Pengembangan Model RNN untuk Prediksi Produksi Daging Sapi dalam Perencanaan Pembangunan Nasional - Yulianingsih Yulianingsih; Tri Yani Akhirina; Za’imatun Niswati (Faktor Exacta, 2022)
Data is an important component because it will support in policies / decisions making, serve as control tools to prevent error from occuring and support transparent, accountable and participative governance. This study examines the prediction of beef production and product consumption with the Long Short Term Memory (RNN LSTM) Recurrent Neural Network approach. Using statistical data on beef production and consumption of products per capita per week from BPS. The data used were 12 records for each data source. LSTM contains information outside the normal flow of recurrent network in the gate cell. Cell makes decisions about what should be stored and when to permit reading, writing and deletion, through open and closed gates. The gate conveys information based on the strength that enters into it and will be filtered to be the weight of the gate itself. These weights are the same as the input and hidden unit weights that are adjusted through learning process on the recurrent network. The results of research carried out by building prediction models of beef production and product consumption get the best results using data for 3 years with RMSE 32121.297 for beef production and 0.001 for product consumption.