JUNIOR HIGH SCHOOL ALUMNI INFORMATON WITH CHATBOT SUPPORT FOR CAYBIGA HIGH SCHOOL

Abstract

Recognizing the limitations of traditional alumni engagement methods in today’s digitally connected world, this study titled "Junior High School Alumni Information with Chatbot Support for Caybiga High School", proposes a desktop application to modernize outdated alumni relations. Periodic newsletters and occasional reunions are no longer sufficient to maintain meaningful, long-term connections. To address this, the research introduces an AI-driven solution that combines a secure alumni database with chatbot support, enabling efficient, accessible, and personalized information retrieval. This system aims to streamline alumni communication, address concerns promptly, and foster stronger, ongoing engagement within the school community. The system was developed following the Software Development Life Cycle (SDLC) using the Waterfall Model, which includes seven sequential phases: Planning, Data Analysis, Design, Development, Testing, Implementation, and Maintenance. To identify system requirements, the researchers conducted interviews with key stakeholders, and the insights gathered were used to design a detailed system architecture. During the development stage, Apache NetBeans was employed for coding and debugging, while MySQL was utilized to design and manage the alumni database efficiently. The Junior High School Alumni Information System with Chatbot Support was successfully developed and deployed at Caybiga High School. It features a secure MySQL database that stores alumni's personal, contact, and academic information. Developed using Apache NetBeans, the system includes a user-friendly interface designed for accessibility and ease of use. Testing confirmed that the system efficiently processes queries and retrieves data. Interview feedback further validated its functionality, with users reporting the system to be intuitive, reliable, and free from major issues. This project underscores the effectiveness of AI-powered chatbots in enhancing alumni engagement in contemporary educational settings. Guided by the Waterfall Model and developed using reliable tools such as Apache NetBeans and MySQL, the system achieved a secure and user-friendly platform. The chatbot significantly improved response times and user satisfaction. However, the project was limited by a small interview sample and the chatbot’s basic functionality. Future developments should prioritize the integration of natural language processing (NLP), improved scalability, social media integration, and sustained system support.