COMPARING FORECASTS OF AGRICULTURAL SECTOR EXPORT VALUES USING SARIMA AND LONG SHORT-TERM MEMORY MODELS

  • Aleytha Ilahnugrah Kurnadipare Department of Statistic and Data Science, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia https://orcid.org/0009-0007-8596-056X
  • Sri Amaliya Department of Statistic and Data Science, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia
  • Khairil Anwar Notodiputro Department of Statistic and Data Science, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia https://orcid.org/0000-0003-2892-4689
  • Yenni Angraini Department of Statistic and Data Science, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia https://orcid.org/0000-0003-3186-2378
  • Laily Nissa Atul Mualifah Department of Statistic and Data Science, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia https://orcid.org/0000-0002-5722-8431
DOI: https://doi.org/10.30598/barekengvol19iss1pp385-396
Keywords: Comparation, Forecasting, Neural Network, Root Mean Squared Error, Time Series Analysis

Abstract

Indonesia's agricultural sector plays a crucial role in the national economy, with significant export potential and supporting the livelihoods of the majority of the population. As part of the government's vision to make Indonesia the world's food barn by 2045, increasing the volume and value of agricultural product exports is a primary focus, making export value forecasting essential for supporting strategic decision-making. Sequential data analysis is an important approach in analyzing data collected over a specific period. This study aims to compare two popular methods in forecasting the export value of the agricultural sector, namely the Seasonal AutoRegressive Integrated Moving Average (SARIMA) model and the Long Short-Term Memory (LSTM) model. Monthly agricultural export data from West Java Province from January 2013 to February 2024 were used as the dataset. The best SARIMA model generated was (1,1,1)(0,1,1)12, while the optimal parameters for the LSTM model were neuron: 50, dropout rate: 0.3, number of layers: 2, activation function: relu, epochs: 500, batch size: 64, optimizer: Adam, and learning rate: 0.01. Evaluation was performed using the Root Mean Squared Error (RMSE) method to measure the accuracy of both models in forecasting the export value of the agricultural sector. The results showed that the LSTM model outperformed the SARIMA model, with a lower RMSE value (SARIMA: 4182.133 and LSTM: 1939.02). This research provides valuable insights for decision-makers in planning agricultural sector export strategies in the future. With this comparison, it is expected to provide better guidance in selecting forecasting methods suitable for the characteristics of the data.

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Published
2025-01-13
How to Cite
[1]
A. I. Kurnadipare, S. Amaliya, K. A. Notodiputro, Y. Angraini, and L. N. A. Mualifah, “COMPARING FORECASTS OF AGRICULTURAL SECTOR EXPORT VALUES USING SARIMA AND LONG SHORT-TERM MEMORY MODELS”, BAREKENG: J. Math. & App., vol. 19, no. 1, pp. 385-396, Jan. 2025.
Issue
Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2025 Aleytha Ilahnugrah Kurnadipare, Sri Amaliya, Khairil Anwar Notodiputro, Yenni Angraini, Laily Nissa Atul Mualifah

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