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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Putra, Fareza Ananda
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Computer Science & IT Engineering

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Early goat disease detection using temperature models: k-nearest neighbor, decision tree, naive Bayes, and random forest Putra, Fareza Ananda; Wella, Wella
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3835-3846

Abstract

This study aims to aid livestock activities by enabling early detection of diseases in goats through body temperature measurement. Early detection is crucial to prevent disease spread and improve livestock welfare. Using the knowledge discovery in databases (KDD) methodology, the study involves collecting, processing, and analyzing goat body temperature data. Four algorithms—k-nearest neighbor (KNN), decision tree, naive Bayes, and random forest—were used to develop disease detection models. The decision tree algorithm was found to be the most accurate, achieving 100% accuracy. This demonstrates its effectiveness in detecting diseases based on body temperature. Implementing this model is expected to significantly benefit farmers by helping maintain the health and productivity of their livestock.
Co-Authors Wella, Wella