PENERAPAN NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK SELEKSI ATRIBUT PENENTUAN MAHASISWA DROP OUT

Authors

  • Laila Septiana Manajemen Informatika AMIK BSI Jakarta

DOI:

https://doi.org/10.33480/pilar.v9i2.133

Keywords:

Neural Network, Particle Swarm Optimization based model, The drop-out student

Abstract

The drop-out student is a student whose student status is revoked, in case of determined orders by the university. The height of drop-out students number will have an impact on the scoring of accreditation in a university. therefore, it is necessary to know what factors the students have the drop-out status are. Most of the algorithm comparison has been done in previous research. Otherwise, this research uses Neural Network with Particle Swarm Optimization based model to level up optimation value in selection attribute influencing prediction the students drop out. 

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References

Astuti, E. D. (2009). Pengantar Jaringan Saraf Tiruan. Wonosobo: Star Publishing. Berndtson, M., Hansson, J., Olsson, B., &

Lundell, B. (2008).A Guide for Students in Computer Science and Information Systems.London: Springer.

Dawson, C. W. (2009). Projects in Computing and Information System A Student's Guide.England: Addison Wesley.

Gorunescu, F. (2011).Data Mining Concept Model Technique.India: Springer. Gray, D. E. (2004).Doing Research in the Real World.New Delhi: SAGE.

Gerben W. Dekker, "Predicting Students Drop Out: A Case Study," In International Conference on Educational Data Mining, Cordoba, Spain, 41-50, 2009

Han, J., & Kamber, M. (2007).Data Mining Concepts and Technique.Morgan Kaufmann publisher.

Hastutik, khafiizh.(2012). Analisis Komparasi Algoritma Klasifikasi Data Mining Untuk Prediksi Mahasiswa Non Aktif.Semantik 2012. ISBN 979 - 26 - 0255 - 0

K, G. S., & Deepa, D. S. (2011).Analysis of Computing Algorithm using Momentum in Neural Networks.Journal of Computing, volume 3, issue 6, 163-166.

Kusrini, & Luthfi, E. T. (2009).Algoritma Data mining.Yogyakarta: Andi.

Larose, D. T. (2005).Discovering Knowledge in Data.Canada: Wiley Interscience.

Ling, S. H., Nguyen, H. T., & Chan, K. Y. (2009).A New Particle Swarm Optimization Algorithm for Neural Network Optimization.Network and System Security, third International Conference , 516-521.

Myatt, G. J. (2007). Making Sense of Data A Practical Guide to Exploratory Data Analysis and Data Mining. New Jersey: A John Wiley & Sons, inc., publication.

Nagadevara,& Vishnuprasad. (2005). Building Predictive models for the election result in India an application of classification trees and neural network. Journal of Academy of Business and Economics Volume 5 .

Park, T. S., Lee, J. H., & Choi, B. (2009).Optimization for Artificial Neural Network with Adaptive inertial weight of particle swarm optimization.CognitiveInformatics, IEEE International Conference , 481485.

Purnomo, M. H., & Kurniawan, A. (2006).Supervised Neural Network.Suarabaya: Garaha Ilmu.

Salappa, A., Doumpos, M., & Zopounidis, C. (2007). Feature Selection Algorithms in Classification Problems: An Experimental Evaluation. Systems

Shukla, A., Tiwari, R., & Kala, R. (2010).Real Life Application of Soft Computing.CRC Press.

Sug, H. (2009). An Empirical Determination of Samples for Decision Trees.AIKED'09 Proceeding of the 8th WSEAS international conference on Artificial intelligence, Knowledge enggineering and data bases , 413416.

Sotiris Kotsiantis, "Educational Data Mining: A Case Study for Predicting DropoutProne Students," Int. J. of KnowledgeEngineering and Soft Data Paradigms, vol. X, 2010

Vercellis, C. (2009). Business Intelligence: Data Mining and Optimization for Decision Making. John Wiley & Sons, Ltd.

Xiao, & Shao, Q. (2011).Based on two Swarm Optimized algorithm of a neural network to prediction the switch's traffic of coal.ISCCS '11 Proceeding of the 2011 International Symposium on Computer Science and Society , 299302

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Published

2013-09-15

How to Cite

Septiana, L. (2013). PENERAPAN NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK SELEKSI ATRIBUT PENENTUAN MAHASISWA DROP OUT. Jurnal Pilar Nusa Mandiri, 9(2), 104–112. https://doi.org/10.33480/pilar.v9i2.133

Issue

Vol. 9 No. 2 (2013): Pilar Nusa Mandiri : Journal of Computing and Information System Periode September 2013

Section

Articles

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