<![CDATA[The rapid expansion of digital creativity in higher education has increased the need for structured, secure, and intelligent platforms capable of managing large volumes of multimedia-based student work. SIGMA-AI was developed to address this challenge by integrating artificial intelligence into a digital gallery system specifically designed for the Multimedia Engineering Technology study program. Using the Rapid Application Development (RAD) methodology, the platform was built through iterative cycles of requirement analysis, system design, implementation, and testing. Key features include AI-driven auto-tagging, intelligent work recommendations, role-based validation, and an academic analytics dashboard. White-box testing demonstrates that the system’s core processes login, artwork upload, and profile updatesoperate reliably across different scenarios. Comparative analysis shows that SIGMA-AI outperforms mainstream portfolio platforms such as Behance, Dribbble, and cloud repositories by offering structured academic workflows, automated metadata generation, and pedagogical insights unavailable in conventional systems. The findings indicate that SIGMA-AI is not only technically feasible but also strategically valuable for strengthening digital archiving, enhancing learning analytics, and supporting AI literacy in creative education environments.]]>
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