Penerapan Algoritma Regresi Random Forest Untuk Prediksi Produksi Jagung Menggunakan Data Statistik Sistem Pertanian Cerdas Smart City
DOI:
https://doi.org/10.47701/19h5ny78Keywords:
Jagung, Produksi Pertanian, Prediksi, Random Forest Regression, Smart City, Machine LearningAbstract
Produksi jagung di Indonesia masih dihadapkan pada berbagai permasalahan seperti ketidakstabilan hasil panen serta kurangnya data prediktif yang mendukung pengambilan keputusan berbasis informasi. Penelitian ini bertujuan untuk mengembangkan model prediksi produksi jagung yang akurat menggunakan algoritma Random Forest Regression berbasis data statistik pertanian. Metode yang digunakan bersifat kuantitatif dengan memanfaatkan data luas panen dan produktivitas jagung dari 38 provinsi di Indonesia tahun 2024, yang dianalisis menggunakan Python dan pustaka machine learning. Hasil evaluasi menunjukkan bahwa model mampu menjelaskan 98% variasi produksi jagung, dengan nilai R² sebesar 0,98 pada data uji. Nilai Mean Squared Error (MSE) dan Mean Absolute Error (MAE) yang rendah menandakan tingkat akurasi yang tinggi. Visualisasi hasil prediksi menunjukkan bahwa nilai-nilai prediksi sangat mendekati nilai aktual. Fitur luas panen memiliki kontribusi paling besar dalam prediksi, diikuti oleh produktivitas. Model disimpan dalam format .pkl dan diintegrasikan ke dalam aplikasi web untuk memudahkan penggunaannya oleh petani maupun dinas pertanian. Penelitian ini mendukung penerapan pertanian cerdas dalam konsep Smart City serta dapat digunakan sebagai alat bantu pengambilan keputusan berbasis data.
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