ANALISA NASABAH POTENSIAL TABUNGAN DEPOSITO BERJANGKA MENGGUNAKAN TEKNIK KLASIFIKASI DATA MINING

Abstract

Time deposits are a product of a financial institution, which is currently increasing. The main target of this time deposit marketing is the old customers of the Bank. To increase the effectiveness of marketing customers are grouped into potential and non-potential customers. This means that potential customers have a greater chance to open a time deposit account. Customer data is taken from the UCI repository, originating from Banks in Portugal. Data is processed with rapidminer software using the Decision Tree method with Particle Swarm Optimization, Naïve Bayes with Particle Swarm Optimization and finally processed using Neural Network with Particle Swarm Optimization. Data processing results were compared and showed that the Naïve Bayes Algorithm with Particle Swarm Optimization had the highest accuracy of 97.04%. Therefore an application designed based on Naive Bayes with Particle Swarm Optimization. From the original attribute consisting of 20, only 9 attributes can be used so that the level of accuracy is high. Attributes used have values ​​more than 0.500, while those that have these values ​​are omitted. The design was created using the Unified Modeling Language (UML) and Visual Basic 6.0 to create an User Interface.