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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Creation is distributed below Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
PARTICLE SWARM OPTIMIZATION UNTUK MENINGKATKAN AKURASI PREDIKSI PEMASARAN BANK
Keywords:
Bank Marketing, Data Mining, Neural Network, Particle Swarm Optimization
Abstract
Pemasaran bank dapat melalui promosi sebuah produk dengan menghasilkan data dan informasi yang cukup kompleks dan luas sehingga akan merasa kesulitan dalam menganalisisnya. Neural network dapat menyelesaikan masalah khususnya sampel data besar yang di dapat di uci data set namun masih memiliki kekurangan pada pemilihan fitur yang sesuai dan optimal pada bobot atribut yang digunakan sehingga menyebabkan tingkat akurasi prediksi menjadi kurang tinggi, Algoritma PSO dapat meningkatkan bobot atribut dan meningkatkan akurasi suatu algoritma dan klasifikasi data yang lebih besar. Hasil penelitian menggunakan Neural network dan Algoritma PSO menunjukkan perbaikan yang lebih baik dan mengesankan dalam kinerja model neural network yang memiliki kelemahan dalam pemilihan fitur.
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References
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Satapathy, S. C., Chittineni, S., Mohan Krishna, S., Murthy, J. V. R., & Prasad Reddy, P. V. G. D. (2012). Kalman particle swarm optimized polynomials for data classification. Applied Mathematical Modelling, 36(1), 115–126. Elsevier Inc.
Vieira, S. M., Mendonça, L. F., Farinha, G. J., & Sousa, J. M. C. (2013). Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients. Applied Soft Computing, 13(8), 3494–3504.
Yusup, N., Zain, A. M., & Hashim, S. Z. M. (2012). Overview of PSO for optimizing process parameters of machining. Procedia Engineering, 29, 914–923.
Cao, J., Cui, H., Shi, H., & Jiao, L. (2016). Big data: A parallel particle swarm optimization-back-propagation neural network algorithm based on MapReduce. PLoS ONE, 11(6), 1–17.
Catal, C., & Diri, B. (2009). No Title. Information Sciences, 179(8), 1040–1058.
Gorunescu, F. (2011). Data Mining: Concepts, Models and Techniques. Data mining - Concepts, Models and Technique.
Ling, C., & Li, C. (1998). Data Mining for Direct Marketing: Problems and Solutions. Proceedings of KDD 98’, 73–79.
Moro, S., Cortez, P., & Rita, P. (2014). A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62, 22–31. Elsevier B.V.
Moro, S., & Laureano, R. M. S. (2011). Using Data Mining for Bank Direct Marketing: An application of the CRISP-DM methodology. European Simulation and Modelling Conference, (Figure 1), 117–121.
Ridwansyah, R., & Purwaningsih, E. (2018). Laporan Akhir Penelitian PDY. Jakarta.
Saputra, E. P. (2017). Prediksi Keberhasilan Telemarketing Bank Untuk. Jurnal Ilmu Pengetahuan dan Teknologi Komputer, 2(2), 66–72.
Satapathy, S. C., Chittineni, S., Mohan Krishna, S., Murthy, J. V. R., & Prasad Reddy, P. V. G. D. (2012). Kalman particle swarm optimized polynomials for data classification. Applied Mathematical Modelling, 36(1), 115–126. Elsevier Inc.
Vieira, S. M., Mendonça, L. F., Farinha, G. J., & Sousa, J. M. C. (2013). Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients. Applied Soft Computing, 13(8), 3494–3504.
Yusup, N., Zain, A. M., & Hashim, S. Z. M. (2012). Overview of PSO for optimizing process parameters of machining. Procedia Engineering, 29, 914–923.
Published
2018-03-15
How to Cite
Ridwansyah, R., & Purwaningsih, E. (2018). PARTICLE SWARM OPTIMIZATION UNTUK MENINGKATKAN AKURASI PREDIKSI PEMASARAN BANK. Jurnal Pilar Nusa Mandiri, 14(1), 83-88. https://doi.org/10.33480/pilar.v14i1.94
Section
Articles
Copyright (c) 2018 Ridwansyah & Esty Purwaningsih
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