Abstract


Proses desain produk selalu berkaitan dengan kebutuhan pengguna. Keberhasilan proses tersebut tercermin dalam kemampuan produk untuk memenuhi kebutuhan fungsional, emosional maupun psikologis konsumen. Kansei engineering merupakan metode desain yang efektif untuk  menterjemahkan kebutuhan emosional menjadi spesifikasi produk. Kebutuhan pengguna direpresentasikan dengan kata-kata kansei yang mewakili perasaan, kesan dan gambaran atas suatu objek atau situasi. Dinamika perubahan pasar yang sangat cepat, berkorelasi dengan perubahan kebutuhan dan preferensi konsumen. Metode identifikasi kata kansei yang efektif diperlukan untuk menangkap akebutuhan  emosional pengguna secara real time dengan biaya dan waktu yang minimal. Kajian ini bertujuan untuk menelaah jenis metode yang telah digunakan dalam proses identifikasi kata-kata kansei pada penelitian sebelumnya, sehingga didapatkan  gambaran efektifitas tiap metode berdasarkan kemampuannya dalam menangkap kebutuhan emosional pengguna. Berdasarkan hasil review, secara garis besar terdapat lima jenis metode identifikasi kata kansei yaitu konvensional, online review, visual object, practical feeling, dan digital. Kelima metode tersebut masing-masing memiliki kelebihan dan kekurangan namun  cukup efektif untuk menggali kansei user need.


Keywords


kansei, metode, identifikasi, kebutuhan pengguna

Full Text:

PDF

References


C. N. Zabotto, S. Sergio Luis da, D. C. Amaral, C. Janaina Mascarenhas Hornos, and B. G. Benze, “Automatic digital mood boards to connect users and designers with kansei engineering,” Int J Ind Ergon, vol. 74, Nov. 2019, doi: 10.1016/j.ergon.2019.102829.

B. Razza and L. C. Paschoarelli, “Affective Perception of Disposable Razors: A Kansei Engineering Approach,” Procedia Manuf, vol. 3, pp. 6228–6236, 2015, doi: 10.1016/j.promfg.2015.07.750.

T. Djatna, L. P. Wrasiati, and I. B. D. Y. Santosa, “Balinese Aromatherapy Product Development Based on Kansei Engineering and Customer Personality Type,” Procedia Manuf, vol. 4, pp. 176–183, 2015, doi: 10.1016/j.promfg.2015.11.029.

S. Chanyachatchawan, H. Bin Yan, S. Sriboonchitta, and V. N. Huynh, “A linguistic representation based approach to modelling Kansei data and its application to consumer-oriented evaluation of traditional products,” Knowl Based Syst, vol. 138, pp. 124–133, Dec. 2017, doi: 10.1016/j.knosys.2017.09.037.

Z. Liu, J. Wu, Q. Chen, and T. Hu, “An improved Kansei engineering method based on the mining of online product reviews,” Alexandria Engineering Journal, vol. 65, pp. 797–808, Feb. 2023, doi: 10.1016/j.aej.2022.09.044.

McKinsey & Company, “The beauty market in 2023: A special State of Fashion report.” Accessed: Dec. 10, 2023. [Online]. Available: https://www.mckinsey.com/industries/retail/our-insights/the-beauty-market-in-2023-a-special-state-of-fashion-report

F. Guo, Q. X. Qu, M. Nagamachi, and V. G. Duffy, “A proposal of the event-related potential method to effectively identify kansei words for assessing product design features in kansei engineering research,” Int J Ind Ergon, vol. 76, Mar. 2020, doi: 10.1016/j.ergon.2020.102940.

P. Wang, J. Chu, S. Yu, C. Chen, and Y. Hu, “A consumers’ Kansei needs mining and purchase intention evaluation method based on fuzzy linguistic theory and multi-attribute decision making method,” Advanced Engineering Informatics, vol. 59, p. 102267, Jan. 2024, doi: 10.1016/j.aei.2023.102267.

M. Misaka and H. Aoyama, “Development of design system for crack patterns on cup surface based on KANSEI,” J Comput Des Eng, vol. 5, no. 4, pp. 435–441, Oct. 2018, doi: 10.1016/j.jcde.2017.12.008.

C. Yang, F. Liu, and J. Ye, “A product form design method integrating Kansei engineering and diffusion model,” Advanced Engineering Informatics, vol. 57, Aug. 2023, doi: 10.1016/j.aei.2023.102058.

W. Kim, T. Ko, I. Rhiu, and M. H. Yun, “Mining affective experience for a kansei design study on a recliner,” Appl Ergon, vol. 74, pp. 145–153, Jan. 2019, doi: 10.1016/j.apergo.2018.08.014.

Y. Jiao and Q. X. Qu, “A proposal for Kansei knowledge extraction method based on natural language processing technology and online product reviews,” Comput Ind, vol. 108, pp. 1–11, Jun. 2019, doi: 10.1016/j.compind.2019.02.011.

X. Lai, S. Zhang, N. Mao, J. Liu, and Q. Chen, “Kansei engineering for new energy vehicle exterior design: An internet big data mining approach,” Comput Ind Eng, vol. 165, Mar. 2022, doi: 10.1016/j.cie.2021.107913.

J. R. Chou, “A Kansei evaluation approach based on the technique of computing with words,” Advanced Engineering Informatics, vol. 30, no. 1, pp. 1–15, Jan. 2016, doi: 10.1016/j.aei.2015.11.001.

M. Kikumoto, Y. Kurita, and S. Ishihara, “Kansei Engineering Study on Car Seat Lever Position,” Int J Ind Ergon, vol. 86, Nov. 2021, doi: 10.1016/j.ergon.2021.103215.

T. Kinoshita, S. Murakamia, T. Yamamoto, M. G. Machizawa, and K. Tanaka, “Design and Experimental Analysis of a Database-Driven Kansei Feedback Control System using EEG Data,” IFAC-PapersOnLine, vol. 56, no. 2, pp. 3610–3615, 2023, doi: 10.1016/j.ifacol.2023.10.1522.

F. Guo, Q. X. Qu, M. Nagamachi, and V. G. Duffy, “A proposal of the event-related potential method to effectively identify kansei words for assessing product design features in kansei engineering research,” Int J Ind Ergon, vol. 76, Mar. 2020, doi: 10.1016/j.ergon.2020.102940.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Widy Astuti

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

View My Stats
This journal (p-ISSN:2830-0076; e-ISSN:2830-0084) is licensed under

a Creative Commons Attribution 4.0 International License

Creative Commons License

Universitas Muhammadiyah Gombong

Address: Jl. Yos Sudarso No.461 Gombong, Kabupaten Kebumen, Jawa Tengah 54412

email: jitin@unimugo.ac.id