Prediksi Rerata Harga Beras Tingkat Grosir Indonesia dengan Long Short Term Memory - fadil indra sanjaya; Dadang Heksaputra (Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP, 2020)
Fluctuations in prices of food staples, especially rice price which is uncontrolled, have an impact on losses for producers and consumers. To be able to bridge these problems requires the right decision making. Prediction is one element that can be used in supporting the right decision making. Predictions in decision making are based on existing data in the present and the past so that they can be used to describe conditions that are in line with the objectives to be achieved. With accurate rice price predictions, it is expected that decision makers will be able to decide on good policies or take preventive actions to minimize losses. In this study examines the prediction of rice prices at the Indonesian wholesale level using the Recurrent Neural Network Long Short Term Memory (RNN LSTM) approach. In this study, the data used is the average rice price at the Indonesian Wholesale / Wholesale Trade Year 2010-2020 obtained from the Indonesian statistical center. The results obtained from this study indicated that LSTM method can be used to predict the price of rice at the Indonesian wholesale level quite well.