Classification of the Feasibility of Providing Loans at Sejahtera Savings and Loan Cooperative Using Naive Bayes Algorithm

Kurniawan, Nathaniel Aditha (2021) Classification of the Feasibility of Providing Loans at Sejahtera Savings and Loan Cooperative Using Naive Bayes Algorithm. Other thesis, Universitas Katholik Soegijapranata Semarang.

[img] Text
16.K1.0003-Nathaniel Aditha Kurniawan-COVER_a.pdf

Download (705kB)
[img] Text
16.K1.0003-Nathaniel Aditha Kurniawan-BAB I_a.pdf

Download (680kB)
[img] Text
16.K1.0003-Nathaniel Aditha Kurniawan-BAB II_a.pdf
Restricted to Registered users only

Download (707kB)
[img] Text
16.K1.0003-Nathaniel Aditha Kurniawan-BAB III_a.pdf

Download (681kB)
[img] Text
16.K1.0003-Nathaniel Aditha Kurniawan-BAB IV_a.pdf

Download (722kB)
[img] Text
16.K1.0003-Nathaniel Aditha Kurniawan-BAB V_a.pdf

Download (724kB)
[img] Text
16.K1.0003-Nathaniel Aditha Kurniawan-BAB VI_a.pdf

Download (679kB)
[img] Text
16.K1.0003-Nathaniel Aditha Kurniawan-DAPUS_a.pdf

Download (691kB)
[img] Text
16.K1.0003-Nathaniel Aditha Kurniawan-LAMP_a.pdf

Download (693kB)

Abstract

In Sejahtera savings and loan cooperative, there are two categories of prospective customers, namely prospective customers who are feasible and those who are not eligible for a loan. The distribution comes from several factors, namely gender, application, salary, term, interest, and guarantees of the prospective customer which are still assessed from human assessments. These judgments sometimes produce inaccurate results. To overcome this problem, an application was made to predict the feasibility of a loan from prospective customers of a Prosperous savings and loan cooperative using the Naive Bayes Algorithm which aims to reduce the occurrence of loan errors. The final result of this research is a precision level of 0.85, a recall level of 0.07, and an accuracy level of 0.15 in the first test using 300 training data and a precision level of 0.91, a recall level of 0.97, and an accuracy level of 0.89 in the second test. by using 600 training data. And in the third testing using K-Fold Cross Validation Method with 10 fold, an accuracy rate of 84,783% was obtained.

Item Type: Thesis (Other)
Subjects: 000 Computer Science, Information and General Works > 005 Computer programming, programs & data
Divisions: Faculty of Computer Science > Department of Informatics Engineering
Depositing User: mr AM. Pudja Adjie Sudoso
Date Deposited: 18 May 2021 01:54
Last Modified: 18 May 2021 01:54
URI: http://repository.unika.ac.id/id/eprint/25041

Actions (login required)

View Item View Item