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Sembiring, Samuel
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Computer Science & IT Economics, Econometrics & Finance

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Analisis Algoritma J48 Pada Pengambilan Keputusan Pemberian Pinjaman Kepada Calon Nasabah Silitonga, Agnes Irene; Ginting, Lukas; Sinaga, Enjelina; Zega, Elson; Sembiring, Samuel; Simamora, Yoakim
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp281-293

Abstract

This research aims to analyze the stages of decision making for granting loans to prospective customers using the J48 Algorithm. Using the "Loan-Approval-Prediction-Dataset" dataset obtained from Kaggle, this research will build a decision tree model that can provide insight into the key factors that influence the decision. It is hoped that the results of this research can contribute to financial institutions in increasing accuracy, efficiency and objectivity in the credit evaluation process, as well as helping prospective customers understand the factors that need to be considered to increase their chances of loan approval.
Analisis Algoritma J48 Pada Pengambilan Keputusan Pemberian Pinjaman Kepada Calon Nasabah Silitonga, Agnes Irene; Ginting, Lukas; Sinaga, Enjelina; Zega, Elson; Sembiring, Samuel; Simamora, Yoakim
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp281-293

Abstract

This research aims to analyze the stages of decision making for granting loans to prospective customers using the J48 Algorithm. Using the "Loan-Approval-Prediction-Dataset" dataset obtained from Kaggle, this research will build a decision tree model that can provide insight into the key factors that influence the decision. It is hoped that the results of this research can contribute to financial institutions in increasing accuracy, efficiency and objectivity in the credit evaluation process, as well as helping prospective customers understand the factors that need to be considered to increase their chances of loan approval.
Co-Authors Agnes Irene Silitonga Enjelina Sinaga Ginting, Lukas Yoakim Simamora Zega, Elson