Implementasi IoT pada Sistem Irigasi Otomatis Berbasis Machine Learning untuk Pertanian Cerdas
Kata Kunci:
Smart Agriculture, Internet of Things (IoT), Machine Learning, Irigasi Otomatis, Optimalisasi Air, Pertanian PresisiAbstrak
Kerugian volumetrik air dan penurunan produktivitas tanaman pada sektor pertanian sering disebabkan oleh sistem irigasi tradisional yang tidak fleksibel dalam menghadapi fluktuasi variabel lingkungan. Guna mengatasi problem tersebut, riset ini mengintegrasikan arsitektur IoT dan komputasi cerdas Machine Learning untuk menciptakan sistem pengairan otomatis yang hemat air dan mampu menjaga kualitas tanah tetap ideal. Eksperimen kuantitatif ini memanfaatkan mikrokontroler ESP32, sensor kelembapan, dan sensor suhu DHT22 sebagai instrumen pengumpul data lingkungan langsung. Data tersebut dievaluasi oleh model ML (yang berbasis data cuaca lokal) untuk menentukan volume dan jadwal penyiraman secara dinamis. Kinerja perangkat dianalisis selama beberapa minggu operasional dan dikomparasikan dengan sistem irigasi otomatis biasa. Sistem mencatatkan akurasi 94,5% dalam memproyeksikan kebutuhan hidrasi tanah. Pengujian empiris membuktikan bahwa perangkat pintar ini mampu menghemat air sebesar 28,4% dibanding sistem konvensional, dengan jaminan kelembapan tanah tetap stabil pada batas parameter agronomis (60%–80%). Sinergi IoT dan pembelajaran mesin terbukti memberikan solusi yang presisi untuk tata kelola air pada pertanian cerdas masa kini. Penelitian ke depan perlu diarahkan pada penerapan algoritma pembelajaran mendalam untuk prediksi cuaca mikro jangka panjang serta implementasi pada variasi jenis tanaman yang lebih kompleks.
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Hak Cipta (c) 2026 Imam (Penulis); Moh. Gilank Alamsyah (Penerjemah)
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