Case Reporting System with Tracking and Chat Machine Learning for The Department of Women's Empowerment and Child Protection Manado - Rumondor, Andreas; Wensen, Viltie A.; Budiman, Marson J.; Kapoh, Harson (Newinera Publisher, 2025)
This study aims to develop a more interactive, responsive, and participatory web-based case reporting system for the public, particularly within the Department of Women’s Empowerment and Child Protection (DWECP) of Manado City. The developed system, named LABRRAK, integrates two key components: (1) a machine learning-based chatbot that utilizes the Google Dialogflow platform with Natural Language Processing (NLP) technology to automatically respond to user inquiries; and (2) a real-time case tracking feature based on a finite state machine algorithm, allowing reporters to directly monitor the progress of their cases. This research adopts a Research and Development (R&D) method combined with the Agile development model, which emphasizes an iterative and evaluative process through sprint stages. This approach enables the system to adapt to changing user needs and enhance its features based on direct feedback. Each sprint cycle concludes with testing using the Black Box Testing method to ensure that all system functionalities perform as expected. The testing results demonstrate that all core features, including case reporting, chatbot conversations, status tracking, and the administrative analytics dashboard, function optimally and meet user requirements. LABRRAK’s innovation lies in its integration of dynamic status tracking and two-way communication features into a single, unified digital platform. The system is expected to accelerate case handling, improve transparency, and strengthen accountability in addressing cases of violence against women and children. Furthermore, LABRRAK has the potential to serve as a prototype for the development of other inclusive, technology-driven public service systems in the social sector.