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All Journal Jurnal Impresi Indonesia Eduvest - Journal of Universal Studies
Nurcholis Ramlan
Universitas Indonesia
Religion Aerospace Engineering Computer Science & IT Economics, Econometrics & Finance Education Health Professions Law, Crime, Criminology & Criminal Justice Neuroscience Social Sciences

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A Systematic Literature Review of Supporting Factors for Big Data Analytics (BDA) in Public Sector Auditing Retisa Heryati Siwi; Gesi Deta Hendika Wardani; Dana Indra Sensuse; Sofian Lusa; Nurcholis Ramlan
Eduvest - Journal of Universal Studies Vol. 6 No. 7 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i7.52986

Abstract

The application of Big Data Analytics (BDA) in auditing offers significant benefits, including increased accountability and transparency, as well as reduced operational costs. BDA is also expected to improve the quality and reliability of audit results used for decision-making. The role of BDA in public sector auditing is crucial, as it helps detect anomalies or fraud, enhance oversight, and evaluate implemented policies. Despite its benefits, the application of BDA in public sector auditing still faces various challenges that need to be addressed. This study aims to analyze the factors that support the implementation of BDA in public sector auditing and identify the challenges encountered during its implementation. This research uses a systematic literature review (SLR) approach with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The study also employs the Content Validity Index (CVI) to validate the relevance of the identified factors and their classification. The results reveal eight factors that support the use of BDA in public sector auditing: perceived organizational benefits; process management; data privacy, security, and governance; data quality; people aspects; auditor aspects; organizational aspects; and systems, tools, and technologies. Public sector auditing needs to consider these factors when implementing BDA to improve audit effectiveness, efficiency, and the quality of oversight. Proper implementation of BDA can strengthen transparency and accountability in public financial management and policy oversight.
Enhancing Master Data Management Maturity: A Case Study of Institution XYZ Gesi Deta Hendika Wardani; Retisa Heryati Siwi; Dana Indra Sensuse; Sofian Lusa; Nurcholis Ramlan
Eduvest - Journal of Universal Studies Vol. 6 No. 7 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i7.53117

Abstract

Data has become a strategic asset that supports decision-making in the digital era. Master Data Management (MDM) is used to assure quality, accuracy, and consistency of master data. However, government electronic certification services face challenges related to data inconsistencies due to the use of two applications with separate databases. This study assessed the MDM maturity level in government electronic certification services (Institution XYZ) using the Spruit & Pietzka Master Data Management Maturity Model (MD3M). It then provided improvement recommendations aligned with the Data Management Body of Knowledge (DMBOK). The research applied five domains: data model, data quality, use and ownership, data protection, and maintenance, encompassing 62 required capabilities. Data were collected through interviews with the data management team. The results indicated that 69.36% of the capabilities in the MD3M model had been implemented.This study identified areas for improvement in master data management within government electronic certification services and provided strategic recommendations to enhance data management effectiveness. This approach is expected to support more effective, secure, and standardized data management in accordance with organizational and regulatory requirements.
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.
Co-Authors Dana Indra Sensuse Gesi Deta Hendika Wardani Iindra Iriyanti Isnina Eva Hidayati Muhammad Rizki Mutia Sayyidah Retisa Heryati Siwi Sofian Lusa