Penerapan Resampling dan Adaboost untuk Penanganan Masalah Ketidakseimbangan Kelas Berbasis NaÏŠve Bayes pada Prediksi Churn Pelanggan
DOI:
https://doi.org/10.32493/informatika.v2i4.1440Keywords:
AdaBoot, Churn Pelanggan, Naïve Bayes, Prediksi, ResamplingAbstract
Banyaknya operator seluler mendorong persaingan usaha yang sangat ketat. Kemudahan pelanggan untuk berpindah ke pesaing merupakan perhatian utama bagi bagian CRM (Customer Relationship Management), karena untuk mendapatkan pelanggan baru membutuhkan biaya yang jauh lebih mahal daripada mempertahankan pelanggan yang sudah ada. Untuk mengambil tindakan yang tepat dalam mempertahankan pelanggan harus mengetahui kecenderungan pelanggan apakah akan mengalami churn atau tidak. Prediksi kecenderungan pelanggan dilakukan dengan menggunakan model data mining. Pada penelitian ini akan diterapkan teknik resampling dan teknik ensemble AdaBoost untuk memperbaiki kinerja pengklasifikasi sedangkan untuk mengukur kinerja model digunakan software RapidMiner. Hasil penelitian menunjukkan bahwa model integrasi random oversampling, AdaBoost, dan Naïve Bayes memiliki kinerja yang lebih baik karena memiliki nilai AUC (Area Under the ROC (Receiver Operating Characteristic) Curve) yang lebih baik.References
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