Comparative Analysis of Cow Disease Diagnosis Expert System using Bayesian Network and Dempster-Shafer Method

Livestock is a source of animal protein that contains essential acids that improve human intelligence and health. Popular livestock in Indonesia is cow. Consumption of meat per capita is increased by 0.1% kg / capita / year. The high demand for beef in Indonesia is due to the increasing of population in Indonesia by 1.49% per year. More than 90% of cows are reared by rural communities with less of knowledge about livestock and have low economic capabilities. In addition, the number of experts or veterinarians are also limited. One of the solutions that can be done to socialize the knowledge of experts or veterinarians is by using expert system. Some methods that can be used in expert systems are Bayesian network and Dempster-Shafer method. The purpose of this research is to analyze the comparison of cow disease diagnosis with bayesian network and Dempster-Shafer method. In order to know which method is better in diagnosing cow disease. The data used is 21 cow diseases with 77 symptoms. Each method is tested with the same 10 cases. The conclusions obtained by Bayesian network and Dempster-Shafer method. Both of methods give the same diagnosis results but with different percentage. The mean value of diagnosis percentage by Dempster-Shafer method is 87,2% while bayesian network method is 75,3%. Thus, it can be said that the Dempster-Shafer method is better at diagnosing cow disease.

@article{Aristoteles2019,
  title     = {Comparative Analysis of Cow Disease Diagnosis Expert System using Bayesian Network and Dempster-Shafer Method},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {4},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Aristoteles Aristoteles and Kusuma Adhianto and Rico Andrian and Yeni Nuhricha Sari},
  doi       = {10.14569/IJACSA.2019.0100427},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100427}
}