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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Informatika Polinema (JIP)
Fadillah, Algies Rifkha
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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Analisis Perbandingan Linear Regression dan Random Forest Regression untuk Prediksi Batas Kredit: Pendekatan Optimasi Hyperparameter Fadillah, Algies Rifkha; Fauzan, Mohamad Nurkamal
Jurnal Informatika Polinema Vol. 10 No. 4 (2024): Vol. 10 No. 4 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i4.5700

Abstract

Penelitian ini mengeksplorasi penggunaan Linear Regression dan Random Forest Regression untuk memprediksi batas maksimal pinjaman kredit dalam industri keuangan. Metode ini digunakan untuk membandingkan performa prediksi dengan dua set fitur yang berbeda. Setelah melakukan optimasi hyperparameter menggunakan Optuna, hasil menunjukkan bahwa Random Forest Regression memberikan akurasi prediksi yang lebih tinggi dibandingkan Linear Regression, dengan nilai RMSE terendah sebesar 7.90% dan MAE terendah sebesar 4.72%. Penggunaan 4 fitur menunjukkan sedikit peningkatan dalam akurasi dibandingkan 7 fitur, meskipun tidak signifikan. Hasil ini menyarankan penggunaan Random Forest Regression untuk meningkatkan keakuratan dalam menetapkan batas kredit, mengurangi risiko kredit, dan meningkatkan stabilitas lembaga keuangan. Dengan demikian, pengoptimalan hyperparameter dengan Optuna dapat meningkatkan performa prediksi model regresi dalam konteks ini.
Systematic Literature Review: Identifying Key Variables and Measuring Maximum Loan Limits Fadillah, Algies Rifkha; Fauzan, Mohamad Nurkamal
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 2 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i2.1156

Abstract

This systematic literature review aims to identify key variables and measurement methods for determining maximum credit loan limits, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The complexity of setting an optimal credit limit to manage credit risk effectively presents a significant challenge. Establishing an efficient maximum loan limit is essential to mitigate credit risk, as an overly high limit increases default potential, while an excessively low limit restricts the financial institution's growth. This study identifies key variables and measurement methods, including Machine Learning techniques, Neural Networks, and traditional statistical approaches. Machine Learning models, such as Random Forest and Gradient Boosting, often surpass traditional methods in handling large, unstructured datasets due to their capacity for modeling complex, non-linear relationships. Conversely, traditional methods like logistic regression may be more suitable for smaller datasets, offering better interpretability and ease of use. The results indicate that systematic variable identification and the use of appropriate measurement methods can enable financial institutions to manage credit loan risk more effectively, supporting the development of sound credit policies.
Co-Authors Fauzan, Mohamad Nurkamal