Integrasi Otonom Internet of Things dan Business Intelligence Berbasis Causal Machine Learning untuk Mitigasi Risiko Finansial Agrikultur Presisi
Kata Kunci:
Kecerdasan Bisnis Otonom, IoT, Bilstm, Pembelajaran Mesin Kausal, Profitabilitas PertanianAbstrak
Ketidakpastian iklim dan dinamika data lingkungan menjadi tantangan utama bagi stabilitas profitabilitas sektor agrikultur di era Ekonomi 5.0. Penelitian ini bertujuan mengembangkan sistem Business Intelligence (BI) otonom yang mensinkronisasikan data sensor hujan Internet of Things (IoT) dengan proyeksi keuntungan finansial secara real-time. Menggunakan pendekatan pengembangan sistem, kerangka kerja ini memanfaatkan model konseptual data untuk mengintegrasikan aliran data heterogen ke dalam arsitektur Big Data yang terstruktur. Inti analitik sistem menggabungkan algoritma Bi-Directional Long Short-Term Memory (Bi-LSTM) untuk prediksi curah hujan presisi dengan Causal Machine Learning guna menganalisis hubungan sebab-akibat terhadap metrik ekonomi. Hasil penelitian menunjukkan bahwa integrasi ini memungkinkan pengambilan keputusan preskriptif yang transparan dalam strategi mitigasi risiko "pre-rain", seperti optimasi jadwal pemupukan dan panen. Keunggulan sistem terletak pada kemampuan otonom dalam mendeteksi data drift dan melakukan pelatihan ulang model secara mandiri untuk menjaga akurasi di tengah perubahan pola cuaca ekstrem. Implementasi ini memberikan kontribusi signifikan terhadap efisiensi operasional dan peningkatan margin keuntungan melalui transformasi data IoT menjadi kecerdasan bisnis yang tangguh dan adaptif.
Unduhan
Referensi
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