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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Aulia Khanza
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Education Library & Information Science Other

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Decentralized File Sharing Infrastructure with IPFS for Censorship Resistance in Blockchain Ecosystems Nesti Anggraini Santoso; Palma Juanta; Sabda Maulana; Kerimbekov Toktar; Aulia Khanza
Blockchain Frontier Technology Vol. 5 No. 1 (2025): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v5i1.835

Abstract

Traditional file sharing systems that rely on centralized servers face various challenges. problems such as vulnerability to censorship, blocking, and server failures.This research focuses on the need for file sharing technology that is more secure, censorship-resistant, and does not rely on a single central authority. Challenge the main challenge in developing a new file sharing system is how to overcome the limitations of network scale, slow access speed, and lack of incentives for node providers. Research purposes this is to analyze the role of the InterPlanetary File System (IPFS) in building a censorship-resistant file sharing infrastructure and evaluate its benefits and constraints compared to conventional file sharing systems. Findings from the literature study show that IPFS has advantages in data decentralization, increasing information availability, and strengthening resistance to censorship. Research results also revealed that although IPFS is able to overcome many weaknesses of traditional systems, further development is still needed in terms of network efficiency and user incentives. In conclusion, IPFS has great potential to become the main foundation in a future digital ecosystem that is more open, secure, and free from third-party intervention.
Data Driven A or B Testing Methodology for Website Effectiveness Qurotul Aini; Aulia Khanza; Vinkan Likita; Lase, Steven Harazaki; Kareem, Yasir Mustafa
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/t04mab20

Abstract

Website design and optimization decisions are often driven by subjective opinions, internal organizational preferences, or prevailing industry trends rather than empirical evidence derived from large-scale user interaction data, resulting in suboptimal performance and inconsistent user experiences. In digital environments characterized by high data volume and velocity, the absence of a structured experimentation methodology limits organizations’ ability to effectively leverage Big Data for continuous website improvement. This paper presents a comprehensive and systematic methodological guide to A or B testing as a data-driven approach for enhancing website effectiveness in data-intensive contexts. Unlike existing A or B testing guides that focus mainly on tools or isolated experimental outcomes, this study proposes an end-to-end framework integrating hypothesis formulation, scalable experimental design, statistical rigor, iterative learning, and practical decision-making into a unified and replicable process. The methodology outlines the complete A or B testing lifecycle, including alignment of business objectives with measurable data signals, development of testable hypotheses, controlled experiment implementation, large-scale data collection, and statistical analysis to ensure validity and significance of findings. The results demonstrate that a disciplined and continuous A or B testing program supported by Big Data analytics enables incremental yet compounding improvements in website performance. Through illustrative case examples, the study shows that relatively small, data-informed changes to website elements such as headlines, calls-to-action, images, and layout structures can lead to statistically significant gains in conversion rates, user engagement, and overall user experience. The paper concludes that A or B testing serves as a strategic Big Data analytics mechanism that supports evidence-based website optimization decisions grounded in empirical user behavior rather than intuition.
Co-Authors Kareem, Yasir Mustafa Kerimbekov Toktar Lase, Steven Harazaki Nesti Anggraini Santoso Palma Juanta Qurotul Aini Sabda Maulana Vinkan Likita