PARTICLE SWARM OPTIMIZATION UNTUK MENINGKATKAN AKURASI PREDIKSI PEMASARAN BANK

Authors

  • Ridwansyah Ridwansyah Teknik Informatika STMIK Nusa Mandiri Jakarta
  • Esty Purwaningsih Manajemen Informatika AMIK BSI Tangerang

DOI:

https://doi.org/10.33480/pilar.v14i1.94

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.

Downloads

Download data is not yet available.

References

Agrawal, R., Ghosh, S., Imielinski, T., Iyer, B., & Swami, A. (1992). An Interval Classi er for Database Mining Applications. Proceedings of the 18th VLDB Conference Vancouver.

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.

Downloads

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

Issue

Vol. 14 No. 1 (2018): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Maret 2018

Section

Articles

License

An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:

  1. Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
  2. Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
  3. Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
    Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.