SISTEM PEMANTAUAN LINGKUNGAN KANDANG KAMBING BERBASIS IOT DALAM MENDUKUNG SMART FARMING PADA ARJUNA FARM - Ng Poi Wong; Andri Andri; Darwin Darwin; Irpan Adiputra Pardosi; Roni Yunis; Poltak Sihombing; Yudhistira Adhitya Pratama (Universitas Muhammadiyah Mataram, 2025)
Abstrak: Kondisi lingkungan kandang dapat mempengaruhi produktivitas ternak dan dapat menjadi dasar dalam pengembangan infrastruktur peternakan dalam konsep Smart Farm. Permasalahan pada Arjuna Farm adalah kesulitan dalam memperoleh data lingkungan kandang kambing untuk keperluan memprediksi produksi susu hingga pengembangan infrastruktur kandang berbasis Smart Farm. Kegiatan ini bertujuan menerapkan sistem pemantauan lingkungan berbasis IoT untuk memantau suhu, kelembaban, cahaya, suara, gas metana, kualitas udara, dan aktivitas pergerakan kambing. Metode yang digunakan meliputi wawancara, perancangan, pengembangan, penerapan, dan evaluasi sistem dalam bentuk observasi dengan indikator pembacaan manual terhadap visualisasi data secara real-time, dengan melibatkan 7 (tujuh) dosen dari 3 (tiga) institusi dan mitra Arjuna Farm. Kegiatan ini berhasil mengembangkan sistem yang mampu mendeteksi dan memvisualisasikan data sensor kondisi lingkungan kandang kambing, dimana hasil evaluasi menunjukkan sistem berhasil mendeteksi suhu kandang berkisar 25-50°C, kelembaban hingga 99%, intensitas cahaya hingga 175 lux, intensitas suara di kisaran 550 unit sensor, serta gas metana dan kualitas udara secara stabil.Abstract: The environmental conditions of the barn can affect livestock productivity and can form the basis for the development of livestock infrastructure in the Smart Farm concept. The problem at Arjuna Farm is the difficulty in obtaining environmental data from the goat barn for the purpose of predicting milk production and developing Smart Farm-based barn infrastructure. This activity aims to implement an IoT-based environmental monitoring system to monitor temperature, humidity, light, sound, methane gas, air quality, and goat movement activity. The methods used include interviews, design, development, implementation, and evaluation of the system through observation with manual readings of real-time data visualisation, involving 7 (seven) lecturers from 3 (three) institutions and Arjuna Farm partners. This activity successfully developed a system capable of detecting and visualising sensor data on the environmental conditions of goat pens, with evaluation results showing that the system successfully detected pen temperatures ranging from 25-50°C, humidity up to 99%, light intensity up to 175 lux, sound intensity within 550 sensor units, as well as stable methane gas and air quality levels.