Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307003040	T126630	T1266302023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1266302023

Publisher

Inderalaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Sriwijaya., 2023

Collation

xiv, 75 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

510.285 07

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Jurusan Matematika
Pengolahan dan Analisa Data di Bidang Matematika

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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