Rice Price Prediction with Long Short-Term Memory (LSTM) Neural Network

  • Rahmat Hidayat Universitas Putra Bangsa
  • Irawan Wibisonya Universitas Putra Bangsa
Keywords: Prediction, Variability, Long Short Term Memory, Artificial Neural Networks

Abstract

Rice is a crucial commodity, especially in countries that rely on rice as a staple food. Fluctuations in rice prices can impact inflation, purchasing power, and economic stability. Therefore, an effective method for forecasting rice prices is essential for timely decision-making. This study aims to develop a rice price forecasting model by incorporating weather variability. Using Long Short-Term Memory (LSTM) neural networks, the model is expected to provide accurate predictions and guide decision-making in rice trading. LSTM is effective in analyzing time-series data. In this study, LSTM was used to examine the relationship between weather variability, crop yields, and land area with rice prices. Daily data from 2015 to 2023 were collected to build a model capable of predicting future rice prices. The results showed that the LSTM model achieved a Root Mean Squared Error (RMSE) of 0.054, indicating high prediction accuracy. This model allows stakeholders, including farmers, traders, and government officials, to better understand future rice price movements. This, in turn, helps them implement more effective strategies in managing rice supply and stabilizing prices.

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References

R. M. S. Adi and S. Sudianto, “Prediksi Harga Komoditas Pangan Menggunakan Algoritma Long Short-Term Memory (LSTM),” Building of Informatics, Technology and Science (BITS), vol. 4, no. 2, pp. 1137–1145, 2022, doi: 10.47065/bits.v4i2.2229.

BBC News Indonesia, “Harga beras naik ‘tertinggi dalam sejarah’ - ‘Ini sangat tidak masuk akal karena kita negara agraris,’” Https://Www.Bbc.Com/Indonesia/Articles/C72Ggeq2139O, p. https://www.bbc.com/indonesia/articles/c72ggeq2139, 2024.

H. Agustian and Syafrial, “Penerapan Metode A* untuk Penentuan Jalur Terpendek Dalam Pengiriman Barang Berbasis Mobile,” TEKNOIS, vol. 13, no. 1, pp. 101–109, 2023.

A. Santoso, A. Irma Purnamasari, and Irfan Ali, “Prediksi Harga Beras Menggunakan Metode Recurrent Neural Network Dan Long Short-Term Memory,” PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer, vol. 11, no. 1, pp. 128–136, 2024, doi: 10.30656/prosisko.v11i1.7921.

M. A. Sholeh, “PERBANDINGAN MODEL LSTM DAN GRU UNTUK MEMPREDIKSI HARGA MINYAK GORENG DI INDONESIA,” EDUSAINTEK: Jurnal Pendidikan, Sains dan Teknologi, vol. 9, no. 3, pp. 800–811, Sep. 2022, doi: 10.47668/edusaintek.v9i3.593.

I. R. Harahap, M. Z. Siambaton, and H. Santoso, “IMPLEMENTASI METODE REGRESI LINEAR SEDERHANA UNTUK PREDIKSI HARGA BERAS DI KOTA MEDAN,” SEMNASTEK UISU, 2023.

S. Karbala, I. A. Program, S. Komputerisasi, A. D3, and F. T. Industri, “MEMPREDIKSI HARGA BERAS ECERAN MENGGUNAKAN ALGORITMA REGRESI LINIER,” 2023.

L. Harianti Hasibuan, S. Musthofa, P. Studi Matematika, and U. Imam Bonjol Padang, “Penerapan Metode Regresi Linear Sederhana Untuk Prediksi Harga Beras di Kota Padang,” 2022.

P. R. Linear, U. Prediksi, H. Beras, D. Indonesia, V. Arinal, and M. Azhari, “Penerapan Regresi Linear Untuk Prediksi Harga Beras Di Indonesia,” Jurnal Sains dan Teknologi, vol. 5, no. 1, p. |pp, 2023, doi: 10.55338/saintek.v5i1.1417.

A. Rahma Anandyani, D. Krisnawati Alfiki Astutik, P. Studi Statistika Fakultas Sains dan Teknologi Universitas PGRI Adi Buana Jl Dukuh Menanggal XII, and J. Timur, “PREDIKSI RATA-RATA HARGA BERAS YANG DIJUAL OLEH PEDAGANG BESAR (GROSIR) MENGGUNAKAN METODE ARIMA BOX JENKINS,” 2021.

W. Ngestisari, B. Susanto, and T. Mahatma, “Perbandingan Metode ARIMA dan Jaringan Syaraf Tiruan untuk Peramalan Harga Beras INFORMASI ARTIKEL ABSTRAK,” Indonesian Journal of Data and Science (IJODAS), vol. 1, no. 3, pp. 96–107, 2020.

S. Taliki, I. Colanus, R. Drajana, and A. Bode, “SUPPORT VECTOR MACHINE BERBASIS CHI SQUARE UNTUK PREDIKSI HARGA BERAS ECER KABUPATEN POHUWATO,” 2022. [Online]. Available: http://jurnal.goretanpena.com/index.php/JSSR

Y. Nur Sukmaningtyas, S. Zahara, and M. Fatchiyatur Rohmah, “PEMODELAN PREDIKSI HARGA BERAS MEDIUM WILAYAH JAWA TIMUR MENGGUNAKAN STACKED LSTM,” SUBMIT, vol. 3, no. 2, pp. 20–24, 2023, [Online]. Available: https://siskaperbapo.jatimprov.go.id/

A. Basit, “IMPLEMENTASI ALGORITMA NAIVE BAYES UNTUK MEMPREDIKSI HASIL PANEN PADI,” Jurnal Teknik Informatika Kaputama (JTIK), vol. 4, no. 2, 2020.

N. Nafi’iyah and P. A. Wulandari, “Prediksi Harga Beras Berdasarkan Kualitas Beras dengan Metode Long Short Term Memory,” Jurnal Inovtek Polbeng, vol. 7, no. 2, pp. 278–288, 2022.

L. Sahrina Hasibuan and Y. Novialdi, “Prediction of Bulk and Packaged Cooking Oil Prices Using the Long Short-Term Memory (LSTM) Algorithm,” Ilmu Komputer Agri-Informatika, 2022, [Online]. Available: https://jurnal.ipb.ac.id/index.php/jika

R. Firdaus, H. Mukhtar, T. Informatika, I. Komputer, and U. Muhammadiyah Riau, “Prediksi Indeks Harga Produsen Pertanian Karet Di Indonesia Menggunakan Metode LSTM,” FASILKOM, vol. 13, no. 1, 2023, [Online]. Available: https://www.bappebti.go.id/.

N. Awalloedin, “Prediksi Harga Beras Super dan Medium Menggunakan LSTM dan BILSTM (Moving Average Smoothing),” Jurnal Ilmu Komputer, vol. 16, no. 1, p. 32, 2023, doi: 10.24843/jik.2023.v16.i01.p04.

J. Cahyani, S. Mujahidin, and T. P. Fiqar, “Implementasi Metode Long Short Term Memory (LSTM) untuk Memprediksi Harga Bahan Pokok Nasional,” Jurnal Sistem dan Teknologi Informasi (JustIN), vol. 11, no. 2, p. 346, 2023, doi: 10.26418/justin.v11i2.57395.

S. Sen, D. Sugiarto, and A. Rochman, “Prediksi Harga Beras Menggunakan Metode Multilayer Perceptron (MLP) dan Long Short Term Memory (LSTM),” Ultimatics : Jurnal Teknik Informatika, vol. 12, no. 1, pp. 35–41, 2020, doi: 10.31937/ti.v12i1.1572.

F. I. Sanjaya and D. Heksaputra, “Prediksi Rerata Harga Beras Tingkat Grosir Indonesia dengan Long Short Term Memory,” JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 7, no. 2, pp. 163–174, 2020, doi: 10.35957/jatisi.v7i2.388.

Published
2024-10-23
How to Cite
Rahmat Hidayat, & Irawan Wibisonya. (2024). Rice Price Prediction with Long Short-Term Memory (LSTM) Neural Network. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 8(5), 658 - 664. https://doi.org/10.29207/resti.v8i5.6041
Section
Information Technology Articles