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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) bit-Tech Jurnal Impresi Indonesia Proceeding of International Conference Health, Science And Technology (ICOHETECH) Journal of Business, Social and Technology Eduvest - Journal of Universal Studies Jurnal Ragam Pengabdian
Sofian Lusa
Universitas Indonesia
Religion Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Neuroscience Social Sciences Other

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Utilization of Artificial Intelligence in Government Hospital Information Systems: A Systematic Review Isnina Eva Hidayati; Sofian Lusa; Iindra Iriyanti; Nurcholis Ramlan; Dana Indra Sensuse
Jurnal Impresi Indonesia Vol. 5 No. 2 (2026): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v5i2.7588

Abstract

The use of Artificial Intelligence (AI) in healthcare continues to expand. Hospital Information Systems (HIS) play a crucial role in managing clinical and operational data within hospitals. With advancements in technology, the integration of AI into HIS is gaining increasing attention due to its potential to enhance efficiency, accuracy, and the overall quality of healthcare services. Currently, government hospitals face various challenges in delivering public health services, including lengthy administrative processes, limited medical personnel, and the growing need for faster, data-driven clinical decision-making. This study focuses on analyzing the role of AI in supporting HIS development in government hospitals, with the objective of improving efficiency, accuracy, and service quality. Using a Systematic Literature Review (SLR) approach, the study collects, evaluates, and analyzes recent literature on the application of AI within HIS in government hospitals, particularly in areas such as patient registration, diagnostic support, electronic medical record management, and digital triage systems. The expected outcome of this study is a more comprehensive understanding of how AI can improve hospital operational efficiency while enhancing the quality of patient experiences, especially within public healthcare contexts. In addition, the study identifies key challenges in implementing AI within HIS, including limited system interoperability, the need for stronger health data security and regulatory frameworks, and insufficient human resource readiness. Therefore, this research is expected to provide meaningful contributions to policymakers, system developers, and government hospitals in designing digital transformation strategies for public health services that are smarter, safer, and more patient-oriented.
Factors Influencing Generative AI Adoption in Government: A Case Study in BPS-Statistics of Indonesia Mutia Sayyidah; Sofian Lusa; Muhammad Rizki; Nurcholis Ramlan; Dana Indra Sensuse
Jurnal Impresi Indonesia Vol. 5 No. 4 (2026): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v5i4.7666

Abstract

Rapid technological developments hold great potential, one of which is generative AI. Technology that is easily accessible and user-friendly tends to spread quickly, and BPS-Statistics of Indonesia is no exception. The challenges currently faced by BPS-Statistics of Indonesia, such as rapid data growth, high data demand, and data analysis and representation, encourage the institution to be adaptive to new technologies that can accelerate work processes. This research aims to determine the factors influencing the acceptance and use of generative AI (GenAI), such as ChatGPT, Gemini, and others, among BPS-Statistics of Indonesia employees, using Behavioral Intention as the central mediating variable that bridges the influence of these predictor factors on Use Behavior. The model also examines the relationships between external factors, such as Social Influence and Trust, and Perceived Usefulness and Perceived Ease of Use, as well as their effects on Attitude. Additionally, it evaluates the influence of Hedonic Motivation, Facilitating Conditions, Perceived Severity, and Perceived Vulnerability on Behavioral Intention. Based on a survey of 166 respondents at BPS-Statistics of Indonesia, the results reveal that Attitude has a significant influence on Behavioral Intention, while Perceived Severity has a significant negative influence on Behavioral Intention. Furthermore, Behavioral Intention is also shown to have a significant positive influence on Use Behavior. These findings contribute theoretically to the development of technology adoption models in the public sector and have practical implications for BPS-Statistics of Indonesia in formulating AI usage policies.
Identifying Key Components of Knowledge Management Strategy in Government: A Systematic Literature Review Sabrina Editha Putri; Irni Irmayani; Dana Indra Sensuse; Sofian Lusa; Nadya Safitri
Journal of Business, Social and Technology Vol. 7 No. 1 (2026): Journal of Business, Social and Technology
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jbt.v7i1.579

Abstract

Background: Amid digital transformation and the demand for adaptive public governance, Knowledge Management (KM) has become a strategic asset for government agencies. Previous studies have examined individual KM success factors—such as leadership, organizational culture, or technology readiness—yet most remain fragmented, case-specific, and lack an integrated strategic framework tailored to public sector governance. Objective: This study aims to identify key components and effective strategies for implementing KM in government organizations through a Systematic Literature Review (SLR) using the PRISMA 2020 framework. Methods: A total of 600 articles were screened from five leading scientific databases, resulting in 20 eligible studies for in-depth analysis. The review addresses two questions: (1) What are the key components of KM in government. (2) What strategies effectively support KM implementation in the public sector. Results: KM success in government rests on two interrelated domains: KM Foundation (leadership, organizational culture, structure, readiness, and technological infrastructure) and KM Solution (knowledge capture, sharing, discovery processes, regulatory mechanisms, and user-friendly systems). Nine strategic implementation areas were identified, including transformational leadership, human capital development, technology integration, performance alignment, and regulatory strengthening. Unlike prior studies that examined KM components separately, this research integrates fragmented findings into a structured and strategic framework combining foundational and operational dimensions. The study contributes theoretically by conceptualizing KM as a strategic governance capability and practically by offering policy-relevant guidance for strengthening adaptive, collaborative, and knowledge-driven public sector reform. Conclusion: An integrated and strategically aligned KM approach is essential for sustainable and effective public governance.
When the Coach Is a Screen: Tacit Knowledge Transformation in AI-Powered Marathon Coaching Dewi Mutiara Nurani; Dana Indra Sensuse; Sofian Lusa
Jurnal Ragam Pengabdian Vol. 3 No. 2 (2026): Mei-Agustus, Sustainable Development Goals (SDGs): Multidisciplinary Perspectiv
Publisher : Lembaga Teewan Journal Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/sjw8e348

Abstract

The increasing use of AI-powered running coaching applications in marathon training has raised questions about whether AI can replace the tacit knowledge of human coaches. This study explores how tacit knowledge changes as coaching shifts from human coaches to AI-powered coaching systems. An exploratory qualitative approach was adopted through semi-structured interviews with eight marathon runners and two running coaches who had experience using AI-powered running coaching applications. The interview data were analyzed using Braun and Clarke's six-phase reflexive thematic analysis. The findings show that AI is effective in supporting data driven coaching tasks. However, AI still has limitations in understanding athletes' emotional conditions, personal circumstances, and contextual factors that influence coaching decisions that remain difficult for AI to replicate. These findings suggest that future AI-powered running coaching systems should be designed to support rather than replace human coaches by considering athletes' contextual and emotional conditions, providing features that assist coaches in decision-making, and encouraging collaboration between AI and human coaches to improve marathon training. 
Co-Authors Agnes Agnes Aji Baskoro Ajie Tri Hutama Arif Wahyudi Asep Suherman Assaf Arief Damayanti Elisabeth Dana Indra Sensuse Darmawan, Rizqi Dewi Mutiara Nurani Didi Kurnaedi Gesi Deta Hendika Wardani Hanif Sudira Iindra Iriyanti Irni Irmayani Isnina Eva Hidayati Khalid Jasir Mirza Triyuna Putra Muhammad Rizki Mutia Sayyidah Nadya Safitri Nur Indrawati Nurcholis Ramlan Prasetyo Adi Putra Tresna Linge Retisa Heryati Siwi Rizki Kurniawan Sabrina Editha Putri Tommy Putra Pratama Gunawan Tsaqif Alfatan Nugraha