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All Journal Building of Informatics, Technology and Science
Rizki Mugi Setya Adi
Institut Teknologi Telkom Purwokerto, Banyumas
Computer Science & IT

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Prediksi Harga Komoditas Pangan Menggunakan Algoritma Long Short-Term Memory (LSTM) Rizki Mugi Setya Adi; Sudianto Sudianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2229

Abstract

Food is a basic need for human survival. The existence of food is influenced by production and selling prices. The problem that exists is that food producers lose out with the dynamics of selling prices. In addition, the low selling price is not commensurate with the production costs that have been spent, especially for food producers in agricultural commodities, namely local farmers. Local farmers lose money because they do not know the price of commodities when selling their agricultural products. In addition, the game of intermediaries causes local farmers to sell their crops at low prices. So from the existing problems, it is necessary to predict commodity prices to help farmers determine the commodity prices before selling their agricultural products to the market. This study aims to predict the price of food commodities, especially in Banyumas, so that local farmers can find the price of commodities before they are sold to the market. The Deep Learning method used is Long Short-Term Memory (LSTM), which can remember a collection of information that has been stored for a long time with time series data. The results obtained, the model can predict food commodity prices. Meanwhile, the prediction model with epoch 50 shows the lowest Root Mean Squared Error (RMSE) with a value of 79.19%
Co-Authors Sudianto