PENGELOMPOKKAN RISIKO KREDIT MENGGUNAKAN METODE NAIVE BAYES

ANGGARA, MUHAMMAD ARI and Yahdin, Sugandi and Resti, Yulia (2021) PENGELOMPOKKAN RISIKO KREDIT MENGGUNAKAN METODE NAIVE BAYES. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Risiko Kredit, Klasifikasi, Naive Bayes
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Muhammad Ari Anggara
Date Deposited:	28 Jul 2021 05:08
Last Modified:	28 Jul 2021 05:08
URI:	http://repository.unsri.ac.id/id/eprint/50641

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