Sistem Pemantauan Suhu dan Kelembapan Kandang Ayam Berbasis ANN-LSTM
Abstract
Mempertahankan suhu dan tingkat kelembapan yang konsisten sangat krusial demi memastikan kesehatan, produktivitas, serta kesejahteraan unggas salah satunya adalah ayam, khususnya di daerah tropis di mana perubahan mendadak dapat menyebabkan stres akibat panas dan penurunan performa. Namun, metode pengawasan tradisional seringkali hanya terbatas pada observasi langsung dan minim dalam hal kemampuan untuk melakukan prediksi. Penelitian ini memperkenalkan sistem monitoring yang berbasis pada ANN–LSTM yang terhubung dengan teknologi Internet of Things (IoT) untuk mengevaluasi kondisi lingkungan di dalam kandang ayam. Data lingkungan diperoleh melalui sistem pemantauan berbasis sensor dan diproses dengan model pembelajaran mendalam hibrida untuk mendeteksi pola temporal serta melakukan kegiatan peramalan dan klasifikasi. Model ini mencapai nilai R² di atas 0,95 dengan kesalahan prediksi yang sangat rendah untuk suhu dan kelembapan, serta tingkat akurasi klasifikasi yang mencapai 95,15% dalam mengidentifikasi kondisi Normal, Peringatan, dan Bahaya. Temuan ini menunjukkan bahwa sistem yang dikembangkan menawarkan pendekatan yang menggabungkan pemantauan secara langsung dengan analisis prediktif dalam peternakan unggas.
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