Penerapan Algoritma Extra Trees untuk Klasifikasi Kondisi Unsur Hara Tanah: Application of the Extra Trees Algorithm for Soil Nutrient Condition Classification

Anas Haetami Putra Ramadhan; Ade Silvia Handayani; Ahmad Taqwa

doi:10.57152/malcom.v6i4.2953

Authors

Anas Haetami Putra Ramadhan Politeknik Negeri Sriwijaya
Ade Silvia Handayani Politeknik Negeri Sriwijaya
Ahmad Taqwa Politeknik Negeri Sriwijaya

DOI:

https://doi.org/10.57152/malcom.v6i4.2953

Keywords:

Extra Trees, Kondisi Hara Tanah, Machine Learning, Pertanian Presisi, Rekomendasi Pemupukan

Abstract

Ketersediaan unsur hara tanah merupakan penentu mendasar produktivitas pertanian. Penggunaan pupuk yang tidak tepat serta mengabaikan kondisi tanah dapat mengurangi efisiensi, meningkatkan biaya, dan menurunkan hasil panen. Oleh karena itu, pendekatan berbasis data diperlukan untuk memberikan rekomendasi pupuk yang akurat. Studi ini menerapkan algoritma Extra Trees untuk mengklasifikasikan kondisi unsur hara tanah menggunakan Fertilizer Recommendation Dataset. Persiapan data melibatkan inspeksi kualitas, pengkodean label untuk atribut kategorikal, dan pembagian dataset 80:20 untuk pelatihan dan validasi. Validasi model menggunakan metrik akurasi, presisi, recall, dan F1-score. Selain itu, validasi silang 5-Fold mengevaluasi konsistensi dan kemampuan generalisasi model. Model mencapai akurasi pengujian 94,50%. Validasi silang menghasilkan akurasi rata-rata 94,02% (SD = 0,0053), menunjukkan ketahanan di berbagai pembagian data. Analisis kepentingan fitur mengidentifikasi Nitrogen, Fosfor, dan Kalium sebagai atribut klasifikasi yang paling penting. Hasil penelitian menunjukkan efektivitas algoritma dalam mengklasifikasikan kondisi tanah dan merekomendasikan pupuk. Pada akhirnya, metodologi ini menawarkan potensi signifikan untuk meningkatkan pengambilan keputusan di bidang pertanian melalui pendekatan aplikasi yang efisien dan berbasis data.

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