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Ipin Sugiyarto | email@example.com
Ipin Sugiyarto, Bibit Sudarsono, Umi Faddillah
Credit analysis needs to identify and assess the factors that can affect customers in returning credit. Accurate measurement and good management ability in dealing with credit risk is an effort to save the economic operations unit and be beneficial for a stable and healthy financial system. Data mining prediction techniques are used to determine credit risk. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method which consists of several stages, namely Business Understanding (dataset), Data Processing (Feature Selection PCA & Dimension Reduce), Algorithm Models (NN+PSO, SVM, LR), Evaluation (Validation and Accuracy). This study has tested the model using a neural network using the PCA selection feature and optimized with the Particle Swarm Optimize (PSO) algorithm to predict credit card approval. Several experiments were conducted to see the best results. The results of this study prove that the use of a single Neural Network method produces an accuracy of 80.33%. whereas the use of PCA + Neural Network + PSO hybrid method has been proven to increase accuracy to 82.67%. Likewise, the AUC NN value of 0.706 increased to 0.749 when the Neural Network was optimized using PSO and used feature selection. The purpose of this study is to implement and compare Support Vector Machine, Logistic Regression and Neural Network algorithms based on PCA and optimize PSO (Particle Swarm Optimization) to improve accuracy in predictions of credit card approvals.
Budiharto, Widodo. (2016). Machine Learning & Computational Intelligence. ANDI, Yogyakarta.
Inspiring. (2018). Understanding Debit and Credit in Financial Statements. Retrieved from https://www.inspiring.id/peng understanding-debit-and-credit/
I. C. Yeh & C. Hui Lien. (2009). The comparison of data mining technique for predictive accuracy of probability of default of credit card client. Expert Syst. Appl., Vol.36, No.2 Part 1, pp. 2473-2480.
Lee, T. S., Chiu, C. C., Lu, C. J., & Chen, I. F. (2002). Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications, 23(3), 245–254. https://doi.org/10.1016/S0957-4174(02)00044-1
Menarianti, I. (2015). Data mining classification in determining lending for cooperative customers. Journal of Scientific Science, 1 (1), 1–10.
Pasha, M., Fatima, M., Dogar, A. M., & Shahzad, F. (2017). Performance Comparison of Data Mining Algorithms for the Predictive Accuracy of Credit Card Defaulters. International Journal of Computer Science and Network Security, 17(3), 178–183.
Pohan. B. Achmad & Sensuse I. Dana. (2014). Optimasi artificial neural network menggunakan genetic algorithm untuk prediksi uji coba marshal pada campuran aspal beton. Journal Ilmiah Prodi. Magister Ilmu Komputer. STMIK Nusa Mandiri.
Ronald L., Iman & W. J. Conover. (2012). A measure of top-down correlation. Technimetrics, Vol.29, No.3
S. F. Putra, R. Pradina & I. Hafidz. (2016). Feature selection pada dataset faktor kesiapan bencana pada provinsi di Indonesia menggunakan metode PCA (Princial Component Analysis). J. Tek. Its, vol.5, No.2, pp. 5-9.
Sugiyarto, I & Gata, W. (2018). thesis book
S. Umair. (2014, 1 Nov). A comparative study of data mining process models (KDD, CRIPS-DM and SEMMA). IJISR, Vol.12, No.1, pp. 217-222.
T. S. Lee, C. C. Chiu, C. J. Lu & I. F. Chen. (2002). Credit scoring using the hybird neural discriminant tehcnique. Expert Syst. Appl. Vol.23, No.3, pp. 245-254.
Vankatesh, A. & Jacob, G. S. (2016). Prediction of Credit-Card Defaulters: A Comparative Study on Performance of Classifiers.
Yuxi. Gao. (2018). An improved hybird group intelligent algorithm based on artificial bee colony and particle swarm optimatizion. International Conf. On Virtual Reality and Intelligent System.
Y. B. Wah & I. R. Ibrahim. Using data mining predictive models to classify credit card applicant. pp. 394-398.
Zurada, C. & Kunene, K. (2011). Comparison of the Performance of Computational Intelligence Methods for Loan Granting Decisions. Proceeding of the 44th Hawaii International Conference on System Science.