Implementation of Long Short-Term Memory for Chili Price Prediction in East Java Province
DOI:
https://doi.org/10.14421/jiska.2025.10.1.33-47Keywords:
LSTM, prediksi, harga cabai, bahan panganAbstract
Di Indonesia, bahan pangan memiliki harga yang berubah-ubah atau tidak stabil seiring berjalannya waktu dan salah satu komoditas bahan pangan yang sering mengalami ketidakstabilan harga yaitu dari komoditas cabai. Untuk itu, langkah prediksi harga cabai dapat ditempuh untuk memperkirakan harga selanjutnya sehingga dapat diambil strategi yang tepat, khususnya melalui proses pembelajaran mesin dengan memanfaatkan jaringan saraf tiruan LSTM. Pada penelitian ini, dilakukan eksperimen pengujian mengenai hyperparameter serta struktur dari jaringan LSTM itu sendiri yang digunakan untuk prediksi pada data harga dua jenis cabai di Provinsi Jawa Timur yaitu cabai merah dan cabai rawit. Hasil dari penelitian ini menunjukkan konfigurasi hyperparameter dan struktur jaringan terbaik sama untuk tiap data harga jenis cabai yang diuji. Data harga cabai merah menghasilkan nilai rata-rata RMSE terbaik yaitu 1751,690, sedangkan data harga cabai rawit menghasilkan nilai rata-rata RMSE terbaik yaitu 1888,741.
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