Year: 2024
Type: article
Abstract: 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.
DOI: 10.35314/wnpm3846
Authors: Sunoto, Mangapul Siahaan
Institution: International University of Batam
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4405693072