Implementasi Naives Bayes-Certainty Factor untuk Diagnosa Penyakit Menular

Yudi Eko Windarto; Marfuah Marfuah

doi:10.32736/sisfokom.v9i2.823

Authors

Yudi Eko Windarto Universitas Diponegoro http://orcid.org/0000-0003-0349-3659
Marfuah Marfuah

DOI:

https://doi.org/10.32736/sisfokom.v9i2.823

Keywords:

Chicken, disease, diagnose, Naïve Bayes, Certainty Factor

Abstract

One type of pet is Chicken (Gallus gallus domesticus). Chickens are the result of the domestication of red jungle fowl (Gallus gallus) which is most commonly kept throughout the world. These animals include animals that are easily maintained because they are good at adapting to various conditions of the place. Types of chickens include Broiler, Kedu, Nunukan, Pelung, Garut. Chicken disease can be caused by bacteria, parasites, and viruses. Sometimes the guards don't know that their pet chickens are sick. This can result in death due to delay in handling given. There are also owners who self-treat their chickens, without knowing clearly beforehand what diseases they are suffering from and what drugs are suitable for the disease. Other factors that influence the occurrence of this problem include the limited availability of veterinarians and are not always available at all times. Therefore, an alternative is needed that can provide convenience and ability like a veterinarian in diagnosing infectious diseases suffered by domestic chickens using the naïve bayes method - certainty based on android with 15 types of diseases including Avian influenza, Encephalomyelitis, Chlamydiosis, Pullorum, Fowl Typhoid , Chicken Pox, Chicken anemia syndrome, Helicopter Disease, Aspergillosis, Infectious Bursal Disease, Infectious Bronchitis, Diarrhea, Bacterial Pododermati, Pyotraumatic Dermatitis and Pyoderma. Analysis of test results is done by comparing the results of system diagnoses with the results of diagnoses performed by experts based on the input of the same symptoms from 50 test data, it is known that 45 system test data are declared to match the expert test data, the accuracy of the accuracy of diagnosis results of 90% is in good category.

Author Biography

Yudi Eko Windarto, Universitas Diponegoro

Departemen Teknik Komputer Fakultas Teknik Universitas Diponegoro

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