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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.
Decentralized Data Storage Using IPFS for Sustainable Blockchain Availability Improvement Jaya, Aswadi; Fahrurrozi, Muh; Sibagariang, Susy Alestriani; Vinkan Likita; Zainarthur, Henry
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.v5i2.860

Abstract

The rapid expansion of digital ecosystems has highlighted the limitations of centralized data storage systems, which often struggle with data loss, censorship, and single points of failure. To address these challenges, this study explores the InterPlanetary File System (IPFS) as a decentralized data management solution that enhances security, availability, and sustainability in distributed information environments. Using the IPFS-KI framework, a descriptive qualitative methodology, this research examines the architectural design, operational mechanisms, and real-world implementations of IPFS. Through literature analysis, node simulations, and case based evaluation, the study investigates IPFS performance in maintaining data integrity, fault tolerance, and resilience against network disruptions and censorship. The findings reveal that IPFS provides improved data reliability, transparency, and scalability compared to conventional centralized architectures, although certain limitations remain in terms of node stability and hidden centralization. This study contributes to a broader understanding of how decentralized storage technologies like IPFS can support the development of more secure, equitable, and sustainable digital infrastructures.