Chatbot for Clothing Color Recommendations Based on Skin Tone - Rahayu Ramli; Anna Dina Kalifia (bit-Tech, 2025)
In the era of global digital fashion and personalized styling technologies, the ability to match clothing colors with individual skin tones has become increasingly important for enhancing self-expression and confidence. Many people struggle to choose clothing colors that match their skin tone due to limited knowledge of proper color combinations. As a result, they often select outfits based on trends or personal taste without considering compatibility with their skin tone, which affects confidence and comfort in appearance. This study aims to develop a novel Natural Language Processing (NLP)–based chatbot that uniquely interprets textual descriptions of skin tone to recommend suitable clothing colors. Users input their skin tone, and the chatbot analyzes it, classifying the input into appropriate skin tone and undertone categories. The data were obtained from interviews with personal color analysts and color theory in fashion. The research involves system requirement analysis, chatbot architecture design, and the creation of flowcharts, use case diagrams, and activity diagrams to describe user–system interactions. Quantitative evaluation shows that the implemented chatbot achieves over 80% accuracy in recognizing textual skin tone descriptions and delivers responses within an average of 1.8 seconds, demonstrating strong empirical performance. It can also suggest matching clothing colors and indicate those to avoid. This system enables users to obtain suitable clothing color recommendations quickly and interactively. The study highlights the growing role of AI-driven interaction design in modern fashion systems and positions the model as a bridge between linguistic input and aesthetic recommendation technologies.