INTEGRATING ARTIFICIAL INTELLIGENCE IN PENTAHELIX COLLABORATIVE GOVERNANCE: ADDRESSING POVERTY TARGETING GAPS IN DKI JAKARTA
DOI:
https://doi.org/10.18623/rvd.v23.5048Palabras clave:
Artificial Intelligence, Collaborative Governance, Pentahelix Model, Urban Poverty, Targeting Precision, Data Integration, Metropolitan Governance, Sociotechnical SystemsResumen
Urban poverty in rapidly urbanizing metropolitan areas requires coordinated, data-driven interventions involving multiple stakeholders. Although DKI Jakarta has achieved poverty rates below the national average (declining from 4.44% to 4.28% in 2024–2025), inequality remains high (Gini ratio 0.441), accompanied by targeting gaps in which 5.5% of priority beneficiaries are not reached due to data fragmentation and weak inter-agency coordination. This study explores the strategic integration of artificial intelligence (AI) within collaborative governance frameworks to improve poverty targeting accuracy and multi-stakeholder coordination in DKI Jakarta, focusing on the operationalization of the pentahelix model. Using a qualitative descriptive approach, data were collected through semi-structured interviews, focus group discussions, and document analysis involving government, private sector, academia, civil society, and community actors. Data analysis employed NVivo 12 with first-level coding based on Hymes’ SPEAKING model and second-level thematic analysis. The findings indicate that AI-enabled poverty governance functions as a complex sociotechnical system, where AI applications primarily support data infrastructure through deduplication and inter-agency synchronization. Targeting precision emerges from the integration of algorithmic analytics with community-based validation rather than automation alone. However, implementation faces challenges related to data governance, human resource capacity, infrastructure limitations, and digital inequality. The study proposes an AI-Enhanced Pentahelix Collaborative Governance Model, emphasizing that effective AI use depends on institutional capacity building and sustained collaborative processes, with AI serving as an augmentative tool rather than a replacement for human decision-making.
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