IMPLEMENTATION OF HYPERPARAMETER TUNING IN RANDOM FOREST ALGORITHM FOR LOAN APPROVAL PREDICTION

Dwi Sandhi Bhakti; Agung Prasetyo; Primandani Arsi

doi:10.52436/1.jutif.2024.5.4.2032

Dwi Sandhi Bhakti Informatics, Computer Science Faculty, Universitas Amikom Purwokerto, Indonesia
Agung Prasetyo Informatics, Computer Science Faculty, Universitas Amikom Purwokerto, Indonesia
Primandani Arsi Informatics, Computer Science Faculty, Universitas Amikom Purwokerto, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.4.2032

Keywords: hyperparameter tuning, loan approval, machine learning, non performing loan, random forest

Abstract

The risk of non-performing loan is a significant issue in the financial industry, including banks and cooperatives. Loan default risks can occur due to various reasons, and one of them is the negligence of staff or subjective decision-making in loan approval. The proposed solution is to enhance an objective and accurate loan approval decision-making system through the application of machine learning technology, aiming to reduce the risk of loan default. The Random Forest algorithm has proven to be the best in predicting loan approval compared to other supervised learning models. Optimization was performed on the Random Forest algorithm through hyperparameter tuning and data balancing using SMOTE. The best accuracy obtained from several experiments was 86.2%. By implementing optimizations on the Random Forest algorithm, it is expected that the model can make loan approval predictions more objectively and accurately, serving as a reference for future loan approval system development.

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References

Kasmir, Manajemen Perbankan. Jakarta: Rajawali Pers, 2017.

Kurniawan, D. Ahmad, and D. Kriestanto, “Penerapan Naïve Bayes untuk Prediksi Kelayakan Kredit,” J. Inform. dan Komput., vol. 1, no. 1, pp. 19–23, 2016.

A. Malik, “Pengaruh Loan To Deposit Ratio (LDR) Terhadap Profitabilitas Dengan Non Performing Loan (NPL) Sebagai Variabel Intervening Pada Subsektor Perbankan,” Sains Manaj. J. Manaj. Unsera, vol. 6, no. 1, pp. 13–22, 2020.

S. P. Dewi, “Pengaruh Capital Adequacy Ratio, Non Performing Loan, Loan to Deposit Ratio dan Efisiensi Operasional Terhadap Profitabilitas Perbankan Yang Terdaftar Di Bursa Efek Indonesia,” J. Akunt., vol. 18, no. 3, pp. 422–437, 2014.

F. Sandria, “NIM Perbankan RI Super Tinggi, Kredit Macet Apa Kabar?,” 2023. https://www.cnbcindonesia.com/market/20230306131512-17-419214/nim-perbankan-ri-super-tinggi-kredit-macet-apa-kabar (accessed Mar. 16, 2024).

CEIC DATA, “Indonesia Ratio Kredit Bermasalah,” 2023. https://www.ceicdata.com/id/indicator/indonesia/non-performing-loans-ratio (accessed Mar. 16, 2024).

Mahmoeddin, Melacak kredit bermasalah. Jakarta: Pustaka Sinar Harapan, 2010.

E. Alpaydin, Machine Learning. Cambridge: MIT press, 2021.

M. A. Sheikh, A. K. Goel, and T. Kumar, “An approach for prediction of loan approval using machine learning algorithm,” in 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 2020.

A. Nata and S. Suparmadi, “Analisis Sistem Pendukung Keputusan Dengan Model Klasifikasi Berbasis Machine Learning Dalam Penentuan Penerima Program Indonesia Pintar,” J. Sci. Soc. Res., vol. 5, no. 3, 2021.

P. Tumuluru, L. R. Burra, M. Loukya, S. Bhavana, H. M. H. CSaiBaba, and N. Sunanda, “Comparative Analysis of Customer Loan Approval Prediction using Machine Learning Algorithms,” in 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), 2022.

L. Zhu, D. Qiu, D. Ergu, C. Ying, and K. Liu, “A study on predicting loan default based on the random forest algorithm,” Procedia Comput. Sci., vol. 162, pp. 503–513, 2019.

J. Wu, X.-Y. Chen, H. Zhang, L.-D. Xiong, H. Lei, and S.-H. Deng, “Hyperparameter optimization for machine learning models based on Bayesian optimization,” ournal Electron. Sci. Technol., vol. 17, no. 1, pp. 26–40, 2019.

