HAFA-TANI: FRAMEWORK AGENTIC AI UNTUK PERTANIAN TROPIS
DOI:
https://doi.org/10.24076/joism.2026v8i1.2699Keywords:
agentic AI, kesesuaian lahan, pertanian tropis, sistem rekomendasi, explainable AIAbstract
Pertanian tropis Indonesia membutuhkan sistem pendukung keputusan yang mampu mengintegrasikan evaluasi kesesuaian lahan, perancangan tata letak tanaman, penilaian risiko sumber daya, dan penjelasan rekomendasi. Kebutuhan ini penting karena keputusan budidaya dipengaruhi variasi agroekologi, keterbatasan data lahan, dominasi petani berlahan kecil, fragmentasi sistem AI pertanian, serta risiko iklim. Penelitian ini mengusulkan HAFA-Tani sebagai framework agentic AI berbasis design research. Metode penelitian mencakup sintesis literatur terbuka 2021-2026, pencarian terarah pada basis data ilmiah, seleksi inklusi-eksklusi, pemetaan konsep, analisis kesenjangan, perumusan prinsip desain, dan validasi analitis melalui scenario-based demonstration. Framework terdiri atas Orchestrator Agent, Land Suitability Agent, Crop Layout and Rotation Agent, Resource and Risk Agent, Explainability Agent, serta Governance and Memory Layer. Hasil perancangan menunjukkan bahwa orkestrasi multi-agen dapat menghubungkan prediksi, optimasi, risiko, explainability, dan audit trail dalam satu alur rekomendasi. Keterbatasan penelitian adalah framework masih konseptual dan belum divalidasi empiris pada dataset serta pengguna pertanian Indonesia.
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