Integration of Belmawa Student Competition Dashboard With Artificial Intelligence Using The Waterfall Methodology at The Faculty of Industrial Engineering Telkom University
Abstrak
The rapid development of information technology demands that higher education institutions manage student achievement data optimally through intelligent and interactive dashboards. The Faculty of Industrial Engineering, Telkom University, still uses conventional dashboards with significant limitations in providing analytical insights for strategic decision-making. This research aims to develop an integrated Artificial Intelligence (AI) dashboard system to interactively analyze student achievement in the Belmawa competition and provide data-driven recommendations. The method used is the Waterfall Software Development Life Cycle (SDLC) model, through the stages of requirements, design, implementation, validation, and maintenance. The system is designed using a four-layer architecture with Aiven MySQL Cloud, Google Looker Studio, Flowise AI with OpenAI GPT-4o, and Laravel. The research data comes from 867 student achievement records with 22 essential attributes processed from 6,642 initial data, representing a data reduction of 86.95%. The implementation produces an interactive dashboard with various visual graphs and an NLP-based AI chatbot that answers user questions related to the data. System testing using Laravel Dusk, UAT, and SUS demonstrated excellent results, with a 100% success rate and a SUS score of 90.0, categorized as "Best Imaginable." This research demonstrates that AI integration in dashboards helps improve data-driven analysis and decision-making in higher education.
Keyword— dashboard, artificial intelligence, belmawa competition, information system, waterfall model
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