PREDIKSI HARGA BERAS DI SUMBA TIMUR MENGGUNAKAN ALGORITMA NEURAL NETWORK - Renol Bulu Manggal; Arini Aha Pekuwali; Raynesta Mikaela Indri Malo (Universitas Nusa Cendana, 2026)
Fluctuations in rice prices in East Sumba Regency are an important issue that directly affects farmers, traders, and consumers. Unstable price changes are influenced by weather conditions, supply availability, distribution, and market dynamics. Therefore, a prediction method is needed that can provide accurate estimates of rice prices as a basis for decision making. This study aims to predict the price of medium rice in East Sumba Regency using the Neural Network algorithm, specifically Long Short-Term Memory (LSTM), which is effective in modeling time series data. The data used are monthly rice price data for the period January 2021 to December 2025 obtained from Perum BULOG Waingapu Branch Office, with data processing and analysis carried out after all 2025 data became available. The research stages include data collection, data preprocessing, normalization using Min-Max Scaling, time series dataset formation, division of training and testing data, LSTM model training, and model performance evaluation. The evaluation was carried out using the Root Mean Square Error (RMSE) metric. The results show that the LSTM model is able to predict rice prices with an RMSE value of 360.91 Rp/Kg or around 3.35% of the average rice price. This value indicates that the prediction error of the model is relatively small, so the model can be said to have good prediction performance. Therefore, the developed LSTM model is considered feasible to be used as a tool for predicting rice prices and is expected to help farmers and traders in planning sales and become a consideration for the local government in maintaining rice price stability in East Sumba Regency.