PREDIKSI TINGKAT KESUKSESAN PROMOSI BANK DENGAN ALGORITMA DNN

Oscar Oscar (Sekolah Pascasarjana Ilmu Komputer, STMIK Nusa Mandiri, Jakarta, Indonesia)
Nurlaelatul Maulidah (Sekolah Pascasarjana Ilmu Komputer, STMIK Nusa Mandiri, Jakarta, Indonesia)
Annida Purnamawati (Sekolah Pascasarjana Ilmu Komputer, STMIK Nusa Mandiri, Jakarta, Indonesia)
Destiana Putri (Sekolah Pascasarjana Ilmu Komputer, STMIK Nusa Mandiri, Jakarta, Indonesia)
Hilman F Pardede (Pusat Penelitian Informatika, Lembaga Ilmu Pengetahuan Indonesia, Indonesia)

Jurnal Informatika
Vol 21, No 1 (2021): Jurnal Informatika

Telemarketing is one effective way for promoting products. However, it is often difficult to measure the success of telemarketing. Therefore, a way to predict the success rate of telemarketing, and hence strategies could be planned to increase the success rate. In this study, we evaluate several implementations of machine learning for prediction the success of telemarketing. The evaluated methods are Deep Neural Network (DNN), Random Forest, and K-nearest neighbor (K-NN). We validate our experiments using 10-fold cross validation and our experiments show that DNN with 3 hidden layers outperforms other methods. Accuracy of 90% is achieved with the DNN. It is better than Random Forest and KNN that achieve accuracies of algorithm and 88% and 89%.Keywords— Bank Marketing, DNN, KNN, Random Forest.