Prediksi Harga Minyak Goreng Curah dan Kemasan Menggunakan Algoritme Long Short-Term Memory (LSTM) - Lailan Sahrina Hasibuan; Yanda Novialdi (Departemen Ilmu Komputer, Institut Pertanian Bogor, 2022)
A very significant increase in the price of basic necessities will affect the economy of the Indonesian people, such as lowering purchasing power. Based on the monitoring of the Strategic Food Price Information Center from November 2021 to August 2022, cooking oil is a necessities that experienced a very significant increase of price in Indonesia. This increase was spread evenly across 34 provinces of Indonesia, including the province of West Java. This significant increase can be prevented by taking preventive actions before, if this increase has been predicted. Deep Learning is a supervised learning method that is widely used today because of its reliability in solving various problems in the field of data mining. Deep learning can predict future cooking oil prices using time series data. This study develops a model to predict the price of cooking oil in bulk and packaged form using deep learning that specifically manages time series data, namely Long Short Term Memory (LSTM). Based on the NRMSE evaluation metric, the model built is able to recognize the price fluctuation of cooking oil in the form of bulk and packaging. The NRMSE value of the LSTM model in the training process is 0.019 for bulk cooking oil data training, and 0.037 for packaged cooking oil data.