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All Journal Jambura Journal of Mathematics Jurnal Matematika UNAND
Ilma, Meisyatul
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Computer Science & IT Mathematics

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Comparative Analysis of ARIMA and LSTM for Forecasting Maximum Wind Speed in Kupang City, East Nusa Tenggara Magfirrah, Indah; Ilma, Meisyatul; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.25834

Abstract

This study compares the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models for predicting maximum wind speed based on accuracy measured by Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Based on the results of the research, the LSTM model is better than the ARIMA model in predicting maximum wind speed in Kupang City, East Nusa Tenggara Province. The best LSTM model has hyperparameters of 200 epochs; batch size of 32; learning rate of 0,001; and 8 neurons. Based on the evaluation results of predicted data against actual data, the MAPE value of the LSTM model is 19,40%. The benefit of this research is that it can contribute to the literature on the development of wind utilization as a basis for building power plants on small islands as a renewable resource, particularly in Kupang City, East Nusa Tenggara.
Performance of Prediction Interval Estimators based on Random Forest Models with Correlated Predictors Ilma, Meisyatul; Sartono, Bagus; Wijayanto, Hari
Jurnal Matematika UNAND Vol. 14 No. 4 (2025)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.14.4.320-332.2025

Abstract

Uncertainty in prediction results is a crucial aspect that needs to be taken into account in regression modeling, especially when there is a high correlation between explanatory variables. This study aims to evaluate the performance of three prediction interval formation approaches, namely Out-of-Bag Prediction Interval (OOB-PI), Quan tile Regression Forest (QRF), and Split Conformal Prediction (SC), in Random Forest modeling. The evaluation was conducted through a simulation study with a variety of data structures, including the level of correlation between variables, the shape of the mean function, and the type of error distribution. Further validation was conducted using data from the National Socio-Economic Survey (SUSENAS) of West Java Province in 2023. The results show that increasing the correlation between explanatory variables can improve the efficiency and accuracy of prediction interval estimation. Overall, OOB-PI showed the most balanced performance compared to the other two methods, with a prediction coverage rate close to 90% and a narrower interval width than QRF and SC. This finding indicates that OOB-PI is an adaptive and efficient approach for various data structures, including socioeconomic data with highly correlated predictors.
Co-Authors Bagus Sartono Hari Wijayanto Magfirrah, Indah Mualifah, Laily Nissa Atul Yenni Angraini