<![CDATA[In this research, the data collection carried out by studying the patterns of consumers who fail to pay, which aimed to build a model so that it could be used in predicting customers who have the potential to fail to pay. The research used the Cross-Industry Standard Process for Data Mining (CRISP-DM) method with details of the business understanding process, data understanding, data preparation, modeling, evaluation and deployment / interpretation. The dataset in this research was taken from sales, cancellation and consumer data from January 2016 to December 2019. Because the dataset in this research was an imbalanced dataset, the researchers tried to use Synthetic Minority Oversampling Technique (SMOTE) in handling the imbalanced dataset. The research conducted a comparison of the value of accuracy, precision, recall, f measure and Area Under the ROC Curve (AUC) between the original dataset and the dataset for the addition of the SMOTE technique to several algorithms including C4.5, K-NN and Naïve Bayes. The attributes used in this research were source of funds, purpose of purchase, age, selling price, occupation, total installments, percentage of total installments, monthly installments, percentage of late installments and status. From the comparison, it was found that the C4.5 algorithm with the SMOTE 480% dataset had the highest accuracy value of 97.62%, precision of 0.976, recall of 0.976, f measure of 0.976 and AUC of 0.986 which meant Excellent Classification. From the research conducted, it was expected that the model formed on the imbalanced dataset with the C4.5 and SMOTE algorithms could be used to predict consumer installment failures.]]>
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