Developing a Reference for Smart Rural Communities: An Enterprise Architecture Perspective for Smart Governance

The digital transformation of rural governance is critical to achieving Sustainable Development Goals (SDGs) and fostering community self-reliance. This study develops a comprehensive Enterprise Architecture (EA) framework tailored for smart rural communities using the TOGAF 9.2 methodology. The fra...

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Bibliographic Details
Main Authors: Intania Fadya Safitri, Asti Amalia Nur Fajrillah, Iqbal Yulizar Mukti
Format: Article
Language:Indonesian
Published: Program Studi Sistem Informasi, Universitas Islam Negeri Raden Fatah Palembang 2024-12-01
Series:Jurnal Sistem Informasi
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Online Access:https://jurnal.radenfatah.ac.id/index.php/jusifo/article/view/24294
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Summary:The digital transformation of rural governance is critical to achieving Sustainable Development Goals (SDGs) and fostering community self-reliance. This study develops a comprehensive Enterprise Architecture (EA) framework tailored for smart rural communities using the TOGAF 9.2 methodology. The framework integrates business, data, application, and technology architectures to address inefficiencies, enhance transparency, and optimize governance processes in rural Indonesia. The research adopts the Design Science Research Methodology (DSRM) to ensure systematic artifact development and alignment with strategic goals. Key outcomes include improved administrative services, asset management, and community engagement, demonstrated through case studies involving systems such as SIMIDES, SISMANDES, and OpenSID. The proposed EA framework directly contributes to enhancing Village Development Index (IDM) scores, supporting SDGs like poverty alleviation, quality education, and reduced regional disparities. While the framework presents a scalable model for rural governance, challenges such as IT infrastructure limitations and stakeholder readiness remain. Future research should explore the integration of emerging technologies like IoT and AI to further enhance the model’s adaptability.
ISSN:2460-092X
2623-1662