Bank Customer Segmentation Model Using Machine Learning - Bunga Tiara, Vira; Siregar, Amril Mutoi; Kusumaningrum, Dwi Sulistya Kusumaningrum; Rohana, Tatang (Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha, 2024)
Banks generally carry out marketing strategies by offering deposit products directly to customers. However, this method is less effective because it requires individualized communication without considering the customer's interest in the product offered. Therefore, this research aims to categorize the classification of bank customers into Yes and No. This research uses a dataset of bank deposits taken from KTM. This research uses a bank deposit dataset taken from Kaggle, the data consists of 11162 rows with 17 attributes. PCA technique was used for feature selection which was optimized by reducing the dimensionality of the dataset before modeling. It was found that the best model accuracy was SVM RBF kernel with C parameters achieving 80.51% accuracy and ANN 80.78%, but ANN showed a higher ROC graph than SVM because ANN performance results were faster than SVM. Thus, the overall performance measurement of ANN is much better.