Jurnal Gaussian
Vol 10, No 4 (2021): Jurnal Gaussian

PENERAPAN GRADIENT BOOSTING DENGAN HYPEROPT UNTUK MEMPREDIKSI KEBERHASILAN TELEMARKETING BANK

Silvia Elsa Suryana (Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro)
Budi Warsito (Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro)
Suparti Suparti (Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro)



Article Info

Publish Date
31 Dec 2021

Abstract

Telemarketing is another form of marketing which is conducted via telephone. Bank can use telemarketing to offer its products such as term deposit. One of the most important strategy to the success of telemarketing is opting the potential customer to create effective telemarketing. Predicting the success of telemarketing can use machine learning. Gradient boosting is machine learning method with advanced decision tree. Gardient boosting involves many classification trees which are continually upgraded from previous tree. The optimal classification result cannot be separated from the role of the optimal hyperparameter.  Hyperopt is Python library that can be used to tune hyperparameter effectively because it uses Bayesian optimization. Hyperopt uses hyperparameter prior distribution to find optimal hyperparameter. Data in this study including 20 independent variables and binary dependent variable which has ‘yes’ and ‘no’ classes. The study showed that gradient boosting reached classification accuracy up to 90,39%, precision 94,91%, and AUC 0,939. These values describe gradient boosting method is able to predict both classes ‘yes’ and ‘no’ relatively accurate.

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Journal Info

Abbrev

gaussian

Publisher

Subject

Other

Description

Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM ...