Rekonstruksi Ekosistem Kopi Indonesia Berbasis Artificial Intelligence dan Teknologi Informasi sebagai Kerangka Sistem Digital Berkelanjutan Menuju Indonesia Emas 2045
DOI:
https://doi.org/10.55606/juisik.v5i3.1991Keywords:
Artificial Intelligence, Coffee Industry, Data Integration, Digital Transformation, Sustainable SystemAbstract
The Indonesian coffee industry has high complexity, involving production, distribution, consumption actors, as well as social and economic dynamics that have not been fully integrated into the digital system. The main issues in the national coffee ecosystem include data fragmentation, limited integrated information systems, and low utilization of Artificial Intelligence (AI) in data-driven decision-making. This study aims to design a conceptual framework for the reconstruction of an AI-based coffee ecosystem supported by information technology as a foundation for a sustainable digital system to support Indonesia's Golden Vision 2045. This research uses an interpretative qualitative approach with an exploratory design. Data was collected through literature studies, observations of digitalization practices within the coffee ecosystem, and semi-structured interviews with stakeholders from the Indonesian Coffee Association (ASKI). The analysis is conducted using a socio-technical systems approach to understand the relationship between technology, actors, and data flows in the coffee ecosystem. The results indicate that AI plays a role as a decision support system capable of adaptively integrating production, market, and social data. Information technology functions as a connecting platform that enhances transparency, efficiency, and sustainability in the coffee ecosystem. This study contributes by offering a conceptual model of an AI-based coffee ecosystem system that is relevant to the development of smart agricultural information systems and national digital transformation.
References
Badan Perencanaan Pembangunan Nasional. (2021). Visi Indonesia 2045. Kementerian PPN/Bappenas.
Bappenas. (2023). Transformasi ekonomi berkelanjutan menuju Indonesia Emas 2045.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209. https://doi.org/10.1007/s11036-014-0510-x
Food and Agriculture Organization. (2022). Digital agriculture and rural transformation. FAO.
Gereffi, G., Humphrey, J., & Sturgeon, T. (2005). The governance of global value chains. Review of International Political Economy, 12(1), 78–104. https://doi.org/10.1080/09692290500049805
Kurniawan, F., & Santoso, H. B. (2020). Sistem informasi pertanian berbasis digital. Jurnal RESTI, 4(5), 873–880. https://doi.org/10.29207/resti.v4i5.1754
Latour, B. (2005). Reassembling the social. Oxford University Press.
Liakos, K. G., et al. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
McAfee, A., & Brynjolfsson, E. (2017). Machine, platform, crowd. W. W. Norton.
Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64–88.
Rogers, E. M. (2003). Diffusion of innovations. Free Press.
United Nations Development Programme. (2022). Digital transformation and SDGs. UNDP.
World Economic Forum. (2023). Artificial intelligence and the future of food systems. WEF.
Xu, L. D., He, W., & Li, S. (2014). Internet of Things in industries. IEEE Transactions on Industrial Informatics, 10(4), 2233–2243. https://doi.org/10.1109/TII.2014.2309299
Zhang, Y., Wang, G., & Jin, J. (2021). AI-based decision support systems. Expert Systems with Applications, 168, 114252. https://doi.org/10.1016/j.eswa.2020.114252
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
