Perbandingan Metode Algoritma C4.5, Naïve Bayes, dan Logistic Regression untuk Penentuan Kelayakan Penerima Kredit
DOI:
https://doi.org/10.26594/teknologi.v14i2.4650Abstract
Kredit adalah layanan penting yang disediakan oleh sektor perbankan selain tabungan, transfer uang, dan investasi. Masalah kredit macet secara global telah mendorong penerapan pembelajaran mesin dan analitik canggih untuk manajemen risiko kredit. Tujuan dari penelitian ini adalah untuk meningkatkan efisiensi dan akurasi proses klasifikasi kelayakan pemohon kredit dengan membandingkan algoritma yang dipilih dan juga ingin mengetahui apakah algoritma yang dipilih dapat meningkatkan akurasi dan efektivitas proses penentuan kelayakan pemohon pinjaman. Algoritma klasifikasi data mining yang akan digunakan dalam perbandingan pada penelitian ini adalah algoritma C4.5, Naive Bayes, dan Logistic Regression. Algoritma C4.5, Naive Bayes dan Logistic Regression digunakan untuk melihat nilai akurasi, presisi dan recall yang dihasilkan antara ketiga algoritma tersebut. Nilai-nilai ini diamati dengan menggunakan model validasi silang. Hasil penelitian menunjukkan bahwa algoritma Regresi Logistik memiliki nilai akurasi tertinggi dengan nilai akurasi sebesar 81,1%. Algoritma Regresi Logistik cukup akurat dalam memprediksi data karena nilai AUC termasuk dalam predikat Fair Classification dengan nilai 0.76. Oleh karena itu, Algoritma Regresi Logistik dapat digunakan sebagai acuan dalam pengambilan keputusan untuk menentukan kelayakan penerima kredit.
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