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All Journal Building of Informatics, Technology and Science Jurnal Ar Ro'is Mandalika (Armada)
Subekthi, Errysa
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Religion Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics

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The Role of Data Governance and Ethical AI in Strengthening Reliability of Healthcare Information Systems Subekthi, Errysa; Fahreza, Muhammad Rizqi
Jurnal Ar Ro'is Mandalika (Armada) Vol. 6 No. 1 (2026): JURNAL AR RO'IS MANDALIKA (ARMADA)
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59613/armada.v6i1.5458

Abstract

The increasing reliance on digital technologies in healthcare has underscored the importance of robust information systems for ensuring the delivery of high-quality patient care. As healthcare systems adopt artificial intelligence (AI) to support decision-making, the role of data governance and ethical AI has become increasingly vital in maintaining the reliability and trustworthiness of these systems. Data governance involves the frameworks, processes, and standards for managing the quality, integrity, and security of healthcare data, while ethical AI ensures that the algorithms driving healthcare decisions are transparent, fair, and free from biases. This paper explores the crucial role that data governance and ethical AI play in enhancing the reliability of healthcare information systems. It highlights key challenges such as data privacy concerns, algorithmic biases, and accountability, and discusses strategies for improving transparency, data stewardship, and decision-making frameworks. The research further examines the potential impacts of poor data governance and unethical AI practices on patient outcomes, trust in healthcare systems, and overall system efficiency. Through a review of current trends and best practices, this study aims to provide actionable insights for healthcare providers, policymakers, and technology developers in strengthening the integrity and effectiveness of healthcare information systems.
Perbandingan Metode Certainty Factor dan Dempster-Shafer dalam Sistem Pakar Diagnosa Penyakit THT Subekthi, Errysa
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8623

Abstract

The diagnosis of Ear, Nose, and Throat (ENT) diseases often encounters difficulties in determining the level of certainty of a disease based on the symptoms experienced by the patient. The main issue in this study is how to compare the accuracy levels between the Certainty Factor and Dempster-Shafer methods in expert systems for diagnosing ENT diseases. As a solution, this research applies both methods and analyzes their computational results based on various symptoms entered by patients. The objective of this study is to identify which method is more effective in providing diagnostic certainty. The findings indicate that the Certainty Factor method produces a higher level of certainty compared to the Dempster-Shafer method — for instance, in the case of tonsillitis, achieving 94.68% compared to only 0.02% with Dempster-Shafer. Therefore, the Certainty Factor method is recommended for use in expert systems for ENT disease diagnosis. This study contributes to enhancing understanding of the application of artificial intelligence methods in the medical field, particularly in improving the accuracy of expert systems to assist healthcare professionals in diagnostic decision-making processes.
Co-Authors Fahreza, Muhammad Rizqi