Analysis and Optimization of Cryptocurrency Mining Efficiency Using unMineable and XMRig for Shiba Inu (SHIB) Token Acquisition
DOI:
https://doi.org/10.29103/sisfo.v10i2.26998Keywords:
cryptocurrency mining, XMRig, unMineableAbstract
Cryptocurrency mining under a non-native token payout scheme presents multidimensional challenges involving technical performance, economic viability, and environmental impact. This study evaluates the operational feasibility of acquiring Shiba Inu (SHIB) tokens through the unMineable platform combined with XMRig mining software by applying a multidimensional efficiency framework. A descriptive-analytical mixed-methods approach was used, in which nine computing units equipped with Intel Core i5 processors and mid-range GPUs were observed continuously for approximately 30 days. The recorded variables included hashrate, power consumption, operating temperature, SHIB payout volume, electricity cost, and estimated carbon emissions. The results show that GPU units achieved a relative efficiency of approximately 208 H/W under the KAWPOW/Ethash algorithm, whereas CPU units running RandomX reached approximately 46 H/W, indicating that GPU mining was about 4.5 times more efficient per watt in the tested configuration. Economically, the combined configuration consumed 133.2 kWh per month with an electricity cost of IDR 213,120, while generating approximately 1,250,000 SHIB tokens valued at IDR 175,000 under the assumed price of IDR 0.14 per token. This resulted in a monthly deficit of IDR 38,120 and a profitability margin of -21.8%. Environmentally, the operation produced an estimated 93.24 kg CO₂ per month, equivalent to 1,118.88 kg CO₂ annually. These findings confirm that mining evaluation should integrate hashrate, power consumption, payout mechanisms, electricity tariffs, and carbon intensity rather than relying solely on computational speed. The study contributes a replicable evaluation framework and recommends GPU power throttling, XMRig huge-page optimization, and operational scheduling as realistic near-term interventions.
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