Klasifikasi Penentuan Pengajuan Kartu Kredit Menggunakan  K-Nearest Neighbor

Yogiek Indra Kurniawan; Tiyssa Indah Barokah

doi:10.33557/jurnalmatrik.v22i1.843

Yogiek Indra Kurniawan Universitas Jenderal Soedirman
Tiyssa Indah Barokah Universitas Muhammadiyah Surakarta

DOI: https://doi.org/10.33557/jurnalmatrik.v22i1.843

Keywords: classification, credit card determination, data mining, K-Nearest Neighbor

Abstract

A credit card is a device payment issued by the bank certain made of plastic and useful as a tool payment on credit carried out by the owner of the card or in accordance with the name of listed in a credit card is on when making purchases goods or services. The problems facing in giving a credit cards to customers bank that have signed up is difficult to determine the category of a credit cards in accordance with the customer bank. By doing this research is expected to facilitate the bank or the analysis to determine the category of a credit card to customers bank right. The research used is by applying methods K-Nearest Neighbor to classify prospective customers in the making a credit card in accordance with the category of customers by using data customers at the Bank BNI Syariah Surabaya. A method K-Nearest Neighbor used to seek patterns on the data customers so established variable as factors supporters in the form of gender, the status of the house, the status, the number of dependants (children), a profession and revenue annually. The results of this research shows that an average of the value of precision of 92%, the value of recall of 83%, and the value of accuracy of 93%. Thus, this application is effective to help analyst credit cards in classifying customers to get credit cards that appropriate criteria.

References

[1] M. Antaristi and Y. I. Kurniawan, “Aplikasi Klasifikasi Penentuan Pengajuan Kartu Kredit Menggunakan Metode Naive Bayes di Bank BNI Syariah Surabaya,” J. Tek. Elektro, vol. 9, no. 2, pp. 45–52, 2017.
[2] Y. I. Kurniawan, “Perbandingan Algoritma Naive Bayes dan C.45 Dalam Klasifikasi Data Mining,” Jurnal Teknologi Informasi dan Ilmu Komputer , vol. 5, no. 4, pp. 455–463, 2018.
[3] A. Zainuddin, “Implementasi K-Nearest Neighbor Untuk Klasifikasi Penduduk Miskin Di Desa Ngemplak Kidul Kabupaten Pati Jawa Tengah,” J. Inform. SIMANTIK, vol. 4, no. 1, pp. 21–28, 2019.
[4] S. Sumarlin, “Implementasi Algoritma K-Nearest Neighbor Sebagai Pendukung Keputusan Klasifikasi Penerima Beasiswa PPA dan BBM,” J. Sist. Inf. Bisnis, vol. 5, no. 1, pp. 52–62, 2015.
[5] D. Yanosma, A. J. T, and K. Anggriani, “Implementasi Metode K-Nearest Neighbor (KNN) dan Simple Addittive Weighting (SAW) dalam Pengambilan Keputusan Seleksi Anggota PASKIBRAKA (Studi Kasus : Dinas Pemuda dan Olahraga Provinsi Bengkulu),” Rekursif J. Inform., vol. 4, no. 2, pp. 222–235, 2016.
[6] Y. Guo, S. Han, Y. Li, C. Zhang, and Y. Bai, “K-Nearest Neighbor combined with guided filter for hyperspectral image classification,” in International COnference On Identification, Information and Knowledge in the Internet of Things, 2018, pp. 159–165.
[7] X. Wu, J. Yang, and S. Wang, “Tea category identification based on optimal wavelet entropy and weighted k-Nearest Neighbors algorithm,” Multimed. Tools Appl., vol. 77, no. 3, pp. 3745–3759, 2018.
[8] T. M. Tran, X. M. T. Le, H. T. Nguyen, and V. N. Huynh, “A novel non-parametric method for time series classification based on k-Nearest Neighbors and Dynamic Time Warping Barycenter Averaging,” Eng. Appl. Artif. Intell., vol. 78, no. October 2017, pp. 173–185, 2019.
[9] K. Mittal, G. Aggarwal, and P. Mahajan, "Performance study of K-nearest neighbor classifier and K-means clustering for predicting the diagnostic accuracy." International Journal of Information Technology, vol. 11, no. 3, pp. 535-540, 2019.
[10] A. Tekin, M. Ulas, and F. Uzun, “Analysis of the Neonatal Sepsis Data Set with Data Mining Methods”. In 2019 1st International Informatics and Software Engineering Conference (UBMYK), 2019, pp. 1-4.
[11] R. Saxena, A. Johri, V. Deep, and P. Sharma, "Heart Diseases Prediction System Using CHC-TSS Evolutionary, KNN, and Decision Tree Classification Algorithm." In Emerging Technologies in Data Mining and Information Security, pp. 809-819. Springer, Singapore, 2019.
[12] M. KOKLU and K. Sabanci, “Estimation of Credit Card Customers Payment Status by Using kNN and MLP,” Int. J. Intell. Syst. Appl. Eng., vol. 4, no. Special Issue-1, pp. 249–251, 2016.
[13] Y.I. Kurniawan, and F. Angguntina. "Aplikasi Prediksi Kelayakan Calon Anggota Kredit Pada KSPPS BMT Arta Jiwa Mandiri Wonogiri Menggunakan Algoritma K-Nearest Neighbor." JISKA (Jurnal Informatika Sunan Kalijaga), vol. 3, no. 2, pp. 84-94, 2019.
[14] A. Subasi, and S. Cankurt. "Prediction of default payment of credit card clients using Data Mining Techniques." In 2019 International Engineering Conference (IEC), IEEE, 2019, pp. 115-120.
[15] Y. I. Kurniawan, E. Soviana, and I. Yuliana, “Merging Pearson Correlation and TAN-ELR algorithm in recommender system,” AIP Conf. Proc., vol. 1977, 2018.
[16] P. Asthana, “A comparison of machine learning techniques for customer churn prediction,” Int. J. Pure Appl. Math., vol. 119, no. 10, pp. 1–9, 2018.
[17] J. Ahmad, A. ul Hasan, T. Naqvi, and T. Mubeen, “A Review on Software Testing and Its Methodology,” Manag. J. Softw. Eng., vol. 3, no. 1, 2019.