KLASIFIKASI TINGKAT RISIKO KREDIT DENGAN MENGGUNAKAN METODE FUZZY RANDOM FOREST BERDASARKAN RESAMPLING K-FOLD CROSS VALIDATION

LATIFATUSSOLEHAH, HUTVINA and Resti, Yulia and Kresnawati, Endang Sri (2023) KLASIFIKASI TINGKAT RISIKO KREDIT DENGAN MENGGUNAKAN METODE FUZZY RANDOM FOREST BERDASARKAN RESAMPLING K-FOLD CROSS VALIDATION. Undergraduate thesis, Sriwijaya University.

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Abstract

Credit is currently growing very rapidly because it is used as a payment mechanism among the public, but the credit provided does not rule out the possibility of having a high risk. Classifying potential debtors is one way to help reduce credit risk. Credit risk levels are classified in this study using the Fuzzy Random Forest method based on K-Fold Cross Validation Resampling. The University of California Irvine (UCI) Machine Learning Repository's Approval Credit Card Taiwan Dataset was the dataset used in this study. The steps in this research are discretizing the data, forming fuzzy sets on numeric variables with membership functions, dividing the training data and test data, forming a decision tree with the Random Forest algorithm, then calculating the accuracy. The results showed that the average values for Accuracy, Precision, Recall, and FScore were 81.99%, 68.39%, 35.27% and 46.53% which indicates that the Fuzzy Random Forest method in this dataset is better at predicting credit that is not high risk than high risk.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Credit Risk, K-Fold Cross Validation, Fuzzy Random Forest
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Hutvina Latifatussolehah
Date Deposited:	09 Aug 2023 05:55
Last Modified:	09 Aug 2023 05:55
URI:	http://repository.unsri.ac.id/id/eprint/126630

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