PREDICTIVE MODEL FOR COOPERATIVE LOAN RECIPIENT ELIGIBILITY USING SUPERVISED MACHINE LEARNING
DOI:
https://doi.org/10.33480/jitk.v11i2.7235Keywords:
classification, DSS, machine learning, non-performing loans, random forestAbstract
Non-performing loans remain a critical challenge for cooperatives as they can undermine financial stability, erode member trust, and impede institutional growth. This study develops a predictive model for cooperative loan eligibility using supervised machine learning techniques and a novel three-class classification framework, Approved, Consideration, and Rejected, to support more objective and transparent decision-making. A dataset of 1,000 borrower records containing demographic and financial attributes was analyzed using Naive Bayes, Decision Tree, and Random Forest algorithms implemented in RapidMiner. The Random Forest algorithm achieved the best predictive performance with an accuracy of 96.02%, demonstrating its robustness and reliability compared to the other models. The proposed three-class system differentiates this study from conventional binary classification approaches, enabling finer distinctions among borrower categories and promoting fairness in cooperative credit evaluations. The findings provide practical guidance for cooperatives to adopt data-driven, transparent, and accountable decision-making systems that reduce manual bias and strengthen financial inclusion. Overall, the proposed three-class model built through a supervised learning framework offers a reliable, fair, and scalable solution to support sustainable lending practices and enhance risk management in cooperative institutions.
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V. Chang, Q. Ariel, X. Shola, H. Akinloye, V. Benson, and K. Hall, “Prediction of bank credit worthiness through credit risk analysis : an explainable machine learning study,” Ann. Oper. Res., vol. 354, pp. 247–271, 2025, doi: https://doi.org/10.1007/s10479-024-06134-x.
M. Z. Hussain et al., “Bank Loan Prediction System Using Machine Learning Models,” in 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), IEEE, Apr. 2024, pp. 1–5. doi: 10.1109/I2CT61223.2024.10543786.
H. Yuliansyah, S. Sulistyawati, T. W. Sukesi, S. A. Mulasari, and W. N. S. Wan Ali, “Artificial intelligence in malnutrition research: a bibliometric analysis,” Bull. Soc. Informatics Theory Appl., vol. 7, no. 1, pp. 32–42, Jul. 2023, doi: 10.31763/businta.v7i1.605.
P. S. Saini, A. Bhatnagar, and L. Rani, “Loan Approval Prediction using Machine Learning: A Comparative Analysis of Classification Algorithms,” in 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), IEEE, May 2023, pp. 1821–1826. doi: 10.1109/ICACITE57410.2023.10182799.
R. Rajunaidi, H. Yuliansyah, S. Sunardi, and M. Murinto, “Predicting Loan Eligibility With Support Vector Machine: A Machine Learning Approach,” J. Teknol. dan Sist. Inf., vol. 11, no. 3, pp. 501–508, 2025, doi: https://doi.org/10.33330/jurteksi.v11i3.3876.
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), IEEE, Feb. 2022, pp. 349–353. doi: 10.1109/ICAIS53314.2022.9742800.
D. W. Sinaga, A. Z. Syah, and E. Saputra, “Implementasi Simple Multi Attribute Rating Technique untuk Menentukan Kelayakan Pengajuan Kredit Mobil,” Edumatic J. Pendidik. Inform., vol. 8, no. 1, pp. 203–211, Jun. 2024, doi: 10.29408/edumatic.v8i1.25687.
I. Kurniawan, D. C. P. Buani, A. Abdussomad, W. Apriliah, and R. A. Saputra, “Implementasi Algoritma Random Forest Untuk Menentukan Penerima Bantuan Raskin,” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 2, pp. 421–428, Apr. 2023, doi: 10.25126/jtiik.20236225.
C. N. Sujatha, A. Gudipalli, B. Pushyami, N. Karthik, and B. N. Sanjana, “Loan Prediction Using Machine Learning and Its Deployement On Web Application,” in 2021 Innovations in Power and Advanced Computing Technologies (i-PACT), IEEE, Nov. 2021, pp. 1–7. doi: 10.1109/i-PACT52855.2021.9696448.
