Analysis Of The Energy Efficiency Of Cryptocurrency Mining Gpus Based On Autolykos2 Mining With Moc - Alfikri, Muhammad Rizel; Ulum, Izza Ahyana Bahrul; Kaluti, Natanael Eka Putra; Bachtiar, Muhammad Ilham Helmi; Maesaroh, Syti Sarah (LPPJPHKI Universitas Dehasen Bengkulu, 2025)
The rapid development of blockchain technology and cryptocurrency mining has led to a significant increase in power consumption. Cryptocurrency mining relies on Graphics Processing Units (GPUs) to solve complex mathematical computations, ensuring network security and transaction validation. However, high power consumption directly impacts monthly operational costs (MOC), making energy efficiency and GPU performance crucial factors for sustainable mining operations.This study aims to analyze the relationship between GPU power consumption and hashrate in determining MOC in cryptocurrency mining. The independent variables in this research are GPU power consumption (Watts) and hashrate (MH/s), while the dependent variable is monthly operational costs (USD/month). A quantitative approach is applied using statistical methods, including descriptive statistical analysis to examine power consumption patterns, bivariate correlation analysis to evaluate variable relationships, and multiple linear regression to predict MOC. Additionally, normality and assumption tests are conducted to ensure data validity before proceeding with further analysis. To enhance accuracy, an online hashrate calculator simulation is also used to estimate real-time mining costs under practical mining conditions. By utilizing real-world GPU performance datasets, electricity pricing data, and mining simulations, this study provides valuable insights for cryptocurrency miners in selecting GPUs that optimize performance while maintaining power efficiency. The findings contribute to improving profitability and minimizing energy expenses in cryptocurrency mining operations.