PREDIKSI KEBERHASILAN TELEMARKETING BANK UNTUK MENCARI ALGORITMA DENGAN PERFORMA TERBAIK
To find algorithms that have the best performance in predicting the success of telemarketing in banking courses researchers have conducted various material tests of several algorithms for data from the uci data set, and have as many as 17 attributes, some algorithms that have previously been tested in this study. to find the best performing algorithm using algorithm authors, among others, is to use an algorithm based on particle swarm optimization to optimize some attribute values and to improve the accuracy of algorithms and higher data classification, and can produce even higher accuracy values. From the neural algorithm network (NN) based on PSO, the results are 91.80%, Support Vector Machine (SVM) to get an accuracy of 90.20%. Naif Bayes (NB) with an accuracy of 89.41%, and to use the Decision Tree (DT) algorithm with an accuracy of 90.93%. Then the PSO Neural Network based algorithm is clear resulting in higher accuracy than some algorithms tested with an accuracy of 91.80%. These results are classified as very good (very good classification).
Hagan, M. T. (2014). Neural Network Design. Oklahoma: Stillwater.
Cao, J., Cui, H., Shi, H., & Jiao, L. (2016). A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce. PLOS: journal.pone.0157551.
Dewan, M., Farida, & Zhanga, L. (2014). Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. ELSEVIER.
Jadoun, V. K., Gupta, N., & Niazi, K. R. (2015). Improved Particle Swarm Optimization for Multi-area Economi Reserve Sharing Scheme. ELSEVIER.
Korada, N. K., & Kumar, S. P. (2012). Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Maize Expert System. International Journal of Information Sciences and Techniques (IJIST).
Kotler, P., & Keller, K. L. (2009). Manajemen Pemasaran (Jilid 1) (Edisi 13). Jakarta: Erlangga.
Myatt, J. G. (2007). Making Sense of Data A Practical Guide to Exploratory Data. New Jersey: A John Wiley & Sons, inc., publication.
Santoso, B. (2007). Data Mining : Teknik Pemanfaatan Data Untuk Keperluan Bismis. Yogyakarta: Graha Ilmu.
Sergio, M., Cortez, P., & Rita, P. (2014). Adata driven approach to predict the success of bank telemarketing. Lisboa: Research Centre, Univ. of Minho.
Wang, H., & Raj, B. (2015). Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas. Pittsburgh: Carnegie Mellon University.
Waringin, T. D. (2008). Marketing Revolution. Jakarta: Gramedia Pustaka Utama.
Zaki, M. J., & Jr, W. M. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms 1st Edition. Cambridge: Cambridge University.
Abstract viewed = 224 times
PDF downloaded = 177 times