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All Journal Journal of Mathematics, Computation and Statistics (JMATHCOS)
Siti Alfiatur Rohmaniah
Universitas Islam Darul 'Ulum
Mathematics

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Implementation of Long Short-Term Memory for Forecasting the Indonesian Rupiah Exchange Rate against the Saudi Arabian Riyal Lisna Fauziyah; Siti Amiroch; Siti Alfiatur Rohmaniah
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/4b0ct819

Abstract

Exchange rates are a key indicator of a country’s economic condition and are inherently volatile and difficult to predict. Indonesian Rupiah exchange rate against Saudi Arabian Riyal (SAR) exhibits complex time series characteristics influenced by various macroeconomic factors. This study aims to forecast the Rupiah–SAR exchange rate using the Long Short-Term Memory (LSTM) method. The dataset consists of secondary data obtained from Bank Indonesia, covering the period from January 2, 2015, to February 27, 2026, with a total of 2,725 observations. The research methodology includes data preprocessing, transformation using a sliding window approach, data splitting, and LSTM modeling with hyperparameter tuning. The best performing model from the research results shows that achieved with a 90:10 train–test split, using 32 LSTM units, a learning rate of 0.001, 100 epochs, a dropout rate of 0.1, and a batch size of 32, yielding a Mean Absolute Percentage Error (MAPE) of 0.240376%, which falls into the highly accurate category. The 30-day forecasting results show a gradual downward trend in the exchange rate. These findings suggest that the LSTM model not only provides high predictive accuracy but also effectively captures the underlying nonlinear dynamics and temporal dependencies of exchange rate movements. Furthermore, the results reflect broader economic interactions, indicating that the model outputs can be utilized as a practical reference for financial planning and economic decision-making.
Comparison of Backpropagation Neural Network and Long Short-Term Memory for Rainfall Prediction in Lamongan Regency Andri Hardiyansyah; Siti Amiroch; Siti Alfiatur Rohmaniah
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/n2b8sn30

Abstract

Rainfall is one of the important factors in the agricultural sector and water resource management especially in Lamongan Regency, which has a seasonal rainfall pattern. Variability and uncertainty of rainfall can affect agricultural activities as well as water availability for irrigation needs and water resource management. As an effort to minimise crop failure, an accurate prediction method is needed to support future planning. This study aims to predict rainfall using Backpropagation Neural Network and Long Short-Term Memory (LSTM) methods, as well as to compare the performance of both methods to determine the most optimal method in rainfall prediction to support planting time planning and water management. The data used are historical rainfall data, particularly from areas known as rice production centres in Lamongan Regency. The data underwent preprocessing stages, including data cleaning, normalisation, and time series data formation. The models were trained using three data splitting scenarios, namely 70:30, 80:20, and 90:10, and were then evaluated using the Root Mean Square Error (RMSE). The best model was determined based on the smallest RMSE value and subsequently used to predict rainfall for the next year. The results show that the best model was obtained using the LSTM method, with RMSE values of 24.70 mm for Lamongan, 26.74 mm for Kembangbahu, 44.77 mm for Tikung, 33.12 mm for Sugio, and 33.67 mm for Sukodadi. Therefore, the LSTM method is considered more optimal than the Backpropagation method in predicting rainfall in Lamongan Regency. The effective rice planting period occurs from May to July, as rainfall during this period is relatively sufficient and stable to support crop growth. In addition, planting activities can be carried out two to three times in a year.
Co-Authors Andri Hardiyansyah Lisna Fauziyah Siti Amiroch