Penerapan Fitur Seleksi dan Particle Swarm Optimization pada Algoritma Support Vector Machine untuk Analisis Credit Scoring

Abstract

After the Covid-19 pandemic, the banking sector faced significant challenges in contributing to achieving national goals in terms of increasing living standards and equalizing the regional economy. Hundreds of millions of low-income people have no credit or bank accounts because they do not have sufficient credit history to warrant the credit scores assigned to them. An estimated 1.7 billion people (31% of the adult population) do not have an account with a financial institution. People today are usually concentrated in developing countries, especially in China 204 million, India 357 million and Indonesia 102 million people. Because it is very difficult to make accurate predictions in determining credit worthiness for low-income people. Cooperatives are financial institutions that have a crucial role in channeling financing to members and the community to develop their businesses. An inappropriate credit distribution process can have a negative effect on KSP, resulting in frequent losses. This risk is known as problem loans, the cause is the KSP's failure to analyze the credit of prospective debtors. Therefore, calculations are needed to detect opportunities for credit risk default by prospective debtors objectively and precisely so that loan problems do not occur. Credit scoring is a method used to evaluate credit risk in terms of loan applications from consumers [4]. In this research we will provide a solution using classification techniques with feature selection methods in the Particle Swarm Optimization (PSO) Algorithm and Support Vector Machine (SVM) to predict the credit risk of prospective debtors failing to make loan payments. The application of the SVM algorithm in credit scoring research is because SVM is good at data classification. However, the standard SVM model still does not produce optimal results due to the difficulty of determining the best parameters, therefore researchers will optimize it with the Feature Selection and PSO algorithms to determine the best parameters. The results from the combination of several parameters using PSO-SVM obtained an accuracy of 87.23%, therefore the application of this method was proven to improve the performance of the SVM algorithm to increase its accuracy results in predicting the feasibility of granting credit.