Prediction of Layer Chicken Disease using Fuzzy Analytical Hierarcy Process

  • Abstract
  • Keywords
  • References
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  • Abstract

    • Chicken disease belong to the herpes group that often attacks poultry like laying hens. Various types of diseases that can attack such as marek, IB chicken, chicken NP, CA, EDS. Therefore it is necessary to be given a special vaccine for poultry that can anticipate the dominant diseases attacking poultry in particular chicken laying. So need a prediction model with the concept of fuzzy analytical hierarchy process. analytical hierarchy process is one of the methods in the decision-making system that uses several variables with a multilevel analysis process using criteria such as decreased egg production, cough, watery eyes, wings hanging down, a gray sprocket, legs paralyzed. From the test results obtained varied values with alternative results obtained: Marek 0.1487, IB chicken 0.3464, CA 0.1769, chicken NP 0.2407, EDS 0.0884. Then fuzzy analytical hierarcy process is good for predicting laying hens disease

  • Keywords

    Fuzzy, Chicken Disease Prediction, Analytical Hierarcy Process (AHP).

  • References

      [1] S. F. Hernomoadi Huminto, Bambang Pontjo Priosoeryanto, I Wayan Teguh Wibawan, Dewi Ratih Agungpriyono, Eva Harlina, “Diagnostic Case of Marek’s Disease in Chickens,” in Seminar Nasional Peternakan dan Veteriner 2000, 2000, vol. 1, pp. 543–546.

      [2] Bambang Yuwono, “Expert System for Diagnosis of Chicken Diseases Caused by Viruses,” Telematika, vol. 6, no. Sistem Pakar, pp. 41–48, 2010.

      [3] S. Rohajawati and R. Supriyati, “Expert System: Poultry Diabetic Dyscogenosis by Certainty Factor Method,” CommIT, vol. 4, no. Sistem Pakar, pp. 41–46, 2010.

      [4] Mohamad Hadi, M. Misdram, and R. F. A, “Design of Expert System Diagnosis of Chicken Disease With Forward Chaining Method,” JImp, vol. 2, no. ISSN : 2503-1945, pp. 111–139, 2016.

      [5] E. Turban, R. Sharda, and D. Delen, Decision Support and Business Intelligence Systems. Chapter 6 Artificial Neural Networks for Data Mining, vol. 8th. 2007.

      [6] R. Irviani, I. Dinulhaq, D. Irawan, R. Renaldo, and A. Maseleno, “Areas Prone of the Bad Nutrition based Multi Attribute Decision Making with Fuzzy Simple Additive Weighting for Optimal Analysis,” Int. J. Pure Appl. Math., vol. 118, no. 7, pp. 589–596, 2018.

      [7] S. Mukodimah, M. Muslihudin, A. Andoyo, S. Hartati, and A. Maseleno, “Fuzzy Simple Additive Weighting and its Application to Toddler Healthy Food,” Int. J. Pure Appl. Math., vol. 118, no. 7, pp. 1–7, 2018.

      [8] T. Noviarti, M. Muslihudin, R. Irviani, and A. Maseleno, “Optimal Dengue Endemic Region Prediction using Fuzzy Simple Additive Weighting based Algorithm,” Int. J. Pure Appl. Math., vol. 118, no. 7, pp. 473–478, 2018.

      [9] E. Turban, J. E. Aronson, and T.-P. Liang, “Decision Support Systems and Intelligent Systems,” Decis. Support Syst. Intell. Syst., vol. 7, p. 867, 2007.

      [10] S. I. Yanti Aprilda, “Decision Support System For The Marketing Of Salt Micro Business Using Analytical Hierarchy Process,” Prosseding KMSI, vol. 1, no. 1, p. 10, 2017.

      [11] A. Maseleno, M. M. Hasan, M. Muslihudin, and T. Susilowati, “Finding Kicking Range of Sepak Takraw Game: Fuzzy Logic and Dempster-Shafer Theory Approach,” Indones. J. Electr. Eng. Comput. Sci., vol. 2, no. 1, p. 187, 2016.

      [12] A. Maseleno, N. Tuah, and C. R. Tabbu, “Fuzzy Logic and Dempster-Shafer Theory to Predict the Risk of Highly Pathogenic Avian Influenza H5n1 Spreading Computer Science Program , Universiti Brunei Darussalam , Faculty of Veterinary Medicine , Gadjah Mada University , Indonesia,” World Appl. Sci. J., vol. 34, no. 8, pp. 995–1003, 2016.

      [13] A. Maseleno, G. Hardaker, N. Sabani, and N. Suhaili, “Data on multicultural education and diagnostic information profiling: Culture, learning styles and creativity,” Data Br., vol. 9, pp. 1048–1051, 2016.

      [14] A. Maseleno and G. Hardaker, “Malaria detection using mathematical theory of evidence,” SJST, vol. 38, no. 3, pp. 257–263, 2016.

      [15] Y. M. Wang and K. S. Chin, “Fuzzy analytic hierarchy process: A logarithmic fuzzy preference programming methodology,” Int. J. Approx. Reason., vol. 52, no. 4, pp. 541–553, 2011.

      [16] Z. Xu and J. Chen, “An interactive method for fuzzy multiple attribute group decision making,” Elsevier, vol. 177, no. 70321001, pp. 248–263, 2007.

      [17] Y. Narukawa and T. Gakuen, “Fuzzy Measures and integrals for evaluating strategies,” in Proceedings Information Technology Coding and Computing, 2004, pp. 1–5.

      [18] S. W. Satria Abadi, “The Model of Determining Quality of Management Private Higher Education Using FAHP (Fuzzy Analytic Hierarchy Process) Method,” in ICESIA 1, 2016, vol. 1, no. 1, pp. 166–172.

      [19] H. Kolahi et al., “Evaluation of Respiratory Protection Program in Petrochemical Industries: Application of Analytic Hierarchy Process,” Saf. Health Work, pp. 3–8, 2017.

      [20] Y. Saputra, “Decision Support System For Selection Laptop With Analytical Hierarchy Process ( AHP ),” Skripsi UNDINUS, pp. 1–8, 2015.

      [21] S. Başaran and Y. Haruna, “Integrating FAHP and TOPSIS to evaluate mobile learning applications for mathematics,” in Procedia Computer Science, 2017, vol. 120, pp. 91–98.

      [22] J. Franek and A. Kresta, “Judgment Scales and Consistency Measure in AHP,” Procedia Econ. Financ., vol. 12, no. March, pp. 164–173, 2014.

      [23] R. P. Kusumawardani and M. Agintiara, “Application of Fuzzy AHP-TOPSIS Method for Decision Making in Human Resource Manager Selection Process,” Procedia Comput. Sci., 2015.




Article ID: 14360
DOI: 10.14419/ijet.v7i2.26.14360

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