Penerapan Segmentasi Warna Menggunakan K-Means Clustering untuk Pemilihan Template dalam Pembuatan Konten
Abstract
The convenience of shopping online has resulted in the development of online business trends making content and publication consistency very important to attract consumers’ attention. Color selection is an important process in content creation. However, not everyone can choose the right colors, create interesting content, and have the time to create content and organize its publication. Li's research uses Generative Adversial Networks to help design’s layout. But these elements are not provided by the application, so user still need to design themselves. To answer this problem, a content maker application was developed.
K-Means Clustering is used to get the most dominant color from an image and Euclidean Distance calculates the closest color distance from the user's image with various design templates available. The additional feature of Scheduled Post addresses the problem of limited time for content publication.
K-means color segmentation of 20 images with 1 or 2 dominant colors obtains 90% accuracy. Five PCU VCD lecturers rated the accuracy of selected template design color nuances 76%. Making content using thesis application is 56.18% faster than using similar application. Result of content maker design compared to other designs won 1st place with voting score of 46.66%.
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