LOCAL GOVERNMENT UNIT 3: BUSINESS REGISTRATION AND RENEWAL SYSTEM WITH DOCUMENT UPLOAD, APPLICATION TRACKING, EXPORT OPTIONS, QR CODE FILTERING, AND AI-POWERED EXPIRATION ALERTS & RENEWAL ANALYTICS

Abstract

The modernization of local government operations has become increasingly necessary to address the growing demand for efficient, transparent, and accessible public service delivery. This project introduces the Business Registration and Renewal System for Local Government Unit 3 (LGU 3), an online platform designed to digitize and streamline business registration and renewal processes. Key features include document upload, application tracking, export options, QR code filtering, and AI-powered expiration alerts and renewal analytics. The system aims to reduce administrative burden while improving operational accuracy and responsiveness. The system was developed using a modular software architecture to ensure scalability and ease of maintenance. It integrates front-end web technologies with a robust back-end framework. Artificial intelligence was utilized to develop predictive models capable of analyzing renewal trends and generating automated expiration notifications. The system also incorporated QR code generation and scanning features for efficient document filtering and verification. Agile methodology guided the development process, allowing for iterative design, testing, and user feedback integration at each stage. The implementation of the system resulted in significant improvements in process efficiency and user satisfaction. Business owners experienced faster registration and renewal procedures, while LGU personnel benefited from reduced paperwork and fewer manual errors. The AI module effectively identified upcoming expirations and prompted timely renewal notifications. Additionally, the QR code feature enabled rapid retrieval and verification of documents, further enhancing workflow speed and accuracy. The project demonstrated the effectiveness of digital transformation in enhancing public service delivery within local government units. By automating filing, tracking, and document processing, the system contributed to reduced administrative workload and improved data management. The integration of AI and automation highlighted the potential for predictive governance and proactive service strategies. Future development may include integration with mobile applications, real-time dashboards, and broader interoperability with other LGU systems to support comprehensive e-governance initiatives.