Penerapan Machine Learning pada Smart Agriculture untuk Prediksi Hasil Panen Berdasarkan Faktor Lingkungan
Kata Kunci:
Smart Agriculture, Machine Learning, Prediksi Hasil Panen, Faktor Lingkungan, Kecerdasan Buatan.Abstrak
Perkembangan teknologi digital dalam sektor pertanian mendorong munculnya konsep Smart Agriculture sebagai solusi untuk meningkatkan produktivitas dan efisiensi pertanian melalui pemanfaatan data dan kecerdasan buatan. Permasalahan utama dalam pertanian modern adalah ketidakpastian kondisi lingkungan seperti suhu, kelembaban, curah hujan, intensitas cahaya, dan kondisi tanah yang dapat memengaruhi hasil produksi tanaman. Penelitian ini bertujuan untuk menerapkan metode Machine Learning dalam melakukan prediksi hasil panen berdasarkan faktor lingkungan sehingga dapat membantu proses pengambilan keputusan pada bidang pertanian. Metode penelitian yang digunakan adalah pendekatan kuantitatif dengan metode eksperimen, yaitu melalui tahapan pengumpulan data lingkungan dan data hasil panen, preprocessing data, pembangunan model Machine Learning, serta evaluasi performa model menggunakan parameter pengukuran kesalahan prediksi. Hasil penelitian menunjukkan bahwa model Machine Learning mampu mengolah data faktor lingkungan untuk menemukan pola hubungan terhadap produktivitas tanaman dan menghasilkan sistem prediksi yang dapat digunakan sebagai pendukung keputusan pertanian. Proses preprocessing dan pemilihan fitur menjadi faktor penting dalam meningkatkan kualitas model karena data lingkungan memiliki karakteristik yang kompleks dan dinamis. Kesimpulan dari penelitian ini menunjukkan bahwa penerapan Machine Learning pada Smart Agriculture dapat menjadi pendekatan efektif dalam meningkatkan akurasi prediksi hasil panen serta mendukung pengelolaan pertanian berbasis data. Penelitian selanjutnya dapat dikembangkan dengan integrasi data sensor IoT secara real-time serta pengujian berbagai algoritma Machine Learning untuk memperoleh model dengan performa prediksi yang lebih optimal.
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Hak Cipta (c) 2026 Mohammad Faisol (Penulis); Syamsul Arifin (Penerjemah)
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