Optimization of the "Mouse-Deer Jump" Algorithm in Predicting Digital Rice Harvests within an Ethereum-Kw Blockchain-Based Metaverse Ecosystem

Abstract

This study aims to test the effectiveness of a new heuristic algorithm named the "Mouse-Deer Jump" (Kancil-Lompat) within a Metaverse-based simulation farming environment. The primary issue addressed is the instability of virtual harvest data caused by crypto token price fluctuations, which form the economic basis of the ecosystem. The method employed was a Monte Carlo simulation with 1,000,000 iterations on a 50-hectare pixel virtual land. The results indicate that the Mouse-Deer Jump algorithm increased harvest prediction accuracy by up to 98%, significantly outperforming the standard "Running Turtle" algorithm. In conclusion, farming in the Metaverse requires a computational approach that is agile yet cautious, ensuring that digital crops are not affected by server latency.