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Arwin Arwin
STMIK Mikroskil
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Optimization Performance of Fuzzy K-Nn with Modifield Particle Swarm Optimization in Credit Risk Classification Wita Clarisa Ginting; Ronsen Purba; Arwin Arwin
Jurnal Mantik Vol. 4 No. 2 (2020): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.956.pp1417-1423

Abstract

Credit risk is a risk due to the failure or inability of the customer to return the amount of credit obtained from the company and its interest according to a predetermined or scheduled period of time. The main task of the credit risk classification method is to provide a separation between those who have the potential to fail and those who have not failed in terms of credit payments. The k-Nearest Neighbor (kNN) method as the most popular, simple and easily implemented machine learning method can be used to classify credit risk. However, its success depends on the number of neighbors or neighbors (k) applied and the relationship between each data with a class is rigid (crisp) where each data only has a relationship with one class exclusively, while the other classes have no relationship at all. This study proposes the incorporation of the principles of fuzzy logic into k-NN to minimize the stiffness that results in a new method known as Fuzzy k-Nearest Neighbor or Fk-NN. However, the fuzzy strength factor (m) and the number of neighbors (k) as the fundamental determinants of Fk-NN which have a direct impact on the accuracy generated by the model, the determination is often not easy and difficult to control, so the Modified method is proposed Particle Swarm Optimization (MPSO) to be able to help Fk-NN find the best m and k values non-manually. The results of the classification of credit risk data are 1000 data, with 900 composition of training data (90%) and 100 data (10%) of test data using Fk-NN with MPSO producing accuracy reaching 92.4%, with the best k value is 7 and the best m value is 9.
Credit Card Risk Classification Using K-Nearest Neighbor Weighted Algorithm Based on Forward Selection Sartika Dewi Purba; Pahala Sirait; Arwin Arwin
Jurnal Mantik Vol. 4 No. 3 (2020): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.960.pp1551-1559

Abstract

One form of credit card risk is non-performing credit cards, which describe a situation where loan repayment approval on credit cards runs the risk of failure. In the classification technique there are several algorithms that can be used, one algorithm that is often used is Weighted k-nearest neighbor (WKNN). This study aims to improve the performance of the Weighted k-nearest neighbor (WKNN) algorithm by applying the forward selection feature that is used to select each unused feature when starting a feature iteration, the results of the study show that by adding forward performance selection of the Weighted k-nearest algorithm neighbor (WKNN) get a better value that is 86.4%, compared to using the Weighted k-nearest neighbor (WKNN) algorithm without a forward selection that is equal to 60.1%.
Co-Authors Pahala Sirait Ronsen Purba Sartika Dewi Purba Wita Clarisa Ginting