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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Ilmiah Universitas Batanghari Jambi IKRA-ITH Humaniora : Jurnal Sosial dan Humaniora JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Universitas Pamulang IKRA-ITH ABDIMAS Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal INFOKUM JUKI : Jurnal Komputer dan Informatika Jurnal Scientia MEANS (Media Informasi Analisa dan Sistem) Jurnal Pengabdian kepada Masyarakat Nusantara JoMMiT : Jurnal Multi Media dan IT Instal : Jurnal Komputer Jurnal Multidisiplin Madani (MUDIMA)
Sanjaya Pinem
Politeknik Negeri Media Kreatif
Aerospace Engineering Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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SIGMA-AI: Multimedia Engineering Technology Digital Gallery Platform with Artificial Intelligence Tyas, Sari Setyaning; Pinem, Sanjaya; Setyasmara, Nofiandri; Kuswoyo, Deni; Mawardi, Cholid
Jurnal Ilmiah Universitas Batanghari Jambi Vol 26, No 1 (2026): Februari
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/jiubj.v26i1.6387

Abstract

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.
Co-Authors Deswidya Hutauruk Desy Rinika Purba Diah Amelia Eko Djuniarto Elsida Aritonang Febrianto Sibuea Freddy Yakob Huda, Achmad Torikul Hutagaol, Victor Edison Hutagaol, Viktor Edison Indera Nata Raharja Kuswoyo, Deni M. Cholid Mawardi Muhamad Ridwan Muhamad Rosit Nofiandri Setyasmara Pakpahan, Victor Maruli Prily Fitria Aziz Purba, Doni El Rezen Syafriyandi, Syafriyandi TONNI LIMBONG Tyas, Sari Setyaning Viktor Edison Hutagaol Wijaya, Dhanny Hidayat Yakob, Freddy Zalzulifa Zalzulifa