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Abstract
Kansei Engineering System (KES) is of technologies in ergonomics that support human-centered design. In Japanese, Kansei means customers’ taste to a certain product. The research aims to apply KES Type II (a more recent development of KES)with artificial neural network (back propagation model) and traingdx learning algorithm to identify product design that fits best with customers’ taste/preferences.The model developed is able to generate a design for steering wheel with various atributes; namely shape of button, number of spokes, material of spokes, texture of grip, style of grip, and color. These atributes are results from 16 pairs of Kansei words which is reduced into five main factors; namely aestethics, emotional evaluation, color, texture, and product reliability.
Keywords: Kansei Engineering System, artificial neural networks, back-propagation, customers’ preferences.
Keywords: Kansei Engineering System, artificial neural networks, back-propagation, customers’ preferences.
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