Prediksi Tingkat Kelancaran Kredit BSU BMT Tunas Harapan Syari’ah Pringgasela Kabupaten Lombok Timur Menggunakan Algoritma Neural Network
DOI:
https://doi.org/10.29408/jit.v4i2.3580Keywords:
Credit, Neural Network Algorithm, data mining, forecastingAbstract
The role of credit in a cooperative is very important. With the credit can be a source of profit for the cooperative. The cooperative was founded with the aim of prospering its members. One of the advantages is that cooperative members can apply for credit loans. To approve the proposed loan, it is necessary to analyze the credit submitted by the members. This has become one of the difficulties for several cooperatives, one of which is KSU BMT Tunas Harapan Syari'ah which is located in thePringgasela village, Pringgasela District, East Lombok Regency. The problem that often arises is that the analysis conducted is often incorrect, resulting in a prolonged bad credit in installment payments. The reason is that cooperatives always use statistical data which is sometimes inaccurate because there is no processing using data processing methods. Therefore, the neural network data mining method can be used as a tool to analyze which customers are problematic and not problematic. From the results of the research that has been done, it produces an accuracy of 96.19% and an AUC of 0.976References
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