Klasifikasi Kelancaran Kredit Dengan Metode Random Forest
DOI:
https://doi.org/10.29080/systemic.v5i2.713Keywords:
Data Mining, Random Forest, Decision Support System, Credit RiskAbstract
This research contains the discussion the use of machine learning for doing prediction toward a good loan using random forest algorithm. This prediction will become basic reference for the bank to continue in evaluating credit risk. At this time, the absence of decision support system for doing prediction toward a good loan became a problem to the bank in attempt to reduce the credit risk. Therefore, a decision support system with machine learning modelling using random forest algorithm was built in predicting a good loan. Based on the result of this research, the prediction model being evaluated in several scenarios and having an average result 96,47%
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References
K. Arun, G. Ishan, and K. Sanmeet, “Loan Approval Prediction based on Machine Learning Approach,” IOSR J. Comput. Eng., pp. 18–21, 2016.
M. Ali, Asset Liability Management, Menyiasati Risiko Pasar dan Operasional dalam Perbankan. Jakarta: PT. Elex Media Kompetindo Kelompok Gramedia, 2004.
P. Manurung, “Sistem Pendukung Keputusan Seleksi Penerima Beasiswa Dengan Metode AHP dan TOPSIS,” Universitas Sumatera Utara, 2010.
C. Zhang, C. Liu, X. Zhang, and G. Almpanidis, “An up-to-date comparison of state-of-the-art classification algorithms,” Expert Syst. Appl., vol. 82, pp. 128–150, 2017.
C. B. Putri, “Klasifikasi Nasabah Thera Bank Membeli Personal Loan Menggunakan Metode Klasifikasi Dalam Machine Learning Pendahuluan Metodologi Penelitian,” Institut Teknologi Sepuluh Nopember, 2018.
T. K. Ho, “Random Decision Forest,” vol. 47, pp. 4–5, 1995.
C. D. Sutton, “Classification and Regression Trees, Bagging, and Boosting,” Handb. Stat., vol. 24, no. 04, pp. 303–329, 2004.
M. van Wezel and R. Potharst, “Improved customer choice predictions using ensemble methods,” Eur. J. Oper. Res., vol. 181, no. 1, pp. 436–452, 2007.
I. M. Budi Adnyana, “Prediksi Lama Studi Mahasiswa Dengan Metode Random Forest (Studi Kasus : Stikom Bali),” CSRID (Computer Sci. Res. Its Dev. Journal), vol. 8, no. 3, pp. 201–208, 2016.
J. Han, M. Kamber, and J. Pei, Data Mining Concept and Techniques. 2011.
R. Kohavi and F. Provost, “Glossary of Terms,” Mach. Learn., vol. 30, pp. 271–274, 1998.
M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf. Process. Manag., vol. 45, no. 4, pp. 427–437, 2009.
