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All Journal JURSIMA (Jurnal Sistem Informasi dan Manajemen) JURSIMA Jurnal Sistem Informasi dan Manajemen
Syafi'i Syafi'i
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science Social Sciences

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PENERAPAN MACHINE LEARNING UNTUK MENENTUKAN KELAYAKAN KREDIT MENGGUNAKAN METODE SUPPORT VEKTOR MACHINE Syafi'i Syafi'i; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.422

Abstract

Credit is one of the services provided by banks, credit risk that occurs in the provision of credit loans, in the case that the customer is unable to pay the loan received is always considered by the bank, and supervises the customer to reduce risk. The main risk for banks and financial institutions is to differentiate creditors who have the potential for bad loans, this crisis is a concern for financial institutions about credit risk. SUPPORT VEKTOR MACHINE algorithm is an algorithm used to form a decision tree. The decision tree is a very powerful and well-known classification and prediction method. The richer the information or knowledge contained by the training data, the accuracy of the decision tree will increase. The SUPPORT VEKTOR MACHINE algorithm classification method can determine the credit worthiness of the national civil capital capitals as evidenced by the performance table data consisting of the AUC results, Acuracy results. The results of the application of machine learning using the vector machine support algorithm against cooperative data in KPRI "RUKUN" SMKN 1 Lemahabang to determine creditworthiness based on the results of the Performance Vector from the Support Vector Machine algorithm resulted in smooth prediction, smooth true 130, prediction of jammed, true jam 72, current prediction true jam 41, prediction of jammed true jam 332. The accuracy rate of the performance vector of the support vector algorithm is 80.34%. .
PENERAPAN MACHINE LEARNING UNTUK MENENTUKAN KELAYAKAN KREDIT MENGGUNAKAN METODE SUPPORT VEKTOR MACHINE Syafi'i Syafi'i; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.422

Abstract

Credit is one of the services provided by banks, credit risk that occurs in the provision of credit loans, in the case that the customer is unable to pay the loan received is always considered by the bank, and supervises the customer to reduce risk. The main risk for banks and financial institutions is to differentiate creditors who have the potential for bad loans, this crisis is a concern for financial institutions about credit risk. SUPPORT VEKTOR MACHINE algorithm is an algorithm used to form a decision tree. The decision tree is a very powerful and well-known classification and prediction method. The richer the information or knowledge contained by the training data, the accuracy of the decision tree will increase. The SUPPORT VEKTOR MACHINE algorithm classification method can determine the credit worthiness of the national civil capital capitals as evidenced by the performance table data consisting of the AUC results, Acuracy results. The results of the application of machine learning using the vector machine support algorithm against cooperative data in KPRI "RUKUN" SMKN 1 Lemahabang to determine creditworthiness based on the results of the Performance Vector from the Support Vector Machine algorithm resulted in smooth prediction, smooth true 130, prediction of jammed, true jam 72, current prediction true jam 41, prediction of jammed true jam 332. The accuracy rate of the performance vector of the support vector algorithm is 80.34%. .
PENERAPAN MACHINE LEARNING UNTUK MENENTUKAN KELAYAKAN KREDIT MENGGUNAKAN METODE SUPPORT VEKTOR MACHINE Syafi'i Syafi'i; Odi Nurdiawan; Gifthera Dwilestari
JURSIMA Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.422

Abstract

Credit is one of the services provided by banks, credit risk that occurs in the provision of credit loans, in the case that the customer is unable to pay the loan received is always considered by the bank, and supervises the customer to reduce risk. The main risk for banks and financial institutions is to differentiate creditors who have the potential for bad loans, this crisis is a concern for financial institutions about credit risk. SUPPORT VEKTOR MACHINE algorithm is an algorithm used to form a decision tree. The decision tree is a very powerful and well-known classification and prediction method. The richer the information or knowledge contained by the training data, the accuracy of the decision tree will increase. The SUPPORT VEKTOR MACHINE algorithm classification method can determine the credit worthiness of the national civil capital capitals as evidenced by the performance table data consisting of the AUC results, Acuracy results. The results of the application of machine learning using the vector machine support algorithm against cooperative data in KPRI "RUKUN" SMKN 1 Lemahabang to determine creditworthiness based on the results of the Performance Vector from the Support Vector Machine algorithm resulted in smooth prediction, smooth true 130, prediction of jammed, true jam 72, current prediction true jam 41, prediction of jammed true jam 332. The accuracy rate of the performance vector of the support vector algorithm is 80.34%. .
Co-Authors Gifthera Dwilestari Odi Nurdiawan