Prediction analysis condition animal use algorithm (SVM+KNN) - Dhany, Hanna Willa; Izhari, Fahmi (Jurnal ICT : Information and Communication Technologies, 2024)
This research focuses on the development of a prediction model. Condition animals use Support Vector Machine (SVM) and K-nearest neighbors (KNN) algorithms. In animal husbandry and health animals, the ability to monitor and analyze condition health in real-time animal monitoring is essential to ensure welfare and productivity. Animals. The SVM and KNN algorithms were selected Because of their advantages in classification and regression tasks. The dataset used covers various health parameters for animals, such as temperature body, heart, diet, daily activity, and health data historical. This study shows that SVM and KNN algorithms are very accurate high in predicting the condition of healthy animals, with SVM achieving an accuracy of 97.63% and KNN achieving an accuracy of 97.16%. This prediction model allows detection early detection of health problems in animals so that the breeder and doctor animals can take action preventive and curative more quickly. The results of this study indicate that The combination of SVM and KNN can provide better predictions. Accurate and reliable, which ultimately will increase the health and well-being of animals.