Meta-Algorithms untuk Meningkatkan Kinerja Klasifikasi dalam Keberhasilan Telemarketing Perbankan
DOI:
https://doi.org/10.33633/tc.v19i4.3725Keywords:
telemarketing, meta-algoritms, decission tree, baggingAbstract
Banyak bank dan perusahaan asuransi berinteraksi dengan pelanggan mereka menggunakan jasa telemarketing agar dapat lebih meningkatkan pendapatan bank dan mencapai target. Kompetisi yang ketat di industri perbankan membuat tenaga penjualan bank mengejar target terutama menjelang akhir tahun. Pelaku telemarketing atau biasa disebut telemarketer pada perbankan bertugas menghubungi nasabah potensial yang dilakukan secara berkala melalui sambungan telepon. Namun tidak semua nasabah mendapatkan respons yang positif, sering kali telemarketing menemui antipati dari nasabah sebelum proses komunikasi dilakukan. Pendekatan yang diusulkan untuk menemukan nasabah potensial adalah dengan cara klasifikasi data nasabah menggunakan pembelajaran mesin (machine learning). Penggunaan algoritme pembelajaran yang dilakukan secara tunggal memungkinkan untuk dikembangkan menggunakan meta-algorithms sehingga akan memaksimalkan kinerja klasifikasi dalam memprediksi target pelanggan atau nasabah yang sesuai dengan kebutuhan perusahaan agar tujuan pemasaran tercapai. Berdasarkan hasil penelitian yang diperoleh, tindakan pra-pemrosesan dan penggunaan meta-algorithms seperti bagging memberikan kontribusi yang baik untuk proses pembelajaran model decission tree pada data telemarketing dengan hasil akurasi terbaik 98,7%.References
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