Implementasi Machine Learning untuk Prediksi Kebutuhan Irigasi Tanaman pada Sistem Smart Agriculture Berbasis Internet of Things
Kata Kunci:
Smart Agriculture, Machine Learning, Internet of Things, Prediksi Irigasi, Pertanian CerdasAbstrak
Perkembangan teknologi Internet of Things (IoT) dan Machine Learning telah membuka peluang dalam pengembangan Smart Agriculture untuk meningkatkan efisiensi pengelolaan sumber daya pertanian, khususnya pada sistem irigasi tanaman. Pengelolaan irigasi yang masih dilakukan secara konvensional sering menyebabkan pemborosan air dan menurunkan produktivitas tanaman. Tujuan penelitian ini bertujuan untuk mengimplementasikan Machine Learning dalam memprediksi kebutuhan irigasi tanaman pada sistem Smart Agriculture berbasis Internet of Things sehingga penggunaan air dapat dilakukan secara lebih efektif dan efisien. Metode penelitian menggunakan metode kuantitatif dengan pendekatan pengembangan sistem yang mengintegrasikan sensor IoT dan algoritma Machine Learning. Data lingkungan diperoleh dari sensor kelembapan tanah, suhu udara, dan kelembapan udara yang dikumpulkan secara real-time, kemudian melalui proses preprocessing, pelatihan model, dan pengujian untuk menghasilkan prediksi kebutuhan irigasi tanaman. Hasil penelitian menunjukkan bahwa sistem berhasil mengumpulkan dan mengolah data lingkungan secara otomatis melalui perangkat IoT. Model Machine Learning mampu menganalisis hubungan antara kelembapan tanah, suhu udara, dan kelembapan udara untuk menghasilkan prediksi kebutuhan air tanaman secara real-time. Integrasi IoT dan Machine Learning memungkinkan proses monitoring lahan berlangsung secara berkelanjutan serta membantu menentukan waktu dan jumlah irigasi yang lebih tepat dibandingkan metode konvensional. Implementasi Machine Learning pada sistem Smart Agriculture berbasis IoT mampu mendukung pengambilan keputusan dalam pengelolaan irigasi secara lebih akurat dan efisien. Sistem yang dikembangkan berpotensi meningkatkan produktivitas pertanian sekaligus mengoptimalkan penggunaan sumber daya air. Penelitian selanjutnya dapat difokuskan pada pengembangan model prediksi yang lebih kompleks dengan memanfaatkan data lingkungan yang lebih beragam untuk meningkatkan akurasi sistem.
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