EMPLOYEE CREDIT CLASSIFICATION ANALYSIS USING DECISION TREE BASED CRISP-DM MODEL (CASE STUDY OF SAMSUNG INDONESIA COMPANY)

Employee credit program is a form of employee retention as an effort to retain potential employees from the company. In its implementation, providing employee credit is not without risks that may occur. These risks include the inability or failure to pay credit installments when due. To minimize the risks that may occur, a survey and analysis with the right method is needed for cooperative members before providing employee credit. Researchers will use a Decision Tree-based algorithm as a tool for decision making in providing employee credit to Cooperatives at Samsung Indonesia Company. Researchers also use the CRoss-Industry Standard Process for Data Mining (CRISP-DM) model on the data mining development life cycle as a research step taken. This CRISP-DM model is very appropriate to use because it is a neutral model or method and can be used in various industries and combined with various tools. From the measurements that have been carried out using a sample data of 10 records from a total of 584 records, a classification model of 2 employees with non-performing employee credit status and 8 employees with performing employee credit status was obtained. The classification model was obtained based on the Gini Index Value of the Employee ID, Division, and Position attributes are 0.7, 0.3428571, and 0.2714286, respectively. So, the decision to grant credit to employees depends on the Position, after that the Division, and the last is the ID of the employee.