TING, YESICA SUGIARTO (2022) LOAN APPROVAL PREDICTION USING LOGISTIC REGRESSION AND EXTREME GRADIENT BOOSTING ALGORITHMS. Other thesis, Universitas Katholik Soegijapranata Semarang.
|
Text
18.K1.0024-YESICA SUGIARTO, TING-COVER_a.pdf Download (334kB) | Preview |
|
|
Text
18.K1.0024-YESICA SUGIARTO, TING-BAB I_a.pdf Download (139kB) | Preview |
|
![[img]](https://repository.unika.ac.id/style/images/fileicons/text.png) |
Text
18.K1.0024-YESICA SUGIARTO, TING-BAB II_a.pdf Restricted to Registered users only Download (184kB) |
|
|
Text
18.K1.0024-YESICA SUGIARTO, TING-BAB III_a.pdf Download (114kB) | Preview |
|
|
Text
18.K1.0024-YESICA SUGIARTO, TING-BAB IV_a.pdf Download (400kB) | Preview |
|
|
Text
18.K1.0024-YESICA SUGIARTO, TING-BAB V_a.pdf Download (364kB) | Preview |
|
|
Text
18.K1.0024-YESICA SUGIARTO, TING-BAB VI_a.pdf Download (112kB) | Preview |
|
|
Text
18.K1.0024-YESICA SUGIARTO, TING-DAPUS_a.pdf Download (177kB) | Preview |
|
|
Text
18.K1.0024-YESICA SUGIARTO, TING-LAMP_a.pdf Download (411kB) | Preview |
Abstract
In the savings and loan business or banking, it is very important to determine whether the borrower is able to pay or cannot repay the loan. This is very necessary in order to avoid losses. In this process, predictions to classify categories of borrowers including those who are able to pay or not, require a computer approach using machine learning with some of the algorithm methods, namely logistic regression and extreme gradient boosting. Both models can predict the outcome of the decision quite well with the acquisition of logistic regression 69% and extreme gradient boosting 82%. Which when compared to extreme gradient boosting has better results.
| Item Type: | Thesis (Other) |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works |
| Divisions: | Faculty of Computer Science > Department of Informatics Engineering |
| Depositing User: | mr AM. Pudja Adjie Sudoso |
| Date Deposited: | 26 Oct 2022 09:27 |
| Last Modified: | 26 Oct 2022 09:27 |
| URI: | http://repository.unika.ac.id/id/eprint/30027 |
Actions (login required)
 |
View Item |
