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All Journal West Science Business and Management Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia Ascendia: Journal of Economic and Business Advancement Journal of Economic Studies Mercatura Lumina: Journal of Management, Entrepreneurship, and Tourism Technema: Journal of Intelligent Engineering and Computing Capitalis: Journal of Economic Stability, Banking, and Investment
Maiza Fikri
Institut Teknologi dan Bisnis Bina Sriwijaya Palembang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : technema journal of intelligent engineering and computing

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Explainable Artificial Intelligence for Engineering Decision Support Systems Rezi Munizar; Ahmad Budi Trisnawan; Maiza Fikri; Tarma Tarma; Eko Sutrisno
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 2 (2026): : June: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

Explainable Artificial Intelligence (XAI) has become increasingly important in Engineering Decision Support Systems because growing algorithmic complexity often reduces transparency, accountability, and practitioner confidence despite substantial improvements in predictive capability. This study develops a conceptual framework that integrates engineering data processing, artificial intelligence inference, explainability mechanisms, and human-centered decision support within a unified multilayer architecture. A non-empirical system design methodology based on design-oriented systems research, architecture-based evaluation, and scenario-driven analytical simulation is employed to examine architectural consistency, interoperability, traceability, and explainability across representative engineering contexts. The analytical results indicate that explanation fidelity, interpretability, transparency, traceability, and modular scalability function as complementary engineering quality attributes that collectively strengthen trustworthy decision support while preserving logical consistency between engineering evidence and computational reasoning. The proposed architecture also demonstrates technology independence and adaptability across heterogeneous engineering domains through explicit modular interactions and standardized information flows. This study contributes an architecture-centered perspective that advances theoretical understanding of explainable engineering intelligence while providing a reproducible conceptual foundation for future empirical implementation, quantitative validation, and standardized evaluation of trustworthy Engineering Decision Support Systems.  
Co-Authors Adebowale I. O. Anggun Wida Prawira Anna Chofyfa Efniar Efniar Eko Sutrisno Faridatul Munawaroh Hendra Prasetyo Hendriansyah Hendriansyah Januar Ida Wibowati Leonardus Teguh Handoyo Martini Martini Mohammad Arfani Nadiah Faradhilla Nita Novianti Rezi Munizar Sahal Hanafi Tarma Toman Sony Tambunan Trisnawan, Ahmad Budi Yuliza Aryani