Sistem Rekomendasi Pupuk Berdasarkan Kondisi Dan Jenis Tanah Menggunakan LightGBM
Abstract
Pemupukan yang tidak tepat masih menjadi permasalahan dalam sektor pertanian karena dapat menurunkan produktivitas tanaman, meningkatkan biaya produksi, serta menyebabkan penggunaan pupuk yang kurang efisien. Oleh karena itu, diperlukan sistem rekomendasi pupuk yang dapat membantu petani menentukan jenis pupuk yang sesuai berdasarkan kondisi tanah. Penelitian ini mengembangkan sistem rekomendasi pupuk berbasis machine learning menggunakan algoritma Light Gradient Boosting Machine (LightGBM) untuk mendukung pertanian presisi. Dataset yang digunakan terdiri dari 10.000 data dengan delapan parameter input, yaitu jenis tanah (clay, silt, sandy, loamy), kelembaban tanah, pH tanah, kandungan karbon organik, serta kadar nitrogen, fosfor, dan kalium. Target prediksi terdiri dari tujuh kelas pupuk. Tahapan penelitian meliputi preprocessing (data cleaning, label encoding, dan normalisasi min-max), pembagian dataset dengan rasio 80:20, pelatihan model LightGBM dengan konfigurasi n_estimators=500 dan learning_rate=0,1, serta evaluasi menggunakan confusion matrix dan classification report. Hasil eksperimen menunjukkan akurasi sebesar 86,35% pada data pengujian. Analisis feature importance menunjukkan bahwa Soil Moisture merupakan fitur paling berpengaruh, diikuti Soil pH, Organic Carbon, dan Nitrogen Level. Model menunjukkan performa sangat baik pada kelas Urea dan DAP dengan f1-score mencapai 0,95. Hasil penelitian membuktikan bahwa LightGBM efektif untuk klasifikasi multi-kelas dalam sistem rekomendasi pupuk dan berpotensi mendukung pertanian presisi yang lebih efisien dan berkelanjutan.
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DOI: http://dx.doi.org/10.30872/jurti.v10i3.26964
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