Abstrak
The research was conducted because the customers selected in the credit card application were still not quite right and the customer selection was not optimal so that the selected customers did not match the criteria that had been set. Based on the problem, namely the inaccuracy and ineffectiveness of the prediction of the eligibility of credit card applications for customers. For this reason, it is necessary to predict the eligibility of credit card applications for customers using the C4.5 algorithm, namely by analyzing customer data, and performing grouping calculations to determine customers who have the potential to be smooth and have the potential to be stuck. In it, variables are applied based on House Status, Income, Marital Status, and Age. This study aims to determine customers in credit card applications accurately, an accuracy test has been carried out using a confusion matrix with an accuracy result of 83.33%
Referensi
Arikunto., 2009. Prosedur Penelitian Suatu Tindakan Praktik. Jakarta: Rineka Cipta
Ahmadi, C. & Hermawan, D., 2013. E-business dan E-commerce. s.l.:Andi.
C., Saragih, R. R. & Tambunan, B. C., 2021. DATA MINING ALGORITHM C4.5 CLASSIFICATION DETERMINATION CREDIT ELIGIBILITY FOR JAYA BERSAMA COOPERATIVES (KORJABE). JURTEKSI (Jurnal Teknologi dan Sistem Informasi), pp. 59-68.
Dermawan, R., 2013. Pengambilan Keputusan : Landasan Filosofis, Konsep Dan Aplikasi. Bandung: Alfabeta
Desyanita, L. & Wibowo, A., 2020. Pemodelan Sistem Prediksi Kelayakan Pengajuan Kredit Kepemilikan Rumah Dengan Metode Algoritma c4.5 dan Naive Bayes. JURNAL ILMIAH ELEKTRONIKA DAN KOMPUTER, pp. 10-22.
Gorunescu, F., 2011. Data Mining Concept, Models and Techniques. s.l.:Springer
Hidayat, W. & Marlina, A. U., 2022. Penerapan Metode Algoritma C4.5 Untuk Menentukan Kelayakan Calon Nasabah Pemegang Kartu Kredit Bank Mega Card center kuningan. Teknois, pp. 31-48
Junaedi, E., Siregar, A. M. & Nurlelasari, E., 2022. Implementasi C4.5 Dan Algoritma K Nearest Neighbor Untuk Prediksi Kelayakan Pemberian Kredit Menggunakan RapidMiner Studio. Scientific Student Journal for Information, Technology and Science, pp. 83-90.
Kamber, J. & Han, M., 2006. Data Mining: Concepts and Techniques, Second Edition. s.l.:Morgan Kaufmann
Mardhiyah, P. A., Siregar, R. R. A. & Palupiningsih, P., 2020. Klasifikasi Untuk Memprediksi Pembayaran Kartu Kredit Macet Menggunakan Algoritma C4.5. Jurnal Teknologia, pp. 91-101.
Maulana, I. & Subhan, M., 2021. Prediction Model of Eligibility of Lending in Credit Banks Using The C4.5 Algorithm and Naive Bayes Method. Jurnal Mantik, pp. 1791-1798.
Moertini, V. S., 2007. Pengembangan Skalabilitas Algoritma Klasifikasi C4. 5 Dengan Pendekatan Konsep Operator Relasi, studi kasus: pra-pengolahan dan klasifikasi citra batik. Bandung: Program Studi Teknik Informatika–ITB
Nawary, A. P. & K., 2021. PENERAPAN DATA MINING DALAM MEMPREDIKSI KELANCARAN KREDIT NASABAH MENGGUNAKAN ALGORITMA C4.5 (STUDI KASUS PADA PT.ASTRA INTERNATIONAL (AUTO 2000 PLAJU). Bina Darma Conference on Computer Science, pp. 1041-1047
Nadiyah. & Hardiyan., 2022. Penerapan Algoritma C4.5 Dalam Pemberian Kelayakan Kredit Motor. Jurnal Rekayasa Perangkat Lunak, pp. 26-31
Nofriansyah, D., 2015. Algoritma Data Mining Dan Pengujian. s.l.:Deepublish.
Sugiyono., 2009. Metode Penelitian Kuantitatif dan Kualitatif dan R&D. s.l.:Alfabeta
Setiawan, R., 2020. Analisis Kelayakan Pemberian Kredit Nasabah Koperasi Menggunakan Algoritma c4.5. Jurnal Ilmu Komputer dan Teknologi Informasi, pp. 74-78.
Daniel T Larose, 2006. Data Mining Methods and Models 1st Edition. s.l.:John Willey & Sons, Inc..
Yusuf, D., Bahri, S. & Larassati, A., 2021. DECISION TREE MENGGUNAKAN ALGORITMA C4.5 UNTUK ANALISA KELAYAKAN PEMBERIAN KREDIT. Jutech, pp. 97-106.