Perbandingan Algoritma Klasifikasi untuk Rekomendasi Tanaman Berdasarkan Data Lingkungan
Abstract
Pemilihan tanaman yang tepat sangat penting dalam meningkatkan produktivitas pertanian. Dengan adanya teknologi machine learning, proses rekomendasi tanaman berdasarkan data lingkungan dapat lebih efisien, terutama dalam menghadapi kondisi iklim yang bervariasi. Penelitian ini bertujuan untuk membandingkan tiga algoritma klasifikasi, yaitu Random Forest, XGBoost, dan SVM, dalam memberikan rekomendasi tanaman yang sesuai berdasarkan data lingkungan yang mencakup suhu, kelembaban, pH tanah, dan curah hujan. Penelitian ini menggunakan dataset yang mencakup fitur lingkungan dari BPS Kota Tasikmalaya, yang kemudian diuji dengan tiga algoritma klasifikasi machine learning Random Forest, XGBoost, dan SVM. Setiap model dievaluasi berdasarkan akurasi, precision, recall, dan F1-score. Random Forest menunjukkan hasil terbaik dengan akurasi 99.32%, diikuti oleh XGBoost dengan akurasi 98.64%, dan SVM dengan akurasi 96.82%. Model-model ini memberikan rekomendasi tanaman seperti jeruk dan melon, sementara SVM lebih sering merekomendasikan mothbeans. Random Forest memberikan hasil yang paling optimal dalam sistem rekomendasi tanaman berbasis data lingkungan, meskipun SVM lebih cepat dalam hal pelatihan model. Penelitian ini menunjukkan pentingnya penerapan algoritma machine learning untuk mendukung keputusan pertanian berbasis kondisi lingkungan lokal.
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DOI: https://doi.org/10.30591/polektro.v14i1.8682
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Tim Redaksi POWER ELEKTRONIK : JURNAL ORANG ELEKTRO
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