Prediksi Harga Cabai Musiman Menggunakan Model LSTM di Jawa Tengah
DOI:
https://doi.org/10.23969/infomatek.v27i2.26460Keywords:
Cabai, cuaca, fluktuasi harga, LSTM, model prediksiAbstract
Penelitian ini mengembangkan model prediksi harga cabai musiman di Jawa Tengah menggunakan algoritma Long Short-Term Memory (LSTM) yang mengintegrasikan data harga harian dan data cuaca, termasuk curah hujan, suhu udara, dan kelembapan relatif. Model ini dirancang untuk memprediksi fluktuasi harga cabai yang dipengaruhi oleh faktor musiman dan kondisi cuaca, yang sering menyebabkan ketidakstabilan harga. Hasil eksperimen menunjukkan bahwa model LSTM berhasil menghasilkan prediksi dengan nilai RMSE 512,83, MAE 387,49, dan R² 0,861, yang mengindikasikan kemampuan model dalam menangkap pola harga yang dipengaruhi oleh faktor eksternal. Keunggulan utama LSTM dibandingkan dengan model lain seperti Support Vector Regression (SVR) dan Random Forest terletak pada kemampuannya untuk menangkap korelasi temporal dan pola musiman dalam data deret waktu. Implikasi praktis dari penelitian ini meliputi penggunaan model untuk membantu petani dalam menentukan waktu tanam dan panen yang optimal serta bagi pemerintah daerah dalam mengatur distribusi dan pengendalian harga cabai untuk mengurangi dampak inflasi pangan dan meningkatkan ketahanan pangan. Penelitian ini membuka peluang untuk penelitian lanjutan yang dapat mengembangkan model yang lebih kompleks dan mengakomodasi faktor eksternal lainnya.
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