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All Journal Edunesia : jurnal Ilmiah Pendidikan Global Science: Journal of Information Technology and Computer Science Journal of Artificial Intelligence and Legal Technology
Deny Prasetyo
Universitas Sugeng Hartono
Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Trust Centric Machine Learning Framework for Secure Decision Making in Decentralized Digital Service Ecosystems Deny Prasetyo; Siska Narulita; Ahmad Jurnaidi Wahidin; Rosalina Yani Widiastuti; Suyahman Suyahman; Very Dwi Setiawan; Agus Wantoro
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.197

Abstract

This study introduces a trust centric machine learning framework designed to improve decision making reliability and security in decentralized digital service ecosystems. Traditional machine learning models often focus on accuracy and efficiency but fail to address the challenges of trust and security in decentralized environments. In contrast, the proposed framework integrates dynamic trust indicators and employs Federated Learning (FL) to ensure privacy while enhancing decision making performance. The framework also incorporates Zero Knowledge Proofp based Verifiable Machine Learning (ZKP-VML), which ensures transparency and security without compromising sensitive data. Through continuous real time trust assessments, the framework adapts to changing conditions, improving the accuracy and reliability of decisions in environments where participants may not fully trust each other. The application of this framework in autonomous vehicles and IoT networks demonstrated its ability to make robust, secure decisions, even in complex and uncertain scenarios. The framework’s ability to incorporate both trust and security into its decision making processes sets it apart from traditional models, which typically do not address the trustworthiness of data or participants. This research highlights the importance of integrating trust and security into machine learning models, particularly in decentralized systems, and offers a robust solution to trust management challenges. However, challenges such as scalability and computational efficiency remain, and future work should focus on enhancing these aspects, along with exploring the framework's applicability in other decentralized domains like finance or supply chain management. The integration of privacy preserving technologies and improvements in adversarial robustness are also potential areas for future research.
Principal Innovation in Managing Budgetary Autonomy: Evidence from a Narrative Review Using the Saber Framework Faridatus Shofiyah; Sri Sumarni; Ardiyan Eko Saputro; Vinda Puspitasari; Deny Prasetyo
Edunesia : Jurnal Ilmiah Pendidikan Vol. 7 No. 2 (2026)
Publisher : Research, Training and Philanthropy Institution Natural Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51276/edu.v7i2.1642

Abstract

Vocational education in Indonesia has transformed the implementation of budgetary autonomy in vocational high schools (SMKs), requiring principals to manage resources strategically and accountably. However, most studies emphasise administrative compliance with funding regulations, and few synthesize principals' innovation within the School Autonomy and Accountability (SABER) framework. This study analyses principals' innovation strategies in managing SMK budgetary autonomy via a narrative literature review. Twelve articles (2022–2025) were thematically synthesized. Findings show three dominant innovation patterns: revenue diversification through internal business units such as BLUD and teaching factories; financing efficiency via strategic industry partnerships; and strengthened accountability through digital internal control systems. These innovations suggest that effective budgetary autonomy depends less on funding magnitude and more on principals' entrepreneurial leadership capacity to balance autonomy with strict accountability. The study contributes a conceptual synthesis of SABER in vocational contexts and recommends capacity-building for principals and adaptive regulatory measures to support innovation, particularly in remote and resource-constrained settings, for wider validation.
An Analysis of the Impact of Social Media Addiction on Students’ Academic Performance Using K-Means and Decision Tree Dewi Sayekti Sutrisni; Maulana Ilham Alisyahbana; Muhammad Luqman Al-hakim; Deny Prasetyo
Journal of Artificial Intelligence and Legal Technology Vol. 1 No. 1 (2025): August 2025
Publisher : Sah Publisher

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Abstract

This study aims to analyze the relationship between social media addiction levels and students' academic performance. With the growing use of social media among university students, concerns have emerged regarding its potential negative impact on academic achievement. The data were obtained from the "Social Media Addiction vs Relationships" dataset and analyzed using two machine learning approaches: K-Means to classify groups based on usage hours and academic impact, and Decision Tree to predict academic satisfaction levels based on digital behavior. The findings reveal distinct clustering patterns that differentiate students based on their addiction levels and academic performance. The Decision Tree model achieved 100% accuracy on the test data in classifying the impact of social media use. These results highlight that daily usage hours and addiction scores are significant contributing factors. Based on these insights, the study recommends implementing digital intervention programs on campus to help mitigate the negative effects of social media addiction.
Co-Authors Agus Wantoro Ahmad Jurnaidi Wahidin Ardiyan Eko Saputro Dewi Sayekti Sutrisni Faridatus Shofiyah Maulana ilham Alisyahbana Muhammad Luqman Al-hakim Siska Narulita Sri Sumarni Suyahman Suyahman Very Dwi Setiawan Vinda Puspitasari Widiastuti, Rosalina Yani