Saidinur
Jurusan Administrasi Publik, Fakultas Ilmu Sosial dan Ilmu Politik, Universitas Sriwijaya

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Strengthening the Capacity of Village Data Operators in the Implementation of Dtsen by the Ogan Ilir Regency Government Atrika Iriani; Saidinur; Elivia Pasma Putri
Jurnal Studi Sosial dan Politik Vol. 9 No. 1 (2025): Jurnal Studi Sosial dan Politik
Publisher : Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jssp.v9i1.36101

Abstract

The implementation of the National Socio-Economic Single Data (DTSEN) as a single national database positions village data operators as key actors in ensuring the accuracy of data used for the distribution of social assistance and the formulation of development policies. However, the implementation of DTSEN in Ogan Ilir Regency still faces various obstacles, such as unequal understanding of the DTSEN concept, limited technical competence of operators, and suboptimal coordination between stakeholders. This study aims to analyze the capacity of village data operators in DTSEN implementation using the 6P Model developed by Christer Vindeløv-Lidzélius (2021), covering perspective, purpose, people, partners, planning, and practice. The research method used is a qualitative approach through document studies, in-depth interviews, and Focus Group Discussions (FGDs) with village data operators and relevant agencies in Ogan Ilir Regency. The results show that the capacity of village data operators has been quite good, but still needs strengthening across all 6P dimensions. The most prominent aspects include limited conceptual understanding of DTSEN, differences in technical competence between operators, and suboptimal coordination across actors. Therefore, a capacity-building strategy is needed through improving human resource competencies, aligning perceptions regarding the objectives of the DTSEN (Village Data Enforcement Agency), strengthening partnerships, improving planning, and providing ongoing support for data management practices. These efforts are expected to improve the quality of socioeconomic data, making it more accurate and valid, and supporting the formulation of targeted development policies.