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Abstract The vast amount of data in the banking industry is very difficult to analyze manually to obtain useful information in order to determine a policy. Therefore, the use of data mining is expected to contribute in processing the data. Various methods have been widely used to classify data, one of which is the support vector machine method. This study aims to classify customers who have the potential to subscribe to deposits in the bank marketing dataset. The focus of this study proposes the development of the support vector machine method, which is the least square support vector machine method, then ensemble using boosting. The data to be processed is the bank marketing dataset. The results show that the proposed method that is ensemble least square support vector machine is better than other methods with a percentage of accuracy level of 95.15%, sensitivity 92.93%, specialization of 97.61%. The average total classification results is 95.23%.The vast amount of data in the banking industry is very difficult to analyze manually to obtain useful information in order to determine a policy. Therefore, the use of data mining is expected to contribute in processing the data. Various methods have been widely used to classify data, one of which is the support vector machine method. This study aims to classify customers who have the potential to subscribe to deposits in the bank marketing dataset. The focus of this study proposes the development of the support vector machine method, which is the least square support vector machine method, then ensemble using boosting. The data to be processed is the bank marketing dataset. The results show that the proposed method that is ensemble least square support vector machine is better than other methods with a percentage of accuracy level of 95.15%, sensitivity 92.93%, specialization of 97.61%. The average total classification results is 95.23%. Keyword: SVM, LS-SVM, Ensemble, AdaBoost, Bank Marketing. Journal of System and Computer Engineering (JSCE) ISSN (online): 2723-1240 Vol. 1 No. 1 Juli 2020

Klasifikasi Pelanggan Deposito Potensial menggunakan Ensembel Least Square Support Vector Machine