PREDIKSI TINGKAT KELANCARAN PEMBAYARAN KREDIT DENGAN MENGGUNAKAN METODE NAIVE BAYES

AGUSTYANDIE, EKO and Resti, Yulia and Yahdin, Sugandi (2021) PREDIKSI TINGKAT KELANCARAN PEMBAYARAN KREDIT DENGAN MENGGUNAKAN METODE NAIVE BAYES. Undergraduate thesis, Sriwijaya University.

	Text RAMA_44201_08011181621010.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_44201_08011181621010_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (5MB) | Request a copy
Preview	Text RAMA_44201_08011181621010_0019077302_0027075803_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (635kB) | Preview
	Text RAMA_44201_08011181621010_0019077302_0027075803_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (161kB) | Request a copy
	Text RAMA_44201_08011181621010_0019077302_0027075803_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (56kB) | Request a copy
	Text RAMA_44201_08011181621010_0019077302_0027075803_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (492kB) | Request a copy
	Text RAMA_44201_08011181621010_0019077302_0027075803_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (54kB) | Request a copy
	Text RAMA_44201_08011181621010_0019077302_0027075803_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (59kB) | Request a copy

Abstract

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

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Risiko Kredit, Klasifikasi, Naive Bayes
Subjects:	Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics > QA279.M67 Experimental design. Response surfaces (Statistics)
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Eko Agustyandie
Date Deposited:	24 Mar 2022 08:23
Last Modified:	24 Mar 2022 08:23
URI:	http://repository.unsri.ac.id/id/eprint/66789

Actions (login required)

View Item