Penggunaan Sentimen Berita, Indeks Google Trends, dan Faktor yang Berpengaruh Lainnya untuk Memprediksi Harga Gabah Kering Panen (GKP) dengan Deep Learning
Abstract
Indonesia merupakan negara agraris yang mayoritas pekerjaan utama penduduknya adalah bertani, khususnya bertani padi. Keputusan mengenai harga gabah dapat mempengaruhi pendapatan petani serta kelangsungan usaha mereka. Hasil penjualan gabah yang sering tidak stabil ditambah dengan biaya produksi yang semakin meningkat menyebabkan petani mengalami kerugian karena harga jual gabah tidak dapat menghasilkan cukup pendapatan untuk mencakup biaya produksi mereka. Pemerintah menetapkan Harga Pembelian Pemerintah (HPP) untuk menstabilkan harga dan mendorong produksi. Prediksi harga Gabah Kering Panen (GKP) dapat membantu pemerintah dalam pengambilan keputusan terkait stabilisasi harga, subsidi, dan insentif bagi petani untuk kesejahteraan masyarakat. Metode yang digunakan untuk memprediksi harga gabah pada penelitian ini adalah metode LSTM, CNN, dan LSTM-CNN. Model yang telah dibangun dievaluasi berdasarkan nilai MAE, MAPE, MSE, dan RMSE untuk menguji efektivitas kerangka kerja yang diusulkan. Hasil dari penelitian ini menunjukkan bahwa model terbaik yang dipilih untuk memprediksi harga Gabah Kering Panen (GKP) pada penelitian ini adalah model LSTM dengan 7 (tujuh) variabel independen paling berpengaruh dengan nilai MAE, MAPE, RMSE, dan MSE sebesar 438,68, 7,71%, 600,37, dan 360439,91. Variabel tersebut diantaranya adalah Harga Pembelian Pemerintah (HPP), total impor beras, harga eceran beras, rata-rata curah hujan bulanan, Indeks Google Trends “harga gabah”, dan jumlah berita dengan sentimen negatif pada bulan tersebut.
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