Analysis of Rice Yield Prediction with Mlpregressor and Long Short-Term Memory Models - Sunoto; Mangapul Siahaan (P3M Politeknik Negeri Bengkalis, 2025)
This research aims to analyse and compare the accuracy of rice productivity prediction using Multi-Layer Perceptron Regressor (MLPRegressor) and Long Short-Term Memory (LSTM) models. The data used comes from the Badan Pusat Statistik (BPS) for the period 2018-2023, covering rice productivity from 34 provinces in Indonesia. The study employed six different architectural models for each model, with training data using the 2018-2020 period and testing data for 2021-2023. The results show that the LSTM model with 2-42-42-42-1 architecture achieved the highest accuracy rate of 94.12% with MSE 0.00305660, while the MLPRegressor model with 2-22-1 architecture achieved 91.18% accuracy with MSE 0.00471975. These results indicate that LSTM performs slightly better in predicting rice productivity, which can be used as a reference for agricultural planning and food policy in Indonesia.