Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Startupreneur Business Digital (SABDA Journal) Blockchain Frontier Technology (BFRONT) JOT
Rendhika Adyatama
University of Raharja
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science

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Distributed Data Integrity and Decentralized Storage Leveraging IPFS in Blockchain Systems Ahmad Gunawan; Mungkap Mangapul Siahaan; Rendhika Adyatama; Toktar Kerimbekov; Kristina Vaher
Blockchain Frontier Technology Vol. 5 No. 2 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v5i2.915

Abstract

In the digital era that increasingly relies on distributed systems, data integrity has become a major challenge for various technological platforms, ranging from cloud services to blockchain-based infrastructures. The dispersion of data across multiple nodes without centralized oversight increases the risk of manipulation, loss, and inconsistency. This study aims to evaluate the effectiveness of the InterPlanetary File System (IPFS) as a distributed data storage solution capable of maintaining data integrity in an automatic, efficient, and verifiable manner. The research method involves a literature review of 30 scientific references and a case study-based simulation conducted within a local network consisting of four active IPFS nodes. The tests include file uploads, verification of Content Identifiers (CID), simulation of node failures, and evaluation of system performance based on parameters such as security, efficiency, and scalability. The results indicate that IPFS successfully detects file changes through CID discrepancies, ensures continued data access despite node failures, and optimizes bandwidth usage via caching and replication mechanisms. In conclusion, IPFS offers a secure, scalable, and tamper-resistant approach to distributed data storage, making it highly relevant for modern digital systems requiring transparency, reliability, and strong data integrity guarantees.
Influence of Digital Technology & Data Analytics on Strategic Decision Making Asep Sutarman; Ronal Aprianto; Rendhika Adyatama; Krishna Chaitanya Pokkali; Muhamad Yusup
Startupreneur Business Digital (SABDA Journal) Vol. 4 No. 1 (2025): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v4i1.685

Abstract

In an increasingly advanced information age, digital technology and data analytics have become important pillars in effective decision making across a wide range of sectors. This journal aims to explore how digital technologies, including big data, artificial intelligence (AI), and machine learning, can be utilized to improve decision quality. Through a case study approach, we analyze several organizations that have successfully implemented these technologies in their decision making processes. The results show that the use of advanced data analytics enables organizations to unearth previously unreachable insights, improving the accuracy and speed of decision making. However, the research also identified several challenges, including the need for high analytical skills, data privacy concerns, and resistance to change in organizational culture. We found that to maximize the benefits of digital technologies, organizations need to adopt the right training strategies and create an environment that supports innovation. This research makes an important contribution to the understanding of how digital technologies can change the way organizations make decisions, as well as practical recommendations for more effective implementation. The findings are expected to help organizational leaders and decision-makers formulate better strategies to face the challenges and opportunities of the digital age. This study uniquely contributes by examining the synergistic interaction between digital technology and data analytics in strategic decision-making across multiple sectors. This study contributes by identifying key factors that influence the effectiveness of digital technology and data analytics adoption in strategic decision making and offering practical recommendations for companies to enhance the adoption of these 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.
Co-Authors Ahmad Gunawan Asep Sutarman Krishna Chaitanya Pokkali Kristina Vaher Marviola Hardini Muhamad Yusup Mungkap Mangapul Siahaan Nur Azizah Ronal Aprianto Souza Nurafrianto Windiartono Putra Syahla Naurah Toktar Kerimbekov