PREDICTING LOAN ELIGIBILITY WITH SUPPORT VECTOR MACHINE: A MACHINE LEARNING APPROACH

Rajunaidi, Rajunaidi and Yuliansyah, Herman and Sunardi, Sunardi and Murinto, Murinto (2025) PREDICTING LOAN ELIGIBILITY WITH SUPPORT VECTOR MACHINE: A MACHINE LEARNING APPROACH. [Artikel Dosen]

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Abstract

Non-performing loans remain one of the main challenges faced by cooperatives, par-
ticularly when the loan eligibility assessment process is still conducted manually. This tradition-
al approach tends to be time consuming, subjective, and prone to inaccurate decisions. This
study aims to develop a predictive model for borrower eligibility using the Support Vector Ma-
chine (SVM) algorithm as a more efficient and objective machine learning-based solution. A
total of 1,000 loan history records were processed using RapidMiner software, taking into ac-
count variables such as salary, years of employment, loan amount, monthly installment, em-
ployment status, monthly expenses, number of dependents, housing status, age, and collateral
value. The model’s performance was evaluated using a confusion matrix and classification met-
rics including accuracy, precision, recall, and kappa. The results indicate that the SVM model
achieved an accuracy of 90.05%, precision of 90.13%, recall of 90.05%, and f1 score of
90,08%, reflecting a strong performance in classifying borrower eligibility. The application of
this method makes a significant contribution to the development of data driven decision support
systems within cooperative environments. This finding expands the scientific understanding in
the field of microfinance and supports the implementation of artificial intelligence technologies
in making decisions that are more precise, rapid, and accurate.

Item Type:	Artikel Dosen
Subjects:	T Technology > T Technology (General)
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Informatics Engineering (S1-Teknik Informatika)
Depositing User:	murinto murinto
Date Deposited:	27 Aug 2025 04:24
Last Modified:	27 Aug 2025 04:24
URI:	http://eprints.uad.ac.id/id/eprint/86699

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