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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi JUITA : Jurnal Informatika
Laily Nissa Atul Mualifah
IPB University
Computer Science & IT Mathematics

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Performance Evaluation of ARIMA and GRU Models for Forecasting Chili Price in East Jawa Windi Pangesti; Nabila Syukri; Khairil Anwar Notodiputro; Yenni Angraini; Laily Nissa Atul Mualifah
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26445

Abstract

Time series forecasting plays a crucial role in predicting future conditions based on historical data, particularly in the food sector, which is highly susceptible to price fluctuations. This study compares two approaches: the conventional ARIMA method and the deep learning method GRU, to forecast the price of red chillies in East Java. East Java was chosen because it is the largest national producer of chilies, thus the stability of its prices has a broad impact. The research results indicate that the GRU model outperforms the ARIMA model with a MAPE value of 19.80% compared to a MAPE of 27.63% for the latter. The benefit of this research is to contribute to the literature on developing agricultural commodity price forecasting models as a basis for enhancing food security policies and stabilizing commodity prices, particularly in East Java Province, Indonesia
Performance Analysis of ARIMA, LSTM, and Hybrid ARIMA-LSTM in Forecasting the Composite Stock Price Index Andi Illa Erviani Nensi; Mahda Al Maida; Khairil Anwar Notodiputro; Yenni Angraini; Laily Nissa Atul Mualifah
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33379

Abstract

This study evaluates the performance of ARIMA, LSTM, and hybrid ARIMA-LSTM models in predicting the closing and opening prices of the Indonesia Stock Exchange Composite Index (IHSG) over various periods (2007-2020, 2007-2022, and 2007-2024). For the LSTM model, a lag of 1 was chosen based on MAPE analysis, showing strong dependence on the previous day’s price. Different learning rates (0.01, 0.001, 0.0001) and batch sizes (16, 32) were tested on various network architectures. Results indicate that while ARIMA effectively captures linear patterns, LSTM consistently outperforms with lower MAPE values—2.27% for closing and 2.02% for opening prices—especially with a simple (1-50-1) architecture and a learning rate of 0.001. The hybrid ARIMA(0,1,1)-LSTM(1-50-1) model showed competitive results, achieving MAPE of 2.00% for closing and 1.74% for opening prices using batch size 16. However, its success depends on ARIMA’s ability to model linear components. Key findings emphasize LSTM’s dominance in accuracy, the importance of parameter tuning, and the effectiveness of simple network structures. The hybrid approach holds promise when linear and nonlinear data components are clearly separable. This research offers methodological insights for optimizing stock price prediction models and practical guidance for model configuration, contributing to the advancement of financial market forecasting.
Co-Authors Andi Illa Erviani Nensi Mahda Al Maida Nabila Syukri Windi Pangesti Yenni Angraini