Penerapan Recurrent Neural Network untuk Prediksi Kesehatan Sapi Berdasarkan Analisis Data Sensor Fisiologis - Riski Kurniawan; Budi Santoso; Bunga Intan (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
Livestock health, particularly of cattle, is a crucial factor in the livestock industry as it directly affects productivity and animal welfare. However, health monitoring in the field is still largely conducted manually, leading to delays in early disease detection and increasing the risk of economic loss for farmers. This study aims to develop and evaluate a cattle health prediction model using a Recurrent Neural Network (RNN) approach based on physiological sensor data such as body temperature, heart rate, and physical activity. The data were collected in real time using Internet of Things (IoT) technology at a farm located in Tanah Periuk Village, Musi Rawas Regency. The results show that the developed RNN model achieved an accuracy of 98.88%, precision of 0.99, and recall of 0.99, indicating high performance in detecting potential cattle health issues. These findings are expected to provide a practical solution for farmers to support timely and accurate decision-making and improve overall livestock welfare.