Belief Value Development In Dempster-Shafer With Particle Swarm Optimization(PSO) For Determining Of The Provision On Cases Of Persecution

Authors

  • Merry Gricelya Nababan Universitas Brawijaya
  • Yusuf Priyo Anggodo Universitas Brawijaya
  • Rekyan Regasari Mardi Putri Universitas Brawijaya

DOI:

https://doi.org/10.25126/jitecs.20172231

Abstract

The problem of uncertainty often becomes an obstacle, especially in terms of diagnosing the provision in this study. There is an algorithm that can solve this problem is Dempster-shafer (D-S), this algorithm has belief value that serves to determine the influence between symptoms. Belief value is obtained from experts, when there are new symptoms must have to ask the experts to know the value of belief, and takes time as well. So the Particle Swarm Optimization (PSO) algorithm will help generate and optimize the value of belief in D-S. PSO is able to produce optimal belief value. This research is to generate value belief D-S with PSO.

References

Susanto, Riki. 2010. Hukum Pidana (Criminal Law). Depok : Universitas Indonesia.

Chazawi, Adami. 2000. Kejahatan Terhadap Tubuh Dan Nyawa . Malang: Rajawali Pers.

Lingtogareng, Jerol. 2013. Analisa Keyakinan Hakim Dalam Pengambilan Keputusan Perkara Pidana di Pengadilan. Lex Crimen. vol. 2, no. 3.

Sidharta, Arief. 2015. Etika Dan Kode Etik Profesi Hukum.

Ahmadzadeh, M.R. dan Petrou, Maria. 2001. Knowledge Fusion Based on D-S Theory. Its Application on Expert System for Software Fault Diagnosis. IEEE.

Yan, Ran., Li, Guoqi., Liu, Bin. 2015. Knowledge Fusion Based on D-S Theory and Its Application on Expert System for Software Fault Diagnosis. Prognostics and System Health Management Conference-Beijing.

Li, Feijiang, Qian, Yuhua, Wang, Jieting, Liang, Jiye. 2016. Multigranulation Information Fusion: A Dempster-Shafer Eidence Theory-Based Clustering Ensemle Method. Information Science. vol. 378, pp. 389-409.

Kennedy dan Eberhart, 1995. A New Optimizer Using Particle Swarm Theory. Sixth IEEE International Symposium on Micro Machine and Human Science.

Anggodo, Y. P. dan Mahmudy, W. F. 2017. Automatic Clustering and Optimized Fuzzt Logical Relationships For Minimum Living Needs Forecasting. Journal of Environmental Engineering & Sustainable Technology (JEEST), vol. 4, no. 01, pp. 1-7.

Marini dan Walzcak, 2015. Particle Swarm Optimization(PSO). A tutorial. Chemometrics and Intelligent Laboratory Systems. vol. 149, pp. 153–165.

Dempster, A. P. 1968 . A generalization of Bayesian inference. Journal of the Royal Statistical Society, vol. 30 pp.205-247.

Wu et all. 2002 . Sensor Fusion Using Dempster-Shafer Theory. EEE Instrumentation and Measurement Technology Conference.

Kennedy dan Eberhart, 1995. A New Optimizer Using Particle Swarm Theory. Sixth IEEE International Symposium on Micro Machine and Human Science

.

Marini dan Walzcak, 2015. Particle Swarm Optimization(PSO)Atutorial. Chemometrics and Intelligent Laboratory Systems. vol. 149, pp. 153–165.

Nouaouria, N., Boukadoum, M. Dan Proulx, R. 2013. Particle Swarm Clasification:Asurvey and Positioning. Pattern Recognition. Vol. 46. pp.2028-2044.

Chen H. L., Yang, Bo., Wang, Gang., Liu, Jie., Xu, Xin., Wang S. J., Liu D. Y. 2011. A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest. Knowledge-Based System,vol. 24, pp.1349-1359.

Beynon M. J., Bruce, Curry., Morgan P. H. 2000. The Dempster-Shafer Theory Of Evidence: An Alternative Approach To Multicriteria Decision Modelling. Pp.37-50.

Downloads

Published

2017-11-05

How to Cite

Nababan, M. G., Anggodo, Y. P., & Putri, R. R. M. (2017). Belief Value Development In Dempster-Shafer With Particle Swarm Optimization(PSO) For Determining Of The Provision On Cases Of Persecution. Journal of Information Technology and Computer Science, 2(2). https://doi.org/10.25126/jitecs.20172231

Issue

Section

Articles