Skripsi

PREDIKSI TINGKAT KELANCARAN PEMBAYARAN KREDIT DENGAN MENGGUNAKAN METODE NAIVE BAYES

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Penilaian

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One of the risks of providing credit in the banking world is bad credit. Almost all banks have cases of bad credit, but the level of credit risk varies, including due to the magnitude and potential social impact. The customer's inability to repay the loan on time is a problem that cannot be ignored by the creditor bank. The success of predicting bank customers who have the potential to fail to pay credit can help banks in selecting prospective customers who will get loans. This study uses the Naive Bayes method to predict customers who have the potential to not pay credit smoothly. The research data are historical records of banking debtors in Taiwan in 1988 as many as 30000 customers with 22 predictor variables, namely gender, education, marital status, age, repayment in September, repayment in August, repayment in July, repaid in June, repaid in May, repaid in April, billed amount in September, billed amount in August, billed amount in July, billed amount in June, billed amount in May, billed amount in April, previous payment amount in September, previous payment amount in August, previous payment amount in July, previous payment amount in June, previous payment amount in May, previous payment amount in April. With the composition of the training data and 80:20 test data, the accuracy is 97.9%, sensitivity is 91.29% and specificity is 100%. It can be concluded that the Naive Bayes method is very good at predicting customers who have the potential to not pay credit smoothly

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107005430	T66789	T667892021	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T667892021

Publisher

Indralaya : Prodi Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2021

Collation

xiv, 45 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

519.507

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Matematika
Matematika Statistikal

Specific Detail Info

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Statement of Responsibility

PITRIA

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