Classification to predict credit card application acceptance using support vector machine
Keywords:
Prediction, Credit card acceptance, Support vector machineAbstract
Credit card creation is one of the banking services that has a large source of risk for business operations. The process of granting credit card functions includes application and customer profile analysis. Customer profile analysis can be determined based on many factors such as savings owned, transfers or cash flow from customer accounts, and income from customer credit applications. This is done as an implementation of the SVM algorithm for the classification of credit card application acceptance using data taken from the Kaggle website. In this research, the SVM method is used with an additional function, namely a kernel trick. From the evaluation of the classification model along with the four kernel functions using the confusion matrix, it is found that the sigmoid kernel has the highest precision and recall percentage of 0.982491857 and recall 0.985300122, while the highest accuracy is produced by the Polynomial Kernel of 98%.
References
[1] Jayanto, D. D.; Wedhatama, C.; Alvian, J. A.; Sulistyo, W. Analisis Perbandingan Metode Penggalian Data Dalam Credit Approval Process. Syntax Literate : Jurnal Ilmiah Indonesia 2021, 6(9), 4382-4391, doi.org/10.36418/syntax-literate.v6i9.1661.
[2] Alvian, F. S.; Carbini, S.; Komarudin, K. Prediksi Kelayakan Pemberian Fasilitas Kartu Kredit Kepada Nasabah Dengan Metode Klasifikasi Data Mining (Studi Kasus : Bank XYZ). Jurnal Computech & Bisnis (e-Journal) 2020, 14(2), 123–128.
[3] Rahayu, W. I.; Prianto, C.; Novia, E. A. Perbandingan Algoritma K-Means dan Naïve Bayes untuk Memprediksi Prioritas Pembayaran Tagihan Rumah Sakit Berdasarkan Tingkat Kepentingan Pada PT. Pertamina (Persero). Jurnal Teknik Informatika 2021, 13(2), 1–8. https://ejurnal.poltekpos.ac.id/index.php/informatika/article/view/1383.
[4] Audina, B.; Fatekurohman, M.; Riski, A. Peramalan Arus Kas dengan Pendekatan Time Series Menggunakan Support Vector Machine. Indonesian Journal of Applied Statistics 2021, 4(1), 34-43, doi.org/10.13057/ijas.v4i1.47953
[5] Fadilah, W. R. U.; Agfiannisa, D.; Azhar, Y. Analisis Prediksi Harga Saham PT. Telekomunikasi Indonesia Menggunakan Metode Support Vector Machine. Fountain of Informatics Journal 2020, 5(2), 45-51 doi.org/10.21111/fij.v5i2.4449
[6] Tamami, M. K.; Kharisudin, I. Komparasi Metode Support Vector Machine dan Naive Bayes Classifier untuk. Indonesian. Journal of Mathematics and Natural Sciences 2023, 46(1), 38–44. https://doi.org/https://doi.org/10.15294/ijmns.v46i1.46174.
[7] Gupta, A.; Kumar, V. Credit Card Fraud Detection Using Machine Learning Techniques. International Journal of Computer Applications 2019, 8(2), 45-62.
[8] Chen, L.; Wang, F. Predicting Credit Card Default Using Neural Networks. Expert Systems with Applications 2018, 11(3), 112-129.
[9] Smith, M.; Jones, P. Comparative Analysis of Credit Scoring Models. Journal of Finance and Banking 2020, 32(4), 789-805.
[10] Patel, R.; Sharma, S. Application of Ensemble Learning in Credit Card Approval Systems. In Proceedings of the International Conference on Machine Learning 2021,Vol. 15, pp. 221-240.
[11] Kim, J.; Lee, H. Credit Limit Estimation Using Decision Trees. Journal of Credit Risk 2017, 15, 103-120.
[12] Kibria, M. G.; Sevkli, M. Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques. International Journal of Machine Learning and Computing 2021, 11(4), 286–290, doi.org/10.18178/ijmlc.2021.11.4.1049
[13] Abdullah, M.F.; Kusrini, K.; Arief, M.R. Prediksi Nilai dan Waktu Kelulusan Mahasiswa Menggunakan Metode SVM (Studi Kasus: Universitas KH A Wahab Hasbullah Jombang. SAINTEKBU 2022, 14(01), 35-44.
[14] Arifianto, T.; Sunaryo, S.; Moonlight, L. S. Penggunaan Metode Support Vector Machine (SVM) Pada Teknologi Mobil Masa Depan Menggunakan Sidik Jari. Jurnal Teknik Informatika dan Teknologi Informasi 2022, 2(2), 3–6.
[15] Charimmah, N.; Lanovia, E.; Usman, K.; Novamizanti, L. Deteksi Kantuk Melalui Citra Wajah Menggunakan Metode Gray Level Co- occurrence Matrix ( GLCM ) dan Klasifikasi Support Vector Machine ( SVM ). In Proceeding Seminar Nasional Teknik Elektro; UIN Sunan Gunung Djati : Bandung, 2019; pp. 174–185.
[16] Rianti, D. L.; Umaidah, Y.; Voutama, A. Tren Marketplace Berdasarkan Klasifikasi Ulasan Pelanggan Menggunakan Perbandingan Kernel Support Vector Machine. STRING (Satuan Tulisan Riset Dan Inovasi Teknologi) 2021, 6(1), 98-105, doi.org/10.30998/string.v6i1.9993
[17] Zhang, Y.; Lee, J.. Credit Card Fraud Detection Using Support Vector Machines. Expert Systems with Applications 2018, 45 (3), 245-260.
[18] Wang, Q.;Liu, C. Credit Scoring Models with Support Vector Machines for Decision Making. International Journal of Banking, Accounting, and Finance 2019, 35(2), 112-128.
[19] Chen, H.; Li, Y. Support Vector Machines for Predicting Credit Card Default: A Comparative Study. Journal of Credit Risk 2020,1, 78-94.
[20] Kim, S.; Park, E. Application of Support Vector Machines in Credit Limit Estimation. Decision Support Systems 2017, 28(4), 315-330.
[21] Lukmana, D. T.; Subanti, S.; Susanti, Y. Analisis Sentimen Terhadap Calon Presiden 2019 dengan Support Vector Machine di Twitter. In Seminar & Conference Proceedings of UMT, 2019; pp. 154–160.
[22] Gata, W.; Amsury, F.; Wardhani, N.K.; Sugiyarto, I.; Sulistyowati, D.N.; Saputra, I. Informative tweet classification of the earthquake disaster situation in indonesia. In 2019 5th International Conference on Computing Engineering and Design (ICCED) 2019, pp. 1-6.
[23] Molydah, S. Analisis Perbandingan Implementasi Sarima Dan Support Vector Machine (SVM) Dalam Prediksi Jumlah Wisatawan Mancanegara. Skripsi 2018, 1–55.
