Permissioned blockchain technology has a significant impact on many industries due to its security and scalability, which has many configurable knobs to control all aspects of it. Too many knobs lead to an exponential increase in the configurable knob combination, so it is vital to find a subset of knobs that affect the performance. This article proposes FIK (Find Important Knobs), which uses (1) Latin hypercube sampling technology to create a data set of knobs and performance. The data set includes more than 10000 samples of three different network architectures of the permissioned blockchain network and three different chaincode. (2) The relationship between knobs and performance is modeled based on the Xgboost method. The performance of this model is better than other selected machine learning models. (3) The SHAP-based method helps decision-makers explain the contribution of the knobs of the permissioned blockchain to performance.