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All Journal Martabe : Jurnal Pengabdian Kepada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Sistem Informasi dan Aplikasi SEINASI-KESI Technologia: Jurnal Ilmiah JISA (Jurnal Informatika dan Sains) Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) Community Empowerment International Journal of Basic and Applied Science Jurnal Abmas Negeri (JAGRI) Jurnal Mandiri IT CEBONG Journal International Journal of Enterprise Modelling Engagement: Jurnal Pengabdian Kepada Masyarakat ABDIKOM Jurnal Sistem Informasi dan Aplikasi
Bambang Saras Yulistiawan
Universitas Pembangunan Nasional Veteran Jakarta, Jakarta, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Physics Public Health Social Sciences Transportation Other

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KMS for overcoming stunting in early childhood and pregnant women using the Soft System Methodology (SSM) with the Learning Lesson System (LLS) approach Krisnanik, Erly; Adrezob, Muhammad; Kraugusteeliana, Kraugusteeliana; Yulistiawan, Bambang Saras; Susramae, I Gede
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.
Towards Autonomous Digital Governance: Integrating AI, Data Governance, and Smart Infrastructure for Future Government Bambang Saras Yulistiawan; Galih Prakoso Rizky A
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 6 (2026): Computer Science
Publisher : Institute of Computer Science (IOCS)

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Abstract

The rapid advancement of digital technologies has transformed public governance, evolving from e-government to more integrated digital government systems. However, the transition toward fully autonomous digital governance remains limited. This study aims to analyze how the integration of Artificial Intelligence (AI), data governance, and smart infrastructure can enable the development of autonomous digital governance systems. Using a mixed-method approach, this research combines a systematic literature review and case study analysis with quantitative survey data to examine the relationships between key variables, including AI capability, data governance quality, and infrastructure readiness. The findings indicate that the integration of these components significantly contributes to the formation of an autonomous decision-making system, which in turn enhances governance outcomes in terms of efficiency, transparency, and responsiveness. AI capability emerges as the most influential factor, particularly in enabling automation and predictive analytics, while data governance ensures the reliability and accountability of data-driven processes. Smart infrastructure supports real-time data collection and system connectivity, although disparities in infrastructure readiness remain a challenge. The study also identifies key benefits of autonomous digital governance, including faster decision-making, reduced human bias, and the development of predictive public services. However, several risks are highlighted, such as ethical concerns, privacy issues, and over-reliance on technology. This research proposes an integrated conceptual model of autonomous digital governance, emphasizing the need for synergy between technological and institutional components. The study contributes to the advancement of digital governance theory while providing practical insights for policymakers in designing future-ready governance systems.
A Unified Artificial Intelligence Driven Data Governance Framework for Decision Intelligence in Smart Digital Ecosystems Bambang Saras Yulistiawan
Jurnal Teknik Informatika C.I.T Medicom Vol 18 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

This research proposes a Unified Artificial Intelligence–Driven Data Governance Framework to enhance decision intelligence in smart digital ecosystems. The rapid growth of technologies such as the Internet of Things (IoT), smart cities, and digital platforms has led to an exponential increase in data volume and complexity, creating challenges related to data silos, poor data quality, lack of governance standards, and ineffective decision-making. While artificial intelligence (AI) has been widely adopted to address analytical needs, existing approaches often fail to integrate data governance with AI-driven decision processes, resulting in unreliable and less transparent outcomes. To address this gap, this study develops a multi-layered framework that integrates data governance, AI, and decision intelligence into a unified architecture. The proposed framework consists of a data layer, governance layer, AI layer, decision layer, and application layer, supported by key components such as data integration modules, data quality engines, policy enforcement mechanisms, AI model management, and decision support systems. A prototype-based methodology is employed to evaluate the framework using machine learning models and optimization techniques within simulated smart ecosystem environments. The results demonstrate that the proposed framework significantly improves decision accuracy, data quality, and system reliability while maintaining acceptable processing time and scalability. Compared to traditional systems and non-governed AI models, the framework provides enhanced transparency, accountability, and compliance. However, challenges related to computational cost, system complexity, scalability, and ethical considerations such as bias and fairness remain. This research contributes to the field by presenting a comprehensive and scalable solution that bridges the gap between AI and data governance.
Co-Authors Adrezob, Muhammad Andhika Octa Indarso Bambang Yuwono Catur Nugrahaeni Catur Nugrahaeni Puspita Dewi Erly Krisnanik Fitri Nur Masruriyah, Anis Galih Prakoso Rizky A Galih Prakoso Rizky A Hananto, Bayu Handayani, Lusia Hengki Tamando Sihotang Hengki Tamando Sihotang Hesti Indriana, Intan Intan Hesti Indriana Kharisma Wiati Gusti Kraugusteeliana Kraugusteeliana Mulianingtyas, RR Octanty Niqotaini, Zatin PD, Catur Nugrahaeni Pinastika, I Wayan Rangga Prasetyo, Rizky Tito Rahmawan, Fauzan Ahmat Rifka Widyastuti Rifka Widyastuti, Rifka Rio Wirawan Rizky A, Galih Prakoso RR Octanty Mulianingtyas Supriyanto Susramae, I Gede Tiana, Ade Hikma Titan Parama Yoga Tjahjanto, Tjahjanto Tri Wahyono , Bambang Wirawan, Rio Zaidiah, Ati