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All Journal Al-MIKRAJ: Jurnal studi Islam dan Humaniora Integrated System and Management Technology
Rizal Rachman
Universitas Komputer Indonesia
Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Epistomologi Sains di Era Kecerdasan Buatan: Menimbang Kebenaran Prediktif Popon Dauni; Rizal Rachman; Sri Erina Damayanti; Agus Nursikuwagus; Usep Mohamad Ishaq; Andrias Darmayadi
AL-MIKRAJ Jurnal Studi Islam dan Humaniora (E-ISSN 2745-4584) Vol. 6 No. 1: Al-Mikraj, Jurnal Studi Islam dan Humaniora
Publisher : Pascasarjana Institut Agama Islam Sunan Giri Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37680/almikraj.v6i1.8879

Abstract

The development of artificial intelligence (AI), particularly machine learning and deep learning, has brought significant changes to contemporary scientific practices. AI no longer functions solely as a computational tool, but plays an active role in the production, validation, and evaluation of scientific knowledge through data modelling and probabilistic inference. This development raises fundamental questions in the philosophy of science, particularly regarding the shift in the concept of scientific truth from the paradigm of empirical verification and causal explanation towards an approach based on prediction, mathematical approximation, and the management of uncertainty. This research aims to re-evaluate the status of scientific truth in the age of AI by philosophically analysing the relationship between uncertainty, computational knowledge, and scientific truth claims generated by AI models. The research method used is a qualitative study based on literature review and conceptual analysis of contemporary science and technology philosophy literature. The study results indicate that the integration of AI into scientific practice is driving a shift in the epistemology of science from a verifiative orientation towards a predictive epistemology that emphasises model reliability and instrumental validity. This research concludes that scientific truth in the AI era is more contextual and pragmatic, thus demanding an adaptive, reflective, and interdisciplinary framework for the epistemology of science. Theoretically, scientific truth in the age of artificial intelligence is more contextual, thus requiring an adaptive, reflective, and interdisciplinary framework for the epistemology of science as its main theoretical contribution.
A Design Science Roadmap for Auditable Ocular Disease Classification: Evidence Mapping of AI Governance Gaps Rizal Rachman; Eddy Soeryanto Soegoto; Irawan Afrianto; Irfan Dwiguna sumitra; Zainal Arifin Hasibuan
Integrated System and Management Technology Vol. 1 No. 2 (2026): July: Integrated System and Management Technology
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/ismat.v1i2.442

Abstract

The integration of Artificial Intelligence (AI) into ocular diagnostics has led to substantial improvements in predictive accuracy. However, a persistent gap remains between technical performance and clinical accountability. The present study addresses the "accuracy trap" and the lack of transparency in current deep learning models for ocular disease classification. The objective of the research is twofold: firstly, to identify methodological deficiencies in extant literature and, secondly, to propose a standardised evaluative framework to ensure model auditability. A systematic evidence mapping (SEM) approach, combined with design science research methodology (DSRM), was utilised to scrutinise 10 high-impact Scopus-indexed studies published between 2023 and 2026. The findings reveal a critical "predictive validity gap," where 80% of the evidence base relies on aggregate accuracy while 90% remains "black box" without functional Explainable AI (XAI) layers. The synthesis of these gaps resulted in the formulation of a conceptual roadmap that mandated multi-metric evaluation, incorporating Cohen's Kappa, and pathophysiological traceability. In conclusion, this research establishes that clinical deployment of AI must transition from model-centric success to a governance-oriented paradigm that prioritises decision utility and auditable audit trails. This roadmap provides a rigorous blueprint for the future implementation of transparent and accountable medical AI systems.
Co-Authors Agus Nursikuwagus Andrias Darmayadi Eddy Soeryanto Soegoto Irawan Afrianto Irfan Dwiguna Sumitra Popon Dauni Sri Erina Damayanti Usep Mohamad Ishaq Zainal Arifin Hasibuan