Klasifikasi Penentuan Pengajuan Kartu Kredit Nasabah Pada Bank Mandiri Dengan Metode Naive Bayes
Abstract
The selection process for credit card applications is a challenge for banks because it still relies heavily on manual evaluation which is prone to subjectivity and inefficiency. This research aims to develop a classification system for credit card applications using the Naive Bayes method to increase accuracy and efficiency in the customer selection process. This method classifies customers based on main parameters such as income, credit history, number of dependents, employment status, and home ownership status. The developed model was tested using a dataset consisting of 166 training data and 20 test data, resulting in 85% accuracy, 86.67% recall and 93% precision. These results indicate that the Naive Bayes method can be an effective solution in helping banks automate the credit card application evaluation process with a high level of accuracy.
Keywords: Credit Card; Naive Bayes; Classification
Abstrak
Proses seleksi pengajuan kartu kredit merupakan tantangan bagi perbankan karena masih banyak bergantung pada evaluasi manual yang rentan terhadap subjektivitas dan ketidakefisienan. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi pengajuan kartu kredit dengan metode Naive Bayes guna meningkatkan akurasi dan efisiensi dalam proses seleksi nasabah. Metode ini mengklasifikasikan nasabah berdasarkan parameter utama seperti pendapatan, riwayat kredit, jumlah tanggungan, status pekerjaan, dan status kepemilikan rumah. Model yang dikembangkan diuji menggunakan dataset yang terdiri dari 166 data latih dan 20 data uji, menghasilkan akurasi 85%, recall 86,67%, dan precision 93%. Hasil ini menunjukkan bahwa metode Naive Bayes dapat menjadi solusi yang efektif dalam membantu bank mengotomatisasi proses evaluasi pengajuan kartu kredit dengan tingkat akurasi yang tinggi.
Kata kunci: Kartu Kredit; Naive bayes; Klasifikasi
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