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All Journal JURSIMA (Jurnal Sistem Informasi dan Manajemen) JURSIMA International Journal of Economics and Management Research
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%. .
THE EFFECT OF RETURN ON EQUITY ON COMPANY VALUE WITH THE INDEPENDENT BOARD OF COMMISSIONERS AND THE AUDIT COMMITTEE AS MODERATING VARIABLES Achmad Daengs GS; Enny Istanti; Diana Zuhro; Retno Susanti; Sutini Sutini; Ruchan Sanusi; Syafi'i Syafi'i; Sutopo Sutopo; Ali Muhdor; Bramastyo Kusumonegoro
International Journal of Economics and Management Research Vol. 1 No. 2 (2022): August: International Journal of Economics and Management Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/ijemr.v1i2.36

Abstract

The purpose of this study was to analyze financial performance as measured by Return on Equity (ROE) on firm value as measured by Tobin's Q as well as to analyze the Corporate Governance mechanism as a moderating variable. The aim of this research is to find empirical proof about (a) the influence of financial performance to firm value, (b) the influence of independent commissioners as moderating variable in the relationships between financial performance and firm value, (c) the influence of audit committee as moderating variable in relationships between financial performance and firm value. The sample of this research is mining firms which are listed on the Indonesia Stock Exchange (IDX) over 2008-2011. The research sample are 10 firms with 39 observations. To analyzed the data using software SPSS ver. 16.0.
Co-Authors Ali Muhdor Bramastyo Kusumonegoro Diana Zuhro Enny Istanti Gifthera Dwilestari GS , Achmad Daengs Odi Nurdiawan Retno Susanti Ruchan Sanusi Sutini Sutini Sutopo Sutopo