Abstract

Blockchain technology has emerged as a transformative innovation with applications across finance, healthcare, supply chain, and education. However, one of its persistent challenges is transaction speed and scalability, which limit its efficiency and adoption in high-volume environments. Existing research has explored various approaches, including consensus algorithm optimization, sharding, and off-chain solutions, but these methods often face issues of complexity, high cost, or limited adaptability. In this study, a fuzzy logic model is proposed to enhance transaction speed and improve scalability on blockchain networks. Using a dataset sourced from Kaggle, the research employed data collection, cleaning, and preprocessing, followed by the application of fuzzy logic through a triangular membership function that incorporates three key parameters: transaction fee, transaction speed, and block density. The model was implemented in Python using Scikit-fuzzy, supported by NumPy, Pandas, and Matplotlib for computation, data manipulation, and visualization. Results demonstrate that the fuzzy logic approach provides a flexible and efficient mechanism for decision-making within blockchain transactions, outperforming conventional analytical methods in adaptability and ease of implementation. The findings indicate that integrating fuzzy logic into blockchain systems can streamline transaction processing, enhance scalability, and support the design of more efficient consensus mechanisms. This work contributes to the growing body of knowledge on intelligent blockchain optimization and provides a foundation for further research integrating fuzzy logic with advanced consensus methods.

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