<![CDATA[Sheep farming is one of the important sectors that supports food security and the local economy, particularly in Garut Regency. However, sheep health monitoring is generally conducted manually, which may lead to delays in detecting health disorders. This research aims to design and develop an Internet of Things (IoT)-based sheep health monitoring system using wearable sensors and the Regresi Linear method for real-time health condition classification. The proposed system utilizes the MAX30102 sensor to measure heart rate, the DS18B20 sensor to monitor body temperature, and the MPU6050 sensor to detect physical activity. The collected sensor data are processed by an ESP32 microcontroller and analyzed using the Regresi Linear algorithm to classify sheep health conditions into three categories: healthy, alert, and emergency. The analysis results are transmitted through a WiFi network and displayed on the Telegram application, enabling farmers to remotely monitor the health status of their livestock. This study employs a Research and Development (R&D) methodology combined with a case study approach and experimental testing. The developed system is expected to assist farmers in monitoring sheep health more effectively, accurately, and efficiently while supporting the adoption of digital technology in the livestock sector of Rancabango Village, Garut.]]>
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