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All Journal International Transactions on Artificial Intelligence (ITALIC)
Adam Faturahman
Alfabet Inkubator Indonesia
Computer Science & IT Control & Systems Engineering Library & Information Science

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Decision Reliability Evaluation of AI Expert Systems in High Impact Domains Zubair Ahmad; Adam Faturahman; Meriyana Sunengsih; Noah Rangi
International Transactions on Artificial Intelligence Vol. 4 No. 2 (2026): May
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i2.1074

Abstract

AI expert decision support systems are increasingly used in public administration, healthcare, and financial risk management, yet conventional accuracy centered evaluations often fail to capture whether systems produce stable decisions across repeated executions. This study aims to develop a reliability oriented evaluation framework for assessing AI expert decision support systems beyond single-run predictive performance. The focus of the study is decision reliability in high-impact AI applications where inconsistent outputs may reduce accountability, weaken institutional trust, and create governance risks. A repeated experimental evaluation approach was applied using recent datasets from 2022 to 2024 representing heterogeneous and imbalanced decision conditions. The proposed framework integrates decision stability measurement, interpretability consistency assessment, confidence interval analysis, and statistical significance testing to examine system behavior under realistic operational scenarios. The results show that models with comparable predictive accuracy can demonstrate statistically significant differences in decision reliability. Confidence interval analysis indicates meaningful variability in output consistency, while interpretability evaluation reveals uneven explanatory stability across model executions. These findings confirm that reliability-oriented evaluation provides a more comprehensive and policy-relevant assessment of AI expert systems than accuracy-based evaluation alone. The study contributes to responsible AI deployment by offering an evaluation perspective that strengthens technical assessment, governance accountability, and trustworthiness in high-impact decision environments.
Assessing User Satisfaction in Hadirku Through an Extended TAM Framework Aswadi Jaya; Henry Zainarthur; Apriani Sijabat; Aulia Rahma Dina; Adam Faturahman
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.937

Abstract

The rapid advancement of Information and Communication Technology (ICT) has accelerated the transition from manual, paper based attendance systems toward digital platforms that promote efficiency and environmental sustainability through reduced paper usage. In this context, the Hadirku online attendance platform has been increasingly adopted across educational, organizational, and eventmanagement settings. This study employs the Technology Acceptance Model (TAM), extended with Service Quality, Organizational Support, and Information Security, to examine determinants of User Satisfaction and Continued Usage. A quantitative design was implemented with 200 valid respondents, and SmartPLS was used to assess construct validity and structural relationships. Reliability was strong (Cronbach’s α = 0.77–0.92), and model fit met recommended thresholds (SRMR = 0.057; NFI = 0.91). The study aims to analyze how perceived usefulness, ease of use, service quality, information security, and organizational support influence user engagement with Hadirku. Findings reveal that information security and perceived usefulness significantly predict continued usage intention, while perceived ease of use and organizational support enhance user satisfaction. Users overall reported positive experiences and strong behavioral intention to continue using the platform. This study contributes to digital transformation and Green ICT literature by providing an extended TAM framework that explains sustained engagement with online attendance systems. The results offer practical insights for platform developers and institutions seeking to optimize user trust, system reliability, and sustainable administrative practices.
Co-Authors Apriani Sijabat Aswadi Jaya Aulia Rahma Dina Henry Zainarthur Meriyana Sunengsih Noah Rangi Zubair Ahmad