OPTIMASI OPTIMAL BINNING MENGGUNAKAN ALGORITMA GENETIKA UNTUK MODEL PENILAIAN KREDIT

Rahmat Rian Hidayat

doi:10.30591/jpit.v7i3.3841

OPTIMASI OPTIMAL BINNING MENGGUNAKAN ALGORITMA GENETIKA UNTUK MODEL PENILAIAN KREDIT

Rahmat Rian Hidayat

Abstract

Credit scoring in financial institutions/banks is very important in determining whether it is feasible or not to receive borrowed funds. Therefore, credit scoring is the most important part of the company and has a great impact on the company's profits. Therefore, there are very strict regulations in the credit scoring process. This credit evaluation uses customer data collected by filling out forms, customer financial history data, and various other data sources. The Optimal Binning Algorithm is an industry-standard method used by data scientists to build credit scoring models. However, data scientists need to analyze each variable or feature individually to understand how the pattern of each variable affects the goal. When there are hundreds or even thousands of variables, it will take a lot of time, From a business perspective, it usually requires a solution that is as fast and effective as possible. To solve this problem, we propose the implementation of evolutionary algorithms to find the best solution of variable value constraints to form optimal binning. Genetic algorithms as a branch of adaptive evolutionary algorithms commonly used to solve value searches in optimization problems. Through this research, genetic algorithms can solve the problem of determining the limitations in the formation of optimal binning ariables very well. The results of the comparison between the classifier methods provide evidence that Logistic Regression can consistently have stable performance. In addition, it can maintain the performance of the predictive model despite changes in trends in the test data, while other methods get a decrease in performance results. In the Logistics Regression t test data, he received an AUC of 0.71

Keywords

Kata Kunci: Credit Scoring, Optimal Binning, Algoritma Genetika, Data Scientist

Full Text:

References

P. Mironchyk. and V. Tchistiakov, “Monotone optimal binning algorithm for credit risk modeling.,” Utr. Work. Pap., 2017.

G. Zeng, “Necessary condition for a good binning algorithm in credit scoring,” Appl. Math. Sci., vol. 65, pp. 3229–3242., 2014.

J. P. Barddal, L. Loezer, F. Enembreck, and R. Lanzuolo, “Lessons Learned from Data Stream Classification Applied to Credit scoring,” Expert Syst. Appl., no. 113899, 2020.

S. Lessmann, B. Baesens, H. V. Seow, and L. C. Thomas, “Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research,” Eur. J. Oper. Res., vol. 34(4), pp. 124–136, 2015.

X. Zhang, Y. Yang, and Z. Zhou, “A Novel Credit scoring Model Based on Optimized Random Forest.,” IEEE 8th Annu. Comput. Commun. Work. Conf., pp. 60–65, 2018.

L. Junior, F. Nardini, C. Renso, R. Trani, and J. Macedo, “A Novel Approach to Define the Local Region of Dynamic Selection Techniques in Imbalanced Credit scoring Problems.,” Expert Syst. Appl., vol. 113351, 2020.

C. Wang, C. Deng, and S. Wang, “Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost,” Pattern Recognit. Lett., pp. 190–197, 2020.

S. V. Kulkarni and S. N. Dhage, “Advanced Credit Score Calculation using Social Media and Machine Learning.,” J. Intell. Fuzzy Syst., vol. 2373-2380., 2019.

P. Pławiak, M. Abdar, and U. . Acharya, “Application of new deep genetic cascade ensemble of svm classifiers to predict the australian credit scoring.,” Appl. Soft Comput. 84, vol. 105740, 2019.

P. R. Hahn, J. S. Murray, and C. M. Carvalho, “Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects (with discussion).,” Bayesian Anal. 15 (13), pp. 965–1056., 2020.

I. K. A. Wijaya, R. S. Hartati, and I. W. Sukerayasa, “Optimasi Penempatan Kapasitor Bank Untuk Perbaikan Rugi Daya Pada Penyulang Saba Menggunakan Algoritma Genetika,” J. SPEKTRUM, vol. 6, no. 2, p. 7, 2019, doi: 10.24843/spektrum.2019.v06.i02.p02.

G. Algorithms, “Estimasi Biaya Proyek Perangkat Lunak Menggunakan JST dan Algoritma Genetika,” Bimipa, vol. 25, no. 3, pp. 253–261, 2019.

I. K. Ariek Astana Adi, I. G. Dyana Arjana, and W. Setiawan, “Studi Optimasi Reposisi Recloser Untuk Meningkatkan Keandalan Pada Penyulang Blahkiuh Menggunakan Metode Algoritma Genetika,” J. SPEKTRUM, vol. 5, no. 2, p. 272, 2018, doi: 10.24843/spektrum.2018.v05.i02.p34.

N. Faisal, R. S. Hartati, and I. W. Sukerayasa, “Implementasi Fuzzy Logic dan Algoritma Genetika dalam Pemebebanan Ekonomis pada Sistem Pembangkitan di Bali,” Maj. Ilm. Teknol. Elektro, vol. 16, no. 3, p. 99, 2018, doi: 10.24843/mite.2017.v16i03p16.

Suprayogi, M. Syafaruddin, and I. T. B. Mahaputra Fakultas Teknologi Industri, “Pemecahan Masalah Rute Kendaraan Dengan Trip Majemuk, Jendela Waktu Dan Pengantaran-Penjemputan Simultan Menggunakan Algoritma Genetika,” J@ti Undip J. Tek. Ind., vol. 12, No. 2, 2017.

M. B. Óskarsdóttir, C. Sarraute, J. Vanthienen, and B. Baesens, “The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics.,” Appl. Soft Comput., vol. 74, pp. 26–39, 2019.

R. Gen, M., Cheng, “Genetic algorithms and engineering optimization,” John Wiley Son, vol. (Vol. 7, p. 12, 199AD.

D. Ilter, E. Deniz, and O. Kocadagli, “Hybridized artificial neural network classifiers with a novel feature selection procedure based genetic algorithms and information complexity in credit scoring.,” Appl. Stoch. Model. Bus. Ind., vol. 37(2), pp. 203–228, 2021.

H. R. Kazemi, K. Khalili‐Damghani, and S. Sadi‐Nezhad, “Tuning structural parameters of neural networks using genetic algorithm: A credit scoring application.,” Expert Syst., vol. 38(7), no. e12733. ., 2021.

DOI: https://doi.org/10.30591/jpit.v7i3.3841