<![CDATA[Ruang Normal Coffee UMKM implements online buying and selling in marketing their products, but these UMKM do not yet have distribution packaging to protect the product during delivery. Based on the field survey, the distribution problems were the coffee cup packaging being dented, leaking, difficult to carry, and unattractive. The distribution packaging is currently considered not following consumer preferences because it is only in the form of clear plastic. This study aims to design packaging for distributing MSME coffee drinks in the Normal Room according to consumer preferences. The method used is Kansei Engineering (KE). KE implementation is supported by decision-making methods in determining packaging design concepts and packaging design elements. The term frequency-inverse document frequency (TFIDF) and principal component analysis (PCA) methods determine the design concept. In contrast, the Rough Set method determines the design elements. Sampling of respondents using purposive sampling with a minimum number of 30 respondents. The results of this study were that there were 21-word pairs based on TFIDF extraction and 40 packaging samples. The design concept was obtained through PCA analysis of the two main components that have a variance value of 1, namely PC-1 produces the concept of "challenging-modern" and PC-2 produces the concept of "old-fashioned-practical." The combination of design elements from rough set analysis for the “challenging-modern” concept is cardboard/corrugated material, blunt end handles, folding cartons, tray/box type without additional features, sans-serif fonts, and illustrations. Meanwhile, the "old fashioned-practical" concept consists of kraft materials, round handles, sleeve shapes, inner stands or trays/boxes, pop-up designs, classic concepts, monochrome, and illustrations. The final step is implementing the design elements into the 3D packaging mockup model. This research has an impact on the development of science and technology in the packaging design process by considering consumer feelings using quantitative methods that are more objective]]>
