DEVELOPMENT OF AN ONLINE SKILL BARTER-BASED SYSTEM FOR PERSONALIZED COMPETENCY MATCHING USING MACHINE LEARNING

The increasing demand for flexible, affordable, and collaborative learning has created opportunities for digital platforms that support peer-to-peer skill exchange. This study focuses on the development of an online skill barter-based system for personalized competency matching using machine learning. The system enables users to offer and learn skills through direct exchange rather than monetary transactions. A K-Nearest Neighbor (KNN) algorithm was integrated with TF-IDF and Natural Language Processing (NLP) techniques to intelligently match users based on skill similarity, learning preferences, and availability. The platform was developed using Next.js and TailwindCSS for the frontend, with Supabase as the backend for authentication and data management. The system promotes inclusive and accessible learning by removing financial barriers while fostering collaboration and community growth. This research demonstrates that integrating machine learning into skill exchange platforms significantly enhances user experience, match accuracy, and trust in non-monetary, peer-driven learning environments.