Affective design identification on the development of batik convection product

International Conference on Informatics, Technology and Engineering 2017

计算机科学

Prastawa, H.^1 ; Purwaningsih, R.^1

Department of Industrial Engineering, Faculty of Engineering, Diponegoro University, Indonesia^1

关键词: Affective design;    Batik;    Consumer perception;    Creative industries;    Cultural heritages;    Design alternatives;    Kansei Engineering;    Value perceptions;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/245/1/012025/pdf
DOI  :  10.1088/1757-899X/245/1/012025

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

The affective design is increasingly applied to product development in order to meet the desires and preferences of customers. Batik is a traditional Indonesian culture containing historical and cultural values. The development of batik design is one of the efforts to strengthen the identity and superiority of Indonesia's creative industries as well as to preserve batik as the cultural heritage of the nation. Batik product designs offered by the manufacturers do not necessarily correspond with the wishes of consumers, especially the affective values involved. Therefore it is necessary to identify consumer perceptions of convection- based batik product in the form of clothing and fabrics, especially the affective value as the consideration for the designer or manufacturer to develop design alternatives to batik convection products. This research aims to obtain information on consumer affective value, to identify the affective value perception differences among X and Y Generation and to classify affective value in the corresponding cluster of the batik products convection. This study uses Kansei engineering to determine the perception of affective design in the form of Kansei word. Cluster Analysis was used to form clusters that classify affective value of the same class. The results showed that there were 16 pairs of Kansei word which was worth as an affective consumer desire, the 3 indicators that had significant differences among X and Y Generation and 4 clusters with different characteristics.