Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Potensi Hilangnya Nasabah Bank
DOI:
https://doi.org/10.33633/tc.v22i1.7302Abstract
Nasabah adalah salah satu aset paling berharga dari sebuah bisnis perbankan. Mereka adalah ujung tombak pengguna produk yang nantinya memberikan keuntungan bagi bank, terutama pada produk kartu kredit. Penelitian ini bertujuan untuk mengetahui nasabah mana sajakah yang berpotensi untuk meninggalkan layanan kartu kredit dari sebuah bank. Pada penelitian sebelumnya belum ada yang melakukan analisis perbandingan algoritma machine learning dengan berbagai macam tahapan preprocessing untuk memprediksi potensi hilangnya nasabah bank. Penelitian ini melakukan analisis perbandingan algoritma machine learning dengan kombinasi tahapan preprocessing untuk memprediksi potensi hilangnya nasabah bank. Analisis ini penting untuk pemilihan algoritma yang paling cocok untuk prediksi potensi hilangnya nasabah bank. Pada tahapan preprocessing diterapkan dimensionality reduction dan feature selection menggunakan metode Variance threshold dan Correlation coefficient. Metode klasifikasi yang digunakan adalah Logistic regression (LR), Decision tree (DT), dan Naïve Bayes (NB). Hasil tertinggi dari ketiga metode tersebut adalah Decision tree yang mampu memiliki nilai F1 Score sebesar 96% dan nilai akurasi mencapai 93%. Logistic regression dan Naïve Bayes berada pada urutan kedua dan ketiga setelah decision tree. Tahapan data preprocessing tidak memberikan pengaruh yang signifikan pada nilai F1 Score dan akurasi.References
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