Teknik Weighting untuk Mengatasi Ketidakseimbangan Kelas Pada Prediksi Churn Menggunakan XGBoost, LightGBM, dan CatBoost

Wahyu Nugraha; Muhamad Syarif

doi:10.33633/tc.v22i1.7191

Authors

Wahyu Nugraha Universitas Bina Sarana Informatika
Muhamad Syarif Bina Sarana Informatika

DOI:

https://doi.org/10.33633/tc.v22i1.7191

Keywords:

Prediksi Churn, Imbalance Class, Klasifikasi Boosting

Abstract

Churn merupakan kondisi dimana seseorang berpindah dari satu layanan ke layanan yang lain. Churn pelanggan menjadi masalah yang meningkat cukup signifikan dan menjadi tantangan utama yang harus dihadapi banyak perusahaan perbankan karena memiki peran penting terhadap laba perusahaan. Oleh sebab itu, diperlukan cara untuk memprediksi perilaku churn tepat waktu agar bisa menerapkan retensi pelanggan. Namun, Permasalahan yang dihadapi oleh model prediksi churn adalah ketidakseimbangan kelas sehingga membuat model klasifikasi menghasilkan kinerja yang buruk. Solusi yang paling sering digunakan untuk mengatasi masalah ketidakseimbangan kelas terbagi menjadi tiga pendekatan yaitu pendekatan level data, level algoritma dan ensemble. Setiap pendekatan mengalami beberapa masalah yang sulit diprediksi ketika digunakan untuk menangani masalah ketidakseimbangan kelas. Pada penelitian ini, peneliti melakukan eksperimen menggunakan metode ensemble berbasis boosting untuk melakukan prediksi churn pelanggan dan mencoba meningkatkan kinerjanya pada dataset yang tidak seimbang dengan parameter tuning menggunakan scale pos weight. Algoritma klasifikasi yang digunakan yaitu XGBoost (extreme gradient boosting), LightGBM (light gradient boosting machine) dan CatBoost. Hasil eksperimen akan membandingkan kinerja dari ketiga algoritma berbasis boosting tersebut dengan menyesuaikan bobot parameternya sebanyak tiga kali. Dari hasil pengujian, model CatBoost memperoleh nilai recall tertinggi sebesar 0.79. Sedangkan untuk nilai recall terendah adalah model CatBoost default dengan nilai 0.47. Bedasarkan hasil ekperimen dapat disimpulan bahwa model bekerja dengan cukup baik pada data yang tidak seimbang dengan memberikan mekanisme hyperparameter scale pos weightsehingga model dapat lebih fokus pada kelas minoritas yang sulit dideteksi.

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Teknik Weighting untuk Mengatasi Ketidakseimbangan Kelas Pada Prediksi Churn Menggunakan XGBoost, LightGBM, dan CatBoost

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