Article Per Year (5 Year)
p-Index From 2020 - 2025
0.882
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Information Systems and Informatics Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Conference Proceedings International Conference on Education Innovation and Social Science
Nugraha, Arya Adhi
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Python For Digital Forensics With Daubert Standard Nugraha, Arya Adhi; Kristomo, Domy
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 2 (2024): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i2.792

Abstract

Digital forensics plays a crucial role in modern investigations, where digital evidence often holds the key to solving complex cases. Python, with its versatility and extensive libraries, has emerged as a powerful tool in the realm of digital forensics. This journal explores the integration of Python into digital forensic practices, focusing on its application in conjunction with the Daubert Standard, a legal criterion for the admissibility of expert testimony. The journal begins by outlining the fundamentals of digital forensics, discussing its methodologies and tools. It then delves into the utility of Python in digital forensic investigations, highlighting key libraries and demonstrating its capabilities through practical examples. Furthermore, the journal provides an overview of the current trends in worldwide forensics, emphasizing the increasing reliance on digital evidence and the growing demand for skilled digital forensic practitioners. It explores how advancements in technology and the proliferation of digital devices have expanded the scope and complexity of forensic investigations on a global scale. A thorough examination of the Daubert Standard follows, elucidating its criteria and implications within the legal context of digital forensics. Drawing upon real-world cases, the journal illustrates the application of the Daubert Standard in assessing the reliability and validity of digital forensic evidence. Furthermore, the journal explores the symbiotic relationship between Python and the Daubert Standard, elucidating how Python scripts and methodologies can be designed to meet the rigorous standards of admissibility and reliability mandated by Daubert. Best practices for utilizing Python in a manner consistent with legal requirements are presented, emphasizing the importance of transparency, reproducibility, and peer review. In conclusion, this journal provides insights into the convergence of Python programming, digital forensics, and legal standards, offering a comprehensive framework for practitioners to navigate the complexities of digital investigations while ensuring the integrity of evidence under the Daubert Standard
Integrating ISO 27001 and Indonesia's Personal Data Protection Law for Data Protection Requirement Model Nugraha, Arya Adhi; Nasyuha, Asyahri Hadi
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.754

Abstract

This research explores the integration of ISO/IEC 27001:2022 with Indonesia's Personal Data Protection (PDP) Law to establish a robust framework for data protection and information security within organizations operating in Indonesia. The research addresses the challenges of aligning the comprehensive information security management systems (ISMS) standard of ISO/IEC 27001:2022 with the specific legal requirements of the PDP Law, which governs personal data collection, processing, and protection. Employing the Action Design Research (ADR) methodology, the study involves a thorough review of existing literature, consultations with domain experts, and the development of a structured framework for integration. Key findings highlight the complementary nature of ISO/IEC 27001:2022's risk-based approach and the PDP Law's emphasis on data subject rights, consent management, and breach notification. The integration framework provides organizations with a unified approach to meet both international standards and local regulatory requirements, enhancing overall data protection. The research concludes with insights and recommendations for organizations seeking to navigate the complex landscape of data protection compliance, emphasizing the importance of harmonizing security measures with legal mandates to build a comprehensive and effective data protection strategy.
Social Engineering Awareness: A Social Science Approach to Cybersecurity Education Nugraha, Arya Adhi; Nasyuha, Asyahri Hadi
Proceedings International Conference on Education Innovation and Social Science 2024: Proceedings International Conference on Education Innovation and Social Science
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In today's increasingly digital society, individuals and organizations face a growing threat from social engineering attacks that exploit psychological and sociological vulnerabilities. This article investigates the integration of social science principles into cybersecurity education to enhance social engineering awareness. Recognizing the multifaceted nature of these attacks, the article proposes a comprehensive, age-tailored curriculum incorporating insights from psychology, sociology, and anthropology. For children, the curriculum introduces basic concepts of trust and online safety. For teenagers, it focuses on understanding psychological manipulation and promoting responsible online behavior. For adults, it offers advanced training on identifying and countering sophisticated social engineering tactics, fostering a security-conscious culture. The research methodology is based on secondary research, analyzing existing literature and case studies on social engineering and cybersecurity education. By leveraging this data, the article identifies effective educational strategies, such as gamification, scenario-based training, and continuous learning programs. These methods engage learners, enhance their practical skills, and ensure they stay updated on evolving threats. The article also emphasizes the importance of collaboration with social science experts and the use of behavioral analytics and machine learning to personalize training. In conclusion, adopting a social science approach in cybersecurity education significantly improves individuals' ability to recognize and resist social engineering attacks. The proposed curriculum and innovative teaching methods equip learners with the necessary knowledge and skills while fostering a proactive, security-conscious mindset. These efforts ultimately contribute to a more resilient and secure digital environment.
Semantic search-enhanced healthcare chatbot for hospital information management system using vector database and transformer models Guslinar Perdana, Erda; Nugraha, Arya Adhi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4600-4613

Abstract

Healthcare chatbots are increasingly used to assist hospital staff, yet most existing systems rely on rule-based or generic machine learning (ML) approaches that lack the ability to comprehend natural language queries, while proprietary deep learning systems often incur high licensing costs. This work addresses this gap by proposing a cost-effective and scalable semantic vector retrieval solution for user intent recognition in a hospital information management system (HIMS) helpdesk chatbot. The MPNet based transformer model is employed to convert user inquiries and predefined intents into feature vectors, enabling highly accurate natural language understanding through cosine similarity retrieval within a dedicated vector database. The proposed vector search method was validated via an ablation study, achieving an accuracy of 0.70 for intent recognition, which demonstrates a significant performance gain of 28.0 percentage points over a traditional keyword-based search baseline. Usability testing across developer and doctor groups yielded an average score of 7.78 on a 10-point Likert scale. This study concludes that integrating semantic vector retrieval with a vector database is highly effective for recognizing specialized clinical intents, offering a more accurate solution that significantly reduces the manual helpdesk workload and enhances 24-hour assistance in healthcare.
Co-Authors Asyahri Hadi Nasyuha Domy Kristomo, Domy Guslinar Perdana, Erda