Blockchain based agriculture product supply chain management system using K nearest neighbor to enhance the accuracy and comparing with random forest algorithm.

This study compares K Nearest Neighbor and random forests to increase accuracy in blockchain-based agriculture product supply chain management systems. K Nearest Neighbor and Random Forest algorithms are tested when the data sets are imported. Algorithms are run with varied training and testing splits to improve accuracy in blockchain-based farm product supply chain management systems. There are two groups for the two algorithms. There are 20 total samples, with 10 in each group, With G power setting parameters of (α=0.05 and power=0.80). Our research demonstrates a statistically significant difference between the K Nearest Neighbor algorithm's accuracy of 83.0% and the Random Forest algorithm's accuracy of 77.0%. Furthermore, the t-test for independent samples with statistically significant value of p=0.000, (p<0.05) was applied to estimate the mean, deviation, and standard error. According to the data obtained for this research, the Innovative K Nearest Neighbour Algorithm demonstrates superior performance in accuracy (83.0%) compared to the Random Forest Algorithm (77.0%). [ABSTRACT FROM AUTHOR]