Prediksi Produktivitas Jagung Berbasis Explainable Machine Learning dan Seleksi Fitur Adaptif
Abstract
Produktivitas jagung dipengaruhi oleh faktor agroklimat dan karakteristik tanah yang saling berinteraksi secara kompleks, sehingga diperlukan pendekatan prediktif yang akurat sekaligus interpretatif. Penelitian ini bertujuan membangun model prediksi produktivitas jagung berbasis machine learning dengan dukungan seleksi fitur adaptif dan explainable machine learning. Data penelitian terdiri atas 180 observasi yang mencakup variabel produktivitas jagung, agroklimat, dan sifat tanah. Tahapan penelitian meliputi pra-pemrosesan data, seleksi fitur adaptif, pengembangan model, evaluasi performa, dan analisis feature importance. Model yang diuji meliputi Elastic Net, Random Forest, dan Gradient Boosting. Hasil penelitian menunjukkan bahwa seleksi fitur adaptif berhasil mereduksi 10 fitur numerik awal menjadi 6 fitur optimal, yaitu curah hujan, penyinaran, suhu rata-rata, elevasi, nitrogen, dan pH tanah. Model terbaik diperoleh pada Random Forest setelah seleksi fitur dengan nilai RMSE sebesar 0,548, MAE sebesar 0,426, dan R² sebesar 0,528. Analisis explainability menunjukkan bahwa curah hujan merupakan fitur paling dominan, diikuti pH tanah, suhu rata-rata, elevasi, penyinaran, dan nitrogen. Hasil ini menegaskan bahwa integrasi seleksi fitur adaptif dan explainable machine learning efektif untuk menghasilkan model prediksi produktivitas jagung yang lebih akurat, efisien, dan interpretatif
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