This project aims to optimize transaction processes and increase operational efficiency at CRANK & ROLL by comparing the performance of six machine learning algorithms, including Bayesian Network, Gradient Boosting, Random Forest, Decision Tree, Support Vector Machine (SVM), and Neural Network, in classifying payment methods, particularly credit cards using SAS software. The researcher used metrics such as accuracy, precision, recall, F1 score, confusion matrix, ROC-AUC, lift chart, and cumulative gain to evaluate the performance of the machine learning models. Based on the analysis results, it was found that Gradient Boosting is the best model with the lowest misclassification rate. Implementing the Gradient Boosting model in the CRANK & ROLL transaction system optimizes transaction processes by making more accurate predictions about customer payment methods, allowing for more efficient and effective transaction processing. Additionally, a better understanding of customer payment preferences enables CRANK & ROLL to optimize inventory management and marketing strategies, as well as support more informed and strategic business decision-making. This study is expected to provide deeper insights into customer payment preferences and increase understanding of machine learning algorithms, particularly Gradient Boosting, in the context of motorcycle parts retail business.