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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) Jurnal CoreIT Syntax Literate: Jurnal Ilmiah Indonesia JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal Nasional Komputasi dan Teknologi Informasi Jesya (Jurnal Ekonomi dan Ekonomi Syariah) TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Majalah Ilmiah UNIKOM Infotronik : Jurnal Teknologi Informasi dan Elektronika JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Jurnal Digit : Digital of Information Technology Journal of Computer, Electronic, and Telecommunication (COMPLETE) Jurnal Pengabdian Masyarakat Indonesia Asean Journal of Science and Engineering (AJSE) International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH) Jurnal Locus Penelitian dan Pengabdian IJCOSIN : Indonesian Journal of Community Service and Innovation Inkubis: Jurnal Ekonomi dan Bisnis Economics and Business Journal Eduvest - Journal of Universal Studies Jurnal Pengabdian Masyarakat Tekno ASEAN Journal for Science and Engineering in Materials Technologia Journal Big Data Analytics and Data Science Cyber Security and Network Management Jurnal Nasional Komputasi dan Teknologi Informasi Curricula: Journal of Curriculum Development Digital Multimedia and Visualization Technology
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Journal : big data analytics and data science

Transforming the Global Aquaculture Supply Chain through the Integration of Artificial Intelligence and Big Data for Overcome Asymmetry Information Hernalom Sitorus; Zaenal Arifin Hasibuan; Bobi Kurniawan; Sri Supatmi
Big Data Analytics and Data Science Vol. 1 No. 2 (2026): June: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i2.443

Abstract

The global aquaculture sector faces structural challenges in the form of information asymmetry that causes a misalignment between production and market demand. The still-dominant production-driven paradigm leads to supply chain inefficiencies, low transparency, and limited traceability. This research aims to develop an information system integration model based on Artificial Intelligence (AI) and Big Data to transform the supply chain into a market-driven one. The research uses the Design Science Research (DSR) method, which includes needs analysis, data integration architecture design, development of Machine Learning and Deep Learning-based predictive models, and evaluation through prototype implementation. Expected outcomes include a data integration architecture, a supply-demand prediction model, and an AI-based traceability framework. This research contributes to improving the efficiency, transparency, and global competitiveness of the aquaculture sector.