Penerapan Algoritma Random Forest untuk Prediksi Kebutuhan Nutrisi Harian Sapi Potong Berdasarkan Berat Badan
DOI:
https://doi.org/10.29407/xr4f8f14Keywords:
Nutrisi Sapi, Machine Learning, Random Forest, Prediksi NutrisiAbstract
Penentuan kebutuhan nutrisi sapi potong masih banyak dilakukan menggunakan tabel konvensional sehingga kurang presisi dalam menyesuaikan kebutuhan individu setiap ternak. Hal ini dapat menurunkan efisiensi pakan dan meningkatkan biaya operasional peternakan. Penelitian ini bertujuan mengembangkan sistem prediksi nutrisi sapi potong berbasis Machine Learning untuk mempermudah penyusunan ransum presisi hanya berdasarkan input berat badan. Metode penelitian meliputi pengumpulan data nutrisi sapi dari sumber resmi, perancangan arsitektur sistem prediksi, preprocessing, dan normalisasi data, pelatihan model Random Forest Regression, implementasi sistem, serta evaluasi performa model. Hasil penelitian menunjukkan bahwa model mampu memprediksi kebutuhan nutrisi harian sapi potong untuk parameter BK, PK, TDN, Ca, dan P dengan tingkat akurasi yang sangat baik, dibuktikan oleh nilai evaluasi R² sebesar 0.964. Sistem yang dibangun memungkinkan peternak memperoleh hasil prediksi nutrisi secara cepat dan otomatis tanpa perhitungan manual. Temuan ini menegaskan potensi teknologi prediksi berbasis data dalam mendukung pakan presisi sehingga mampu meningkatkan efisiensi produksi dan mengurangi pemborosan pakan.
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