OPTIMASI MODEL PREDIKSI EXTREME GRADIENT BOOSTING DENGAN GENETIC ALGORITHM UNTUK PRODUKSI DAN PRODUKTIVITAS PADI
DOI:
https://doi.org/10.55123/storage.v4i4.6633Keywords:
Produksi Padi, Produktivitas Padi, Genetic Algorithm, XGBoost, GA-XGBoostAbstract
Produksi padi di Kabupaten Buleleng meningkat dari tahun 2021 hingga 2023, namun produktivitas justru menurun hingga 8%. Kondisi ini berpotensi mengganggu stabilitas pasokan beras di tengah pertumbuhan penduduk sebesar 4,59% per tahun. Diperlukan pendekatan prediktif berbasis kecerdasan buatan untuk memodelkan hubungan kompleks antar variabel pertanian. XGBoost dipilih karena kemampuannya dalam menangkap pola non-linear dan sering digunakan dalam analisis data pertanian, sementara Genetic Algorithm (GA) digunakan untuk menentukan kombinasi hiperparameter optimal guna meningkatkan performa model. Model XGBoost tanpa optimasi diterapkan sebagai pembanding untuk mengevaluasi efektivitas pendekatan hybrid. Hasil analisis menunjukkan bahwa optimasi hiperparameter berpengaruh signifikan terhadap hasil prediksi. Model GA-XGBoost menghasilkan tingkat kesalahan lebih rendah, dengan penurunan nilai MAPE sekitar 2,98% untuk prediksi produksi padi dan 0.21% untuk produktivitas dibandingkan dengan model standar atau default.
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