Rule-Based Hybrid Filtering for Skill Event Recommendation in A Multi-Platform Application

Digital skill development events require effective mechanisms for personalized event discovery and efficient administrative management. This study developed and validated SkillFest, a multi platform event management application incorporating a rule based hybrid filtering recommendation model for skill development events. The research employed an applied software engineering approach using Agile Kanban to guide requirement analysis, system design, implementation, and iterative evaluation. The recommendation mechanism combined content matching, interaction history, and explicit decision rules based on user interests, skill levels, and location preferences to support transparent recommendation generation under limited behavioral data conditions. The prototype integrated a web administration platform, a mobile application, a REST API layer, and a relational database environment. Empirical validation was conducted through API testing, functional testing, scenario based User Acceptance Testing, and White Box Testing. The results demonstrated successful implementation of event management, participant registration, recommendation delivery, QR attendance recording, certificate management, discussion forums, and reporting functions. All evaluated testing scenarios achieved successful outcomes and confirmed the reliability of core application processes. The study contributes an explainable recommendation framework that supports practical event discovery while maintaining operational feasibility during early stage deployment and prototype validation.