An Integrated Kansei Engineering and Genetic Algorithm Apporach for Modeling Oolong Tea Package Design
DOI:
https://doi.org/10.32734/ee.v9i2.2793Keywords:
Desain Kemasan, Elemen Kemasan, Genetic Algorithm, Kansei Engineering, Packaging Design, Packaging ElementsAbstract
Penelitian ini dilatarbelakangi oleh belum optimalnya elemen desain kemasan teh oolong dalam merepresentasikan konsep yang diharapkan oleh konsumen. Permasalahan tersebut terlihat dari kelemahan pada aspek visual maupun informasi yang disajikan pada kemasan teh oolong. Berdasarkan hasil survey pada penelitian terdahulu, sebanyak 93% responden menyatakan perlunya dilakukan redesain pada elemen kemasan teh oolong. Metode yang digunakan pada penelitian ini adalah Kansei Engineering. Metode ini bertujuan untuk mengkaji hubungan antara respon emosional konsumen dengan atribut desain. Metode pendukung yang digunakan ialah Genetic Algoritma untuk memperoleh kombinasi elemen desain yang optimal. Data yang diperoleh dari 66 sampel kemasan, analisis morfologi, serta hasil kuesioner semantic differential 2, kemudian diolah menggunakan software Visual Code Studio. Hasil penelitian menunjukkan bahwa elemen desain kemasan mengarah pada dua konsep, yaitu ekslusif dan praktis. Konsep ekslusif memperoleh nilai 0,679 dengan material sebagai elemen utamaa, sedangkan konsep praktis memperoleh nilai 0,687 dengan volume sebagai elemen yang di prioritaskan.
This study was motivated by the fact that the design elements of oolong tea packaging have not yet optimally represented the concepts expected by consumers. This issue is evident in the weaknesses of both the visual aspects and the information presented on oolong tea packaging. Based on the results of a survey conducted in a previous study, 93% of respondents stated that the design elements of oolong tea packaging need to be redesigned. The method used in this study is Kansei Engineering. This method aims to examine the relationship between consumers’ emotional responses and design attributes. The supporting method used is the Genetic Algorithm to obtain the optimal combination of design elements. Data obtained from 66 packaging samples, morphological analysis, and the results of the Semantic Differential Scale 2 questionnaire were then processed using Visual Code Studio software. The results of the study indicate that packaging design elements align with two concepts: exclusive and practical. The exclusive concept received a score of 0.679, with material as the primary element, while the practical concept received a score of 0.687, with volume as the prioritized element.
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