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p-Index From 2021 - 2026
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All Journal Green Social: International Journal of Law and Civil Affairs Jurnal Manajemen, Bisnis dan Kewirausahaan
Vivi Venti Vera Sulila
Universitas Negeri Gorontalo
Decision Sciences, Operations Research & Management Law, Crime, Criminology & Criminal Justice

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Sinergi antara Lembaga Pemerintah melalui E-Government: Kajian Implementasi dan Tantangan Kolaboratif Vivi Venti Vera Sulila; Sri Yulianty Mozin
Jurnal Manajemen, Bisnis dan Kewirausahaan Vol. 5 No. 3 (2025): Desember : Jurnal Manajemen, Bisnis dan Kewirausahaan
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jumbiku.v5i3.6071

Abstract

This article analyzes inter-agency synergy in Indonesia through e-Government as a foundation for collaborative governance. The study investigates how digital governance creates coordination, interoperable data exchange, and transparency among ministries and government agencies, and maps the collaborative challenges that impede implementation. Using a qualitative descriptive approach based on policy analysis and literature review, the paper synthesizes Indonesia’s SPBE (Electronic-Based Government System) policy regime, national initiatives such as Satu Data Indonesia and GovTech (INA Digital), and recent scholarly findings on collaborative and agile governance. Findings indicate that e-Government enhances synergy through integrated platforms, shared standards, and institutionalized coordination, yet progress is uneven due to bureaucratic silos, fragmented legacy systems, regulatory ambiguity at the technical level, cyber-security risks, and human-capital gaps. The paper argues that sustained leadership, a national interoperability framework, trust-based data governance, and capability development are decisive enablers. The implications highlight the need to institutionalize cross-agency stewardship, align performance metrics with collaboration outcomes, and embed agile routines to adapt to fast-changing technologies.
Artificial Intelligence Applications in Modern Tax Admin-istration: Opportunities and Challenges Vivi Venti Vera Sulila; Fenti Prihatini Dance Tui
Green Social: International Journal of Law and Civil Affairs Vol. 3 No. 2 (2026): June: International Journal of Law and Civil Affairs
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greensocial.v3i2.299

Abstract

This research examines the application of artificial intelligence (AI) in modern tax administration, focusing on three domains: fraud detection, taxpayer services, and operational efficiency. The main problems identified are the gap between AI's rapid technical advancement and the readiness of governance, legal, and ethical frameworks, as well as the lack of integrated analysis across these dimensions. The objective is to synthesise empirical evidence on AI adoption opportunities and challenges, and to formulate actionable policy recommendations. Using a systematic literature review with thematic analysis of 20 sources (2021–2026), including OECD reports, peer-reviewed articles, and institutional studies, this research compares evidence from multiple jurisdictions (EU, US, China, Indonesia). The main findings show that AI adoption soared from 9% (2016) to 69% (2023), with fraud detection rates reaching 50%, potential efficiency gains of up to 40%, and processing time reductions of up to 70%. However, five critical challenges are identified: algorithmic bias and opacity, legal ambiguity, data privacy risks, accountability gaps, and human resource constraints. The synthesis reveals a non-linear (inverted U‑shape) relationship between AI usage intensity and professional vigilance. This research concludes that AI offers substantial opportunities, but success depends on humane, transparent, and accountable governance. An integrative three‑phase roadmap (inception, consolidation, optimisation) is proposed to balance power and trust dimensions in AI‑enabled tax administration.
Co-Authors Fenti Prihatini Dance Tui Sri Yulianty Mozin