Penerapan Metode Algoritma C5.0 Untuk Klasifikasi Pemberian Kredit KUR UMKM Pada PT Pegadaian

Abstract

KUR or known as People's Business Credit is a government program which aims to provide business assistance to small people, namely those who own businesses (MSMEs). Providing Credit can also refer to a situation where one party provides money or services to another party. Every time you give credit to a customer, there is a possibility of default. Default conditions can be detrimental to those who provide credit. This default condition, apart from being detrimental to the credit provider company, also has an impact on the company's performance. To overcome the risk of default, it is important for credit companies to carry out careful credit analysis, provide financial education to customers, and have an effective strategy. In addition, monitoring and updating customers' financial conditions regularly are also an important step in preventing payment failures. Data mining is a method for finding knowledge from piles of data. In this research, data mining is used to overcome the problem of default risk on business credit worthiness by involving data to identify patterns and factors that can predict potential default. By using the natural 5.0 algorithm method, data processing can be used to automate most of the credit assessment process which can save time and costs. The application of data mining in pawnshops allows more precise decisions to be made based on historical data and in-depth analysis, thereby helping to reduce risk and increase operational efficiency at credit granting companies. The application of the C5.0 Algorithm method helps identify critical factors that influence the feasibility of MSME sharia currency, such as customer profiles, business characteristics and financial performance. The results of the feasibility evaluation show that the majority of MSMEs receive funding through sharia currency.