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JOT
Published by Sinar Mentari Sundara
ISSN : 31632653     EISSN : 31632645     DOI : https://doi.org/10.68012/jot
Core Subject :
Journal of Orange Technology (JOT) is an international, quarterly, open-access journal. It is positioned to be indexed by Scopus in the categories of Computer Science (Applications), Engineering (Biomedical), Psychology (Applied), and Social Sciences (Health). Aspiring to achieve a Q1 ranking in Scopus across these interdisciplinary fields, JOT aims to establish itself as a premier global platform for humanistic innovation. The journal delivers groundbreaking, high-quality research at the dynamic crossroads of technology, empathy, and societal care. Designed for academicians, graduate students, industry practitioners, policymakers, and interdisciplinary researchers in fields like engineering, psychology, and ethics, JOT provides an unparalleled venue for sparking transformative ideas and collaborative discussions. Every submission undergoes a meticulous double-blind peer-review process, ensuring rigor, integrity, and credibility, with all accepted works made freely accessible online as open-access content to inspire a worldwide audience and maximize global impact.
Arjuna Subject : -
Articles 24 Documents
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Human Centered Artificial Intelligence Through GDPR Compliant Data Science Workflow Implementation Nuke Puji Lestari Santoso; Dewi Khairani; Siti Ummi Masruroh; Agung Rizky; Aman Jaiswal
Journal of Orange Technology Vol. 3 No. 1 (2026): October
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/jot.v3i1.96

Abstract

The rapid advancement of Artificial Intelligence (AI) and data science technologies has transformed organizational decision-making across healthcare, finance, and digital services. Despite these advancements, the increasing use of personal data has intensified concerns regarding privacy, transparency, ac countability, and public trust. Building trustworthy Human-Centered AI requires data science workflows that integrate regulatory compliance, ethical principles, and responsible governance throughout the AI lifecycle. This study aims to examine how GDPR-compliant data science workflow implementation supports the development of trustworthy Human-Centered AI through a qualitative and practice-oriented research approach. The study synthesizes evidence from recent literature, industry case studies, expert perspectives, and governance oriented analytical frameworks to identify effective strategies for integrating privacy by design, data minimization, transparency, accountability, and privacy-preserving techniques, including anonymization, pseudonymization, and differential privacy, into data science workflows. The findings indicate that successful implementation depends not only on technical safeguards but also on strong organizational governance, continuous compliance monitoring, and cross-functional collaboration among legal, technical, and managerial stakeholders. Furthermore, the integration of explainable and governance-aware machine learning models improves transparency, strengthens stakeholder trust, and supports responsible human-centered AI without significantly reducing analytical performance. This study proposes a structured GDPR-compliant data science workflow framework that enables organizations to balance analytical effectiveness, regulatory compliance, and human-centered principles while fostering trustworthy, transparent, and sustainable Artificial Intelligence for real-world digital innovation.
Enhancing Human Trust Through Explainable AI Communication for Nontechnical Stakeholders Dwi Apriliasari; Bintang Nandana Henry; Alexander Williams
Journal of Orange Technology Vol. 3 No. 1 (2026): October
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/jot.v3i1.101

Abstract

The rapid adoption of Artificial Intelligence (AI) in data driven decision making has increased the complexity of analytical models, creating significant communication barriers between technical experts and nontechnical stakeholders. Limited understanding of AI generated insights often reduces trust, delays decision making, and restricts the effective adoption of AI supported recommendations. As organizations increasingly rely on explainable and human centered AI systems, effective communication has become essential to ensuring that AI out- puts are accessible, transparent, and meaningful for diverse stakeholder groups. This study aims to identify the key challenges in communicating complex AI model results and to develop practical communication strategies that enhance human trust among nontechnical stakeholders. A qualitative research design was employed using case studies, open ended surveys, expert interviews, and focus group discussions involving data scientists and nontechnical decision makers from business organizations. The collected data were analyzed through thematic analysis to identify recurring communication barriers and effective explanatory practices. The findings reveal that technical jargon, cognitive overload, and limited contextual explanations are the primary factors reducing stakeholder trust in AI generated insights. Conversely, explainable AI communication supported by intuitive data visualization, contextual storytelling, simplified summaries, and audience centered messaging significantly improves understanding, transparency, and stakeholder confidence across organizational contexts. The study proposes a human centered communication framework that strengthens trust in AI assisted decision making while promoting more inclusive and responsible technology adoption. These findings contribute to explainable AI research by demonstrating how effective communication can bridge the gap between technical complexity and human understanding, thereby generating meaningful humanistic impacts in organizational decision making and sustainable organizational innovation.
Explainable AI Supporting Responsible Human AI Interaction in Intelligent Decision Support Systems Cut Amalia Saffiera; Takumi Sase; Qurotul Aini; Danny Manongga; Harry Agustian
Journal of Orange Technology Vol. 3 No. 1 (2026): October
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/jot.v3i1.105

Abstract

The rapid adoption of Artificial Intelligence (AI)-enabled Intelligent Decision Support Systems (IDSS) has transformed decision-making across multiple sectors. However, increasing AI complexity often limits users’ understanding of recommendation processes, creating challenges for responsible human-AI interaction. Existing studies mainly emphasize explainability and transparency from technical perspectives while providing limited evidence regarding their influence on responsible interaction and decision quality. This study investigates the effects of Perceived Explainability and Perceived Transparency on Responsible Human-AI Interaction and Decision Quality, including the mediating role of Responsible Human-AI Interaction. Grounded in the Human-AI Teaming perspective and the Responsible AI Framework, this quantitative study employs a survey of 180 respondents with experience using AI-enabled Intelligent Decision Support Systems. Data will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The study is expected to demonstrate that explainability and transparency strengthen responsible human-AI interaction, which subsequently enhances decision quality. These findings are expected to enrich responsible AI literature and provide practical guidance for designing transparent, human-centered Intelligent Decision Support Systems that promote trustworthy decision-making and reinforce the humanistic values underpinning intelligent technologies
Humanizing Data Science Through Domain Expertise for Ethical and Trustworthy Artificial Intelligence Marviola Hardini; Nur Azizah; Syahla Naurah; Rendhika Adyatama; Souza Nurafrianto Windiartono Putra
Journal of Orange Technology Vol. 3 No. 1 (2026): October
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/jot.v3i1.111

Abstract

The rapid adoption of Artificial Intelligence (AI) has transformed data-driven decision making across healthcare, finance, education, and public services. De spite these advances, AI systems continue to face challenges related to algorithmic bias, limited contextual understanding, insufficient transparency, and declining user trust, highlighting the need to integrate human expertise throughout the data science process. This study aims to systematically examine the role of domain expertise in Humanizing Data Science for the development of Ethical and Trustworthy Artificial Intelligence. A Systematic Literature Review (SLR) was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Peer-reviewed studies published be tween 2021 & 2026 were identified, screened using predefined inclusion and exclusion criteria, and analyzed through thematic synthesis. The review identifies five major themes, namely Human-Centered Artificial Intelligence, Domain Expertise Integration, Ethical and Trustworthy AI, Explainability and Human Oversight, and Humanizing Data Science. Based on these findings, this study proposes a Humanizing Data Science Framework integrating domain expertise throughout the AI lifecycle. The framework demonstrates that combining tech nical capabilities with human knowledge supports transparent, fair, accountable, trustworthy, and human-centered AI, providing valuable practical and theoretical guidance for researchers, practitioners, organizations, policymakers, and future interdisciplinary innovation initiatives worldwide.

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