DESIGN AND IMPLEMENTATION OF A KNN-BASED JOB MATCHING SYSTEM USING WEB TECHNOLOGY

The process of finding suitable on-campus job opportunities remains a significant challenge for many university students, particularly due to limited access, lack of awareness, and minimal networking platforms. To address these issues, this project focuses on the design and implementation of a K-Nearest Neighbors (KNN)-based Job Matching System using web technologies. The system is tailored specifically for students within a university environment, aiming to bridge the gap between student skill sets and available opportunities such as internships, research assistance, and part-time campus jobs. Leveraging the KNN algorithm, the system classifies and recommends jobs by analyzing student profiles, including their academic major, career interests, and listed skills. Through a web-based interface, students can input their information, which the system then uses to match them with relevant job listings based on similarity metrics derived from existing data. The platform also facilitates networking among students with similar career goals, fostering collaboration and peer support. The system was developed using React.js for the front end, Node.js for the back end, and MongoDB for data management. A need assessment survey informed the design of the KNN model, ensuring its relevance to student needs. Additionally, the system was evaluated for user-friendliness, scalability, efficiency, robustness, and security, with testing conducted both by target users (Software Engineering students) and using tools like Webpagetest.org. Findings from testing confirmed that the system effectively streamlines the job search process, offering personalized, timely recommendations and enhancing student engagement. The results demonstrated the practical application of machine learning in academic settings, showcasing how technology can simplify Job Matching while building stronger campus communities. The system also supports student entrepreneurship by allowing student-run businesses to connect with potential clients within the university. This project highlights the potential of integrating AI-driven solutions into student services, offering a scalable, adaptable model for enhancing career development and networking in higher education settings.