IMPLEMENTASI MODEL MACHINE LEARNING “STYLE QUEST” UNTUK REKOMENDASI PAKAIAN BERBASIS KECERDASAN BUATAN

Abstract

This research develops a web platform “Style Quest” that uses artificial intelligence to provide clothing mix and match recommendations. The ability to combine clothes appropriately is a challenge in the fashion industry, especially for those with limited choices or fashion knowledge. We implemented machine learning models such as Naive Bayes, Support Vector Machine (SVM), and Decision Tree to provide optimal recommendations. Clothing data was collected from various online sources, processed, and used to train the models. Results show that the SVM model provides the highest accuracy, followed by Decision Tree and Naive Bayes. This implementation was not only effective in the pilot test but also provided a satisfactory user experience on the Style Quest website. This research shows that machine learning models can improve the quality of clothing recommendations and offer practical solutions for everyday fashion applications. Future research can explore the integration of augmented reality technology to enhance user interaction.