Skripsi

KLASIFIKASI TINGKAT RISIKO KREDIT MENGGUNAKAN METODE NAIVE BAYES DAN FUZZY NAIVE BAYES DENGAN TEKNIK K-FOLD CROSS VALIDATION

---

Penilaian

---

Credit is a condition of delivery in the form of goods, money or services from the first party (credit giver) to another party (credit recipient) with a joint agreement to be completed within a certain period of time in return for the additional principal. Customers who apply for credit must meet the conditions set by the bank with the aim of avoiding unexpected possibilities such as bad credit. In preventing bad credit, it is necessary to make the right decision to accept or reject credit applications. Therefore, this research was conducted with the aim of classifying credit risk levels using the naïve Bayes and fuzzy naïve Bayes methods. However, in the use of naïve Bayes and fuzzy naïve Bayes methods, there are often ups and downs in accuracy, so validation techniques are needed to measure model performance. In this study, the k-fold cross validation technique will be used to obtain the model with the best accuracy. The data used is the UCI Credit Card Approval Dataset Repository, totaling 30,000 data. Classification using the naïve Bayes method produces an average value of accuracy, precision, recall and f-score of 79.85%, 70.34%, 15.41%, 5.24%, respectively. Classification using the fuzzy naïve Bayes method produces an average value of accuracy, precision, recall and f-score of 80.23%, 62.22%, 26.72%, 37.32%, respectively. The results showed that the fuzzy naïve Bayes method has a better level of classification accuracy than the naïve Bayes method as seen from the average accuracy, recall and f-score. However, the naïve Bayes method has better precision than the naïve Bayes fuzzy method.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2307002220	T107515	T1075152023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1075152023

Publisher

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

Collation

xiv, 69 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

511.307

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

-

Subject(s)

Fuzzy Logic
Jurusan Matematika

Specific Detail Info

-

Statement of Responsibility

SEPTA

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment