PREDIKSI HARGA PANGAN DI PASAR TRADISIONAL KOTA SURABAYA DENGAN METODE LSTM
Article Sidebar
Main Article Content
Abstract
Long Short-Term Memory is the development of an artificial neural network that has the ability to overcome the vanishing gradient problem, and makes it possible to remember long-term information, and understand temporal patterns in time series data, so that LSTM has good performance in predicting food prices [1]. In Indonesia, especially in Surabaya, food prices are often unstable. Fluctuations in food prices can be caused by many factors such as weather, growing season and production. Under these conditions, this research was conducted to predict future food prices. The purpose of this study is to apply the LSTM method in predicting food prices so that it can provide maximum results and can be used by the community in making good decisions. In this study the dataset used included 5 types of food, namely rice, beef, chicken eggs, granulated sugar, and cooking oil. The dataset was obtained from the website of the National Strategic Food Price Information Center (PIHPS Nasional, https://www.bi.go.id/hargapangan). Predictive results are evaluated with RMSE and MAE. RMSE and MAE values of 5 types of food, namely rice 32 and 27, beef 229 and 125, chicken eggs 319 and 213, cooking oil 424 and 215, granulated sugar 30 and 18.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This work is licensed under a Jurnal Komunikasi Creative Commons Attribution-ShareAlike 4.0 International License.
References
Zahara, Soffa, Sugianto, dan M. Bahril Ilmiddafiq. “Prediksi Indeks Harga Konsumen Menggunakan Metode Long Short Term Memory (LSTM) Berbasis Cloud Computing”. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), Vol. 3, No. 3, (2019), h. 357 -63.
Hasibuan Lailan Sahrina, dan Yanda Novialdi. “Prediksi Harga Minyak Goreng Curah Dan Kemasan Menggunakan Algoritme Long Short-Term Memory (LSTM)”. Jurnal Ilmu Komputer Dan Agri-Informatika, Vol. 9, No. 2, (Bogor, 2022), h. 149-57.
Putu Harry Gunawan, Devi Munandar, dan Anis Zainia Farabiba, “Long Short-Term Memory Approach for Predicting Air Temperature in Indonesia”, Jurnal Online Informatika, Vol. 5, No. 2, (Desember, 2020), h. 161-168.
Mitchell Ryu Sopany, Dyah Erny Herwindiati dan Janson Hendryli, “Prediksi Kelembapan Tanah Pada Tingkat Kecamatan di Wilayah Bogor Dengan Metode CNN LSTM”, Computatio: Jurnal of Computer Science and Information Systems, Vol. 6, No. 1, (Juni, 2022), h. 1-9.
Adi, Rizki, dan Sudianto Sudianto. “Prediksi Harga Komoditas Pangan Menggunakan Algoritma Long Short-Term Memory (LSTM)”. Building of Informatics, Technology and Science (BITS), Vol. 4, No. 2, (2022), h. 1137−1145.
Jiseong Noh et al. "Gated recurrent unit with genetic algorithm for product demand forecasting in supply chain management.", MDPI Mathematics Journal, Vol. 8, No. 4, (2020), h. 21.