D. De Silva and D. Alahakoon, “An artificial intelligence life cycle: From conception to production,” Patterns, vol. 3, no. 6, 2022.

M. Solekhah, “PEMANFAATAN TEKNOLOGI ARTIFICIAL INTELLIGENCE CUBATBOT (CULTURE BALINESE CHATBOT) SEBAGAI INFORMASI KEBUDAYAAN BALI,” Kreat. Pada Pengabdi. Masy., vol. 1, no. 2, 2023.

R. Chakma, J. Paul;, and S. Dhir, “Organizational Ambidexterity: A Review and Research Agenda,” IEEE Trans. Eng. Manag. ( Early Access ), pp. 1–17, 2021, doi: 10.1109/TEM.2021.3114609.

V. Ukani, “Loan Eligible Dataset,” 2020. https://www.kaggle.com/datasets/vikasukani/loan-eligible-dataset (accessed Mar. 21, 2024).

Suyebaanjum, “Mastering Null Value Handling: A Comprehensive Guide to Replacing Missing Data in Your Dataset,” 2023. https://medium.com/@suyebaanjum98/mastering-null-value-handling-a-comprehensive-guide-to-replacing-missing-data-in-your-dataset-1a0bf711e531 (accessed Apr. 30, 2024).

Iswanto, Tulus, and Poltak, “COMPARISON OF FEATURE SELECTION TO PERFORMANCE IMPROVEMENT OF K-NEAREST NEIGHBOR ALGORITHM IN DATA CLASSIFICATION,” J. Tek. Inform., vol. 3, no. 6, pp. 1709–1716, 2022.

M. H. Ariansyah, E. N. Fitri, and S. Winarno, “IMPROVING PERFORMANCE OF STUDENTS’ GRADE CLASSIFICATION MODEL USES NAÏVE BAYES GAUSSIAN TUNING MODEL AND FEATURE SELECTION,” J. Tek. Inform., vol. 4, no. 3, pp. 493–501, 2023.

A. Altmann, L. Toloşi, O. Sander, and T. Lengauer, “Permutation importance: a corrected feature importance measure,” Bioinformatics, vol. 26, no. 10, pp. 1340–1347, 2010.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” J. Artif. Intell. Res., vol. 16, pp. 321–357, 2002.

N. N. Sholihah and A. Hermawan, “IMPLEMENTATION OF RANDOM FOREST AND SMOTE METHODS FOR ECONOMIC STATUS CLASSIFICATION IN CIREBON CITY,” J. Tek. Inform., vol. 4, no. 6, pp. 1387–1397, 2023.

R. Faurina, M. J. Gazali, and I. D. A. Herani, “Optimization Of Disease Prediction Accuracy Through Artificial Neural Network (ANN) Algorithms In Diagnese Application,” J. Tek. Inform., vol. 5, no. 2, pp. 339–347, 2024.

S. Ambesange, R. Nadagoudar, R. Uppin, V. Patil, S. Patil, and S. Patil, “Liver Diseases Prediction using KNN with Hyper Parameter Tuning Techniques,” in 2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC), pp. 1–6. 2020.

A. S. Munir and R. Waluyo, “Optimasi Prediksi Kematian pada Gagal Jantung: Analisis Perbandingan Algoritma Pembelajaran Ensemble dan Teknik Penyeimbangan Data pada Dataset,” J. Sist. dan Teknol. Inf., vol. 12, no. 2, pp. 365–372, 2024.

F. P. Utama, R. Faurina, T. Mardiansyah, and A. Vatresia, “Scientific Articles Recommendation System Based On User’s Relatedness Using Item-Based Collaborative Filtering Method,” J. Tek. Inform., vol. 4, no. 3, pp. 467–475, 2023.

Madaan, Mehul, A. Kumar, C. Keshri, R. Jain, and P. Nagrath, “Loan default prediction using decision trees and random forest: A comparative study,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1022, no. 1, p. 012042, 2021.

L. U. Bhanu and S. Narayana, “Customer Loan Prediction Using Supervised Learning Technique,” Int. J. Sci. Res. Publ., vol. 11, no. 6, pp. 403–407, 2021.