Multiple Liniar Regression and Forecasting Models for Consumer's Rice Price in Indonesia
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Extensive shrinkage of rice rate fields in Indonesia areas such as Tangerang, Serang, Bekasi, Karawang, Purwakarta, Bandung and Bogor as a regional center for rice national
Penerapan metode campuran ARIMA dan Quantile Regression dilakukan untuk mengetahui prediksi harga beras pada beberapa periode ke depan dan mengetahui faktor atau
Perubahan kenaikan dan penurunan harga beras menunjukkan nilai yang tidak sig- nifikan pada harga produsen periode sebelumnya t-1 sehingga dapat disimpulkan bahwa ke- naikan
find the best model, first we evaluate the stationary of the data by using time series plot, Autocorrelation Function (ACF), and Unit root Test.. Then the Time Series model was
An increase in the amount of production and the harvested area will also increase the amount of harvest produced; therefore, to find out which areas have the potential to become
This is expected to contribute in 1 verifying whether the hybrid models such as ARIMAX-LSTM, VECM- LSTM have better forecasting performance than the single econometric and machine
METODE PENELITIAN Tahapan dalam penelitian pengembangan model prediksi data time series untuk harga bawang putih di Indonesia menggunakan model optimasi RNN-LSTM dengan deep learning
This study aims to carry out multivariate rice price forecasting in DKI Jakarta by involving rice prices, weather, economic, and health factors using the Gated Recurrent Unit GRU
