PREDIKSI PERSETUJUAN PINJAMAN BANK MENGGUNAKAN METODE RANDOM FOREST

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Published: 2022-04-20
Keywords:
Loans, Decision Tree, Random Forest, Logistic Regression, Prediction
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Alvian Wijaya

Abstract

With the current growth in the banking sector, the demand for loans in banks is increasing. However, the challenge faced by banks in providing loans is assessing whether individuals can repay the loan or default on it. This research will collect data such as income, credit score, employment status, loan tenure, loan amount, asset value, and loan status of loan applicants. Subsequently, using the Decision Tree, Random Forest, and Logistic Regression algorithms, their highest evaluation values will be compared. The aim of this research is to predict, with machine learning algorithms, whether loan applicants are eligible for a loan or not. The research stages involve Data Understanding, Feature Extraction, Data Pre-Processing, Modeling, and insight analysis. From the conducted research, after the comparison with the algorithms used, the Random Forest algorithm yielded the highest value compared to other algorithms, with an accuracy value of 0.978.

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Vol. 17 No. 1 (2022): Jurnal Komputer dan Informatika

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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