DOI: https://doi.org/10.14710/jil.22.2.313-325

Pemodelan Ensemble Prediksi Distribusi Ekologis Padi (Oryza sativa) di Provinsi Kalimatan Utara

1Program Studi Ilmu Pengelolaan Sumberdaya Alam dan Lingkungan, Sekolah Pascasarjana, IPB University, Bogor 16144, Indonesia, Indonesia

2Departemen Silvikultur, Fakultas Kehutanan dan Lingkungan, IPB University, Bogor 16680, Indonesia, Indonesia

3Departemen Ilmu Ekonomi, Fakultas Ekonomi dan Manajemen, IPB University, Bogor 16680, Indonesia, Indonesia

Received: 5 Aug 2022; Revised: 2 Sep 2023; Accepted: 8 Nov 2023; Available online: 4 Feb 2024; Published: 15 Feb 2024.
Editor(s): Budi Warsito

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Abstract

Pemerintah Provinsi Kalimantan Utara berusaha mencapai ketahanan pangan dengan prinsip kemandirian pangan melalui perluasan lahan pertanian. Penilaian kesesuaian lahan pertanian, terutama untuk padi, dilakukan menggunakan pendekatan pemodelan ensemble yang melibatkan lima algoritma pembelajaran mesin. Model-model ini dibangun menggunakan paket species distribution modeling (SDM) di RStudio dengan pembagian data pelatihan dan pengujian 70:30 serta pengaturan parameter termasuk bootstrapping dan tiga kali pengulangan. Hasil penelitian menunjukkan variasi dalam respons variabel prediktor antara algoritma. Variabel NDVI memiliki pengaruh tertinggi pada SVM dan BRT (masing-masing 48,1% dan 36,6%), sementara variabel jarak dari jalan paling berpengaruh pada GLM, MARS, dan RF (masing-masing 44,6%, 27,6%, dan 26,5%). Distribusi padi (sawah) bervariasi antara model, dengan RF memiliki persentase tertinggi (6,34%). Evaluasi kinerja model-model ini menunjukkan bahwa model RF memiliki akurasi terbaik, sementara GLM memiliki akurasi buruk dalam nilai Kappa Cohen. Model ensemble memperoleh akurasi yang dapat diterima dengan nilai masing-masing 0,96; 0,70; dan 0,71 untuk AUC, TSS, dan Kappa. Dengan demikian, pendekatan pemodelan multi-algoritma dengan model ensemble memungkinkan penilaian yang lebih baik terhadap variabilitas dalam kinerja algoritma dan menghasilkan peta kesesuaian distribusi padi yang lebih baik daripada algoritma tunggal.

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Keywords: Ensemble; Model distribusi spesies; Regresi; Pembelajaran mesin; Kesesuaian lahan padi.

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