Skripsi

PENGELOMPOKKAN RISIKO KREDIT MENGGUNAKAN METODE NAIVE BAYES

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Penilaian

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The use of credit in this day and age has been increasing from the bottom to the top. The level of credit risk is also higher due to the failure or inability of the customer to return the loan amount obtained from the bank and its interest in accordance with the scheduled period. So it takes a computer system that can study historical data from credit lending effectively. The Naive Bayes method is a probability approach to generate classifying the class probability determination for an object. Required classification model used in predicting credit risk there are 20 variables that affect the status, duration, credit history, purpose, amount, savings, employment duration, installment rate, personal status sex, other debtor, present residence, property, age, other installment plans, housing, number credit, job, people liable, telephone, foreign worker. Confusion Matrix results are 122 classifications of good credit and 24 bad credits, it can be concluded that the classification results are more dominant good credit. The accuracy rate of the combination of variables obtained is 73%. The combination is produced by several combinations that exist, which use 1000 data record histories debtor, the data is used for training as many as 800 data and testing as much as 200 data.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107003410	T50641	T506412021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T506412021

Publisher

Inderalaya : Prodi Ilmu Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alamc., 2021

Collation

xiii, 35 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

519.707

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Ilmu Matematika
Matematika Pemrograman
Metode Naive Bayes

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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