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All Journal Jurnal Teknik Komputer AMIK BSI
Fitri, Maysade
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Computer Science & IT

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Application of Random Forest Algorithm To Classify Credit Status of KPR Customers at Bank BTN Based on Machine Learning Fitri, Maysade; Sobri, Ahmad; Rizki, Fido
JURNAL TEKNIK KOMPUTER AMIK BSI Vol 11, No 1 (2025): Periode Januari 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jtk.v11i1.25261

Abstract

In the banking sector, this study is very suitable for determining and improving accuracy and determining credit status classification. This study aims to apply the Exploratory Data Analysis (EDA) method in supporting credit status classification at PT. Bank Tabungan Negara KCP Lubuklinggau Persero Tbk. Exploratory Data Analysis (EDA) as data exploration and Machine Learning Algorithms such as Random Forest as modeling in determining classification. The results show that the Exploratory Data Analysis (EDA) method successfully determines data patterns, while Random Forest in modeling achieves accuracy, recall, Precision, F1-Score of 100% in predicting the credit status of KPR customers. This method is expected to be useful in making decisions on more accurate credit status by the bank.
Co-Authors Rizki, Fido sobri, ahmad