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All Journal International Journal of Basic and Applied Science Indonesia Accounting Research Journal
Bambang Saras Yulistiawan
Universitas Pembangunan Nasional Veteran Jakarta, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Physics

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A System dynamics quantitative model for enhancing e-government maturity in the indonesian education sector Bambang Saras Yulistiawan; Rifka Widyastuti; Rr Octanty Mulianingtyas; Galih Prakoso Rizky A; Hengki Tamando Sihotang
International Journal of Basic and Applied Science Vol. 14 No. 2 (2025): Optimization and Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i2.693

Abstract

This study develops a deterministic mathematical model integrated with system dynamics to measure key success factors driving e-government maturity in Indonesia’s education sector. Addressing the gap in previous research, which mainly relied on descriptive methods, the model quantitatively examines causal relationships among leadership commitment, budget support, digital infrastructure, human capital, service quality, and feedback mechanisms. The methodology involves three stages: (1) constructing a causal loop diagram based on theoretical and empirical insights, (2) converting these relationships into a linear system of equations normalized on a [0–1] scale, and (3) performing simulations and sensitivity analyses to evaluate policy scenarios. Simulation results indicate that even relatively high leadership commitment (K=0.75) only produces moderate maturity levels (M≈0.409). The greatest improvement occurs when feedback loops are reinforced and service quality investments are prioritized. Sensitivity analysis reveals the model is particularly responsive to changes in feedback effectiveness and service quality weighting, identifying these as critical leverage points for accelerating transformation. Under optimal conditions, maturity can increase from 0.41 to 0.48, reflecting a 7% gain over the baseline. The study contributes a replicable quantitative framework for evidence-based policymaking, while noting limitations in parameter assumptions and empirical calibration for future refinement.
KMS for overcoming stunting in early childhood and pregnant women using the Soft System Methodology (SSM) with the Learning Lesson System (LLS) approach Erly Krisnanik; Muhammad Adrezob; Kraugusteeliana Kraugusteeliana; Bambang Saras Yulistiawan; I Gede Susramae
International Journal of Basic and Applied Science Vol. 14 No. 3 (2025): Optimization and Artificial Intelligence
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i3.834

Abstract

This study addresses the concerning prevalence of stunting among early childhood and pregnant women in Indramayu Regency, which reached 18.4% in 2024, exceeding the national target of 14%. It aims to develop a Knowledge Management System (KMS) to support integrated stunting control efforts by employing Soft Systems Methodology (SSM) for comprehensive problem identification and the Learning Lesson System (LLS) to incorporate proven best practices. The KMS is designed to optimize information distribution regarding the causes, impacts, and interventions for the stunting issue, while enhancing collaboration among government, community, and families. The integration of SSM and LLS allows the system to adapt to changing local conditions and needs, providing relevant, evidence-based information. This research result suggests that the implementation of KMS can significantly improve the effectiveness of health policies and intervention programs at reducing stunting, particularly among vulnerable populations. However, questions remain regarding the specific features of the KMS, the implementation strategy within communities, and the evaluation measures for assessing its long-term effectiveness in combating stunting.
Toward an integrated AI-Driven governance architecture for smart cities and digital economy systems Bambang Saras Yulistiawan; Henry Eko Hapsanto; Satriyo Wibowo; Hengki Tamando Sihotang
Indonesia Accounting Research Journal Vol. 13 No. 3 (2026): March: IT Governance, Finance, Accounting, Management
Publisher : Institute of Accounting Research and Novation (IARN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/iacrj.v13i3.665

Abstract

The rapid growth of smart city technologies and digital economy systems has significantly increased the complexity of urban governance, particularly in integrating heterogeneous data sources, supporting intelligent decision-making, and ensuring effective coordination across systems. However, existing approaches often remain fragmented, with limited integration between data infrastructures, artificial intelligence (AI), and governance mechanisms. This study addresses this gap by proposing and evaluating an AI-driven governance architecture designed to integrate smart city systems and digital economy ecosystems into a unified, data-driven framework. This research adopts the Design Science Research (DSR) methodology, encompassing problem identification, objective definition, architecture design, demonstration, evaluation, and communication. The proposed architecture is structured into five interconnected layers: data acquisition, data management, AI intelligence, governance, and service delivery. A demonstration scenario integrating smart mobility and digital economy systems illustrates the operational capabilities of the architecture. The evaluation is conducted using a multi-framework approach, incorporating COBIT, ISO 37120, TOGAF, NIST AI Risk Management Framework, ITIL, and GDPR, combined with expert-based assessment. The results indicate that the proposed architecture achieves a high level of effectiveness, with an overall evaluation score of 4.39, demonstrating strong alignment with governance, architectural, and service requirements. This study contributes by introducing an integrated AI-driven governance model that bridges smart city systems and digital economy ecosystems, enabling adaptive, predictive, and data-driven urban governance. The findings provide both theoretical insights and practical guidance for developing next-generation governance architectures in complex digital environments.
Co-Authors Erly Krisnanik Galih Prakoso Rizky A Hengki Tamando Sihotang Henry Eko Hapsanto I Gede Susramae Kraugusteeliana Kraugusteeliana Muhammad Adrezob Rifka Widyastuti Rr Octanty Mulianingtyas Satriyo Wibowo