D. Paul, D. P. Singh, N. Purusothaman, R. Lal, S. Singh, and C. Kalaiarasan, “Predictive Modeling for Bank Loan Approval : From Data to Decisions,” Procedia Comput. Sci., vol. 259, pp. 1426–1431, 2025, doi: 10.1016/j.procs.2025.04.097.
B. Spoorthi, S. S. Kumar, A. P. Rodrigues, R. Fernandes, and N. Balaji, “Comparative Analysis of Bank Loan Defaulter Prediction Using Machine Learning Techniques,” in 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), IEEE, Nov. 2021, pp. 24–29. doi: 10.1109/DISCOVER52564.2021.9663662.
M. Rashid, B. S. Bari, Y. Yusup, M. A. Kamaruddin, and N. Khan, “A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction,” IEEE Access, vol. 9, pp. 63406–63439, 2021, doi: 10.1109/ACCESS.2021.3075159.
E. Hussein Sayed, A. Alabrah, K. Hussein Rahouma, M. Zohaib, and R. M. Badry, “Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing,” IEEE Access, vol. 12, pp. 193997–194019, 2024, doi: 10.1109/ACCESS.2024.3509774.
P. Khalili, M. Kargari, M. A. Rastegar, and M. Alavi, “A Hybrid Method for Online Loan Default Prediction Using Machine Learning Techniques,” in 2025 11th International Conference on Web Research (ICWR), IEEE, Apr. 2025, pp. 503–508. doi: 10.1109/ICWR65219.2025.11006211.
R. Nancy Deborah, S. Alwyn Rajiv, A. Vinora, C. Manjula Devi, S. Mohammed Arif, and G. S. Mohammed Arif, “An Efficient Loan Approval Status Prediction Using Machine Learning,” in 2023 International Conference on Advanced Computing Technologies and Applications (ICACTA), IEEE, Oct. 2023, pp. 1–6. doi: 10.1109/ICACTA58201.2023.10392691.
S. Riyadi, M. M. Siregar, K. fadhli F. Margolang, and K. Andriani, “Analysis Of Svm And Naive Bayes Algorithm In Classification Of Nad Loans In Save And Loan Cooperatives,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. 8, no. 3, pp. 261–270, Aug. 2022, doi: 10.33330/jurteksi.v8i3.1483.
B. Prasojo and E. Haryatmi, “Analisa Prediksi Kelayakan Pemberian Kredit Pinjaman dengan Metode Random Forest,” J. Nas. Teknol. dan Sist. Inf., vol. 7, no. 2, pp. 79–89, Sep. 2021, doi: 10.25077/TEKNOSI.v7i2.2021.79-89.
H. Yuliansyah, Z. A. Othman, and A. A. Bakar, “A new link prediction method to alleviate the cold-start problem based on extending common neighbor and degree centrality,” Phys. A Stat. Mech. its Appl., vol. 616, p. 128546, Apr. 2023, doi: 10.1016/j.physa.2023.128546.
H. Yuliansyah, R. A. P. Imaniati, A. Wirasto, and M. Wibowo, “Predicting Students Graduate on Time Using C4.5 Algorithm,” J. Inf. Syst. Eng. Bus. Intell., vol. 7, no. 1, p. 67, Apr. 2021, doi: 10.20473/jisebi.7.1.67-73.
E. H. Sayed, A. Alabrah, K. Hussein Rahouma, M. Zohaib, and R. M. Badry, “Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing,” IEEE Access, vol. 12, pp. 193997–194019, 2024, doi: 10.1109/ACCESS.2024.3509774.
I. Aruleba and Y. Sun, “Effective Credit Risk Prediction Using Ensemble Classifiers With Model Explanation,” IEEE Access, vol. 12, no. August, pp. 115015–115025, 2024, doi: 10.1109/ACCESS.2024.3445308.
Z. Chen, “Machine Learning in Credit Risk Assessment: A Comparative Analysis of Different Models,” SSRN Electron. J., 2025, doi: 10.2139/ssrn.5006192.
J. P. Noriega, L. A. Rivera, and J. A. Herrera, “Machine Learning for Credit Risk Prediction: A Systematic Literature Review,” Data, vol. 8, no. 11, p. 169, Nov. 2023, doi: 10.3390/data8110169.
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Copyright (c) 2025 Rajunaidi Rajunaidi, Herman Yuliansyah, Sunardi Sunardi, Murinto Murinto
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