Certainty Factor Algorithm Approach for Early Stage of Cattle Disease Diagnosis Using Mobile-Based Expert System

Budy  Satria; Nurfiah Nurfiah; Bima  Prakasa

doi:10.52436/1.jutif.2026.7.3.5374

Authors

Budy Satria Faculty of Information Technology, Department of Informatics, Universitas Andalas, Indonesia
Nurfiah Faculty of Information Technology, Department of Informatics, Universitas Andalas, Indonesia
Bima Prakasa Faculty of Information Technology, Department of Informatics, Universitas Andalas, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.3.5374

Keywords:

Cattle Disease Diagnosis, Certainty Factor, Early Detection, Expert System, Mobile-Based

Abstract

Cattle are one of the livestock that play a crucial role in meeting the demand for meat and milk, as well as providing a source of income for farmers, particularly in various regions of Indonesia. Diseases in cattle pose a serious problem due to the lack of knowledge about accessing veterinary services, a lack of understanding among farmers, and the high cost and time required for consultations, which are significant obstacles for farmers in identifying diseases in cattle early, potentially leading to death. Limitations in accessing veterinary services, a lack of understanding among farmers, and the high cost and time required for consultations are significant obstacles to treating diseases in cattle. This study aims to assist farmers in diagnosing cattle diseases using an expert system based on the observed symptoms. The application of the expert system employs a certainty factor algorithm approach, utilizing the knowledge base of animal experts in the diagnosis process. This study used 6 types of diseases and 34 lists of symptoms in cattle. Based on the results of implementing the Certainty Factor method, it was concluded that the expert system was able to diagnose cattle diseases, specifically worms, with a confidence level of 90.1504%. This is certainly influenced by the selection of symptoms, the user's confidence value for each symptom, and the combination of the confidence values from experts. In addition, testing was also carried out on the functionality of the expert system built; the results obtained showed that all functionalities run well and as expected. Thus, the final conclusion is that expert systems can be a solution and help farmers diagnose cattle diseases. Suggestions for further research include comparing algorithms to achieve better accuracy and disease identification in specific cattle species.

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