INDRA
@unsulbar.ac.id
Fakultas Teknik
Universitas Sulawesi Barat
EDUCATION
S1 Sistem Informasi
S2 Magister Manajemen
RESEARCH INTERESTS
Sistem Informasi
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ahmad Ilham, Laelatul Khikmah, Indra, Ulumuddin, and Ida Bagus Ary Indra Iswara
IOP Publishing
The long-term deposits product is often offered to prospective customers by the bank through telemarketing strategy. How to maximize customer value through telemarketing strategy is a major focus in this research. Therefore, required a model that can classify potential customers with the potential to increase corporate earnings. The Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbour (K-NN), Support Vector Machine (SVM), Neural Network (NN), and Logistic Regression (LR) model have been proposed to compare. The comparison of these algorithms evaluated using the Protestal Bank dataset of the UCI Machine Learning repository, for performance algorithms using Area Under Curve (AUC) and Accuracy. From the experiment, the results show that SVM yield promising results with the Accuracy and AUC 97.07% and 0.925 respectively. It can be concluded that SVM is the best choice for classifying prospective customers who have the potential to be interested in time deposit products that are offered by telephone or cellular as distinguished from other classification algorithms.