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This Author published in this journals
All Journal Startupreneur Business Digital (SABDA Journal) Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Ikyboy Van Versie
Eesp Incorporation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science Other

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Blockchain Enabled Voting System for Improving Election Transparency and Trust Nasib Nasib; Fina Nailatul Izzah; Aldila Dinanti; Az Zahrawani Ramadhan; Ikyboy Van Versie
Blockchain Frontier Technology Vol. 6 No. 1 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/b-front.v6i1.1035

Abstract

Elections are a fundamental component of democratic systems, yet conventional paper based and centralized electronic voting mechanisms continue to face persistent challenges related to transparency, security, auditability, and declining public trust. To address these issues, this study aims to design and evaluate a blockchain enabled voting system that can improve election transparency and strengthen voter trust by leveraging decentralization, immutability, and crypto- graphic security. The research adopts a system design and evaluation methodology that integrates an in depth literature review, blockchain architecture modeling, smart contract development, and experimental evaluation focusing on security, transparency, and performance under simulated election conditions. The results show that the proposed system successfully ensures immutable vote recording, prevents double voting through automated smart contract enforcement, enhances end to end auditability via a transparent distributed ledger, and significantly improves resistance to vote manipulation and unauthorized access when compared to conventional and centralized electronic voting systems. Performance analysis indicates that while transaction latency increases with higher voting loads due to consensus mechanisms, the system remains stable, reliable, and operational, demonstrating feasibility for small to medium scale elections. Furthermore, the decentralized architecture reduces single points of failure and minimizes reliance on trusted third parties. Overall, this study concludes that blockchain technology provides a robust and trustworthy foundation for modern digital voting systems, while also highlighting scalability, computational overhead, and real world implementation challenges that should be addressed through optimization techniques and pilot deployments in future research.
Self Learning Artificial Intelligence for Autonomous Threat Detection in Computer Networks Dwi Cahyono; Herman Herman; Ikyboy Van Versie
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tk5ypk40

Abstract

The rapid expansion of large-scale computer networks and the exponential growth of big data have significantly increased the complexity and frequency of cyber threats, rendering traditional signature-based security mechanisms inadequate for adaptive detection. This study aims to develop a self-learning AI model capable of autonomously identifying evolving attack patterns and anomalous behaviors in large-scale networks without relying exclusively on pre-labeled datasets. The proposed framework integrates deep neural architectures, incremental learning, and behavior-based traffic analysis to enable continuous adaptation to dynamic threat environments while ensuring computational efficiency and scalability. The model was trained and evaluated using realistic network traffic datasets simulating distributed attacks, zero-day exploits, and advanced persistent threats across heterogeneous environments. Experimental findings demonstrate that the self-learning approach enhances detection accuracy, reduces false positives, and accelerates response times compared to conventional intrusion detection systems. In addition, the combination of deep neural architectures with incremental learning and scalable data processing further strengthens model robustness and adaptability in complex and evolving networks. The results indicate that integrating adaptive AI into cybersecurity frameworks enhances proactive defense capabilities, improves resilience in large-scale computer networks, and provides a scalable, intelligent solution for next-generation threat detection systems. This study highlights the practical relevance of combining AI, big data analytics, and cybersecurity strategies to support intelligent, adaptive security solutions capable of addressing emerging threats, minimizing operational risks, and fostering robust network protection in increasingly complex digital infrastructures.
Online Marketing Strategy Optimization to Increase Sales and E-Commerce Development: An Integrated Approach in the Digital Age Lunatari Sanbella; Ikyboy Van Versie; Sipah Audiah
Startupreneur Business Digital (SABDA Journal) Vol. 3 No. 1 (2024): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v3i1.492

Abstract

In the face of the challenges of intensifying competition in the rapidly growing world of e-commerce, this research focuses on investigating, identifying, and optimizing online marketing strategies to increase sales and support the growth of the e-commerce industry. This research aims to provide in-depth insights to online business owners by thoroughly understanding consumer behavior, technology trends, and digital market dynamics.This research adopts a mixed qualitative and quantitative approach by analyzing data from multiple sources. Customer surveys, sales data analysis, and case studies on successful e-commerce platforms provide the foundation for exploring consumer needs and preferences. Special emphasis is placed on assessing the sustainability of digital marketing strategies, utilization of social media, and implementation of the latest technologies in the e-commerce ecosystem.The research results are expected to provide comprehensive guidance for online marketing practitioners and e-commerce business owners. The findings not only include an in-depth understanding of consumer desires and market opportunities, but also provide valuable information on the effective use of social media, content strategies, and the latest ways to increase brand awareness, consumer engagement, and sales conversion. The use of technology is also emphasized as a key element in improving the competitiveness of e-commerce companies in the era of increasing digitalization.
Co-Authors Aldila Dinanti, Aldila Az Zahrawani Ramadhan Dwi Cahyono Fina Nailatul Izzah Herman Herman Lunatari Sanbella Nasib Sipah Audiah