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All Journal Jurnal Mantik Journal of Intelligent Decision Support System (IDSS)
Aimar Akbar, Aminnur
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Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Application of machine learning for short-term climate prediction in Indonesia Gunawan, Gunawan; Andriani, Wresti; Aimar Akbar, Aminnur
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5215

Abstract

This study explores the Application of Machine Learning for Short-Term Climate Prediction in Indonesia, focusing on enhancing forecast accuracy through advanced computational models. The primary objective was to develop and validate Random Forest and Support Vector Machine (SVM) models to predict short-term climate conditions accurately across ten major Indonesian cities. Employing a quantitative approach, the study utilized experimental design, rigorous data analysis, and model validation using historical weather data from April 2024 provided by the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). The results indicate that both Random Forest and SVM significantly outperform traditional climate prediction models, with Random Forest achieving an average accuracy of 87.5% and SVM 85.2%. These findings underscore the potential of machine learning to revolutionize short-term climate predictions in regions with complex meteorological dynamics like Indonesia, offering substantial implications for disaster preparedness, agricultural planning, and urban management. Future research can expand upon these models by incorporating real-time data and exploring deep learning techniques to enhance predictive reliability further
Application of deep neural network with stacked denoising autoencoder for ECG signal classification Gunawan, Gunawan; Aimar Akbar, Aminnur; Andriani, Wresti
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.247

Abstract

Applying deep neural networks with stacked denoising autoencoders (SDAEs) for ECG signal classification presents a promising approach for improving the accuracy of arrhythmia diagnosis. This study aims to develop a robust model that enhances the classification of ECG signals by effectively denoising the input data and extracting rich feature representations. The research employs a method involving data preprocessing, feature extraction using SDAEs, and classification with a deep neural network (DNN) validated on the MIT-BIH Arrhythmia Database. The results demonstrate that the proposed model achieves an impressive accuracy of 98.91%, significantly outperforming traditional machine learning methods. The implications of this research are substantial, offering a reliable and automated tool for arrhythmia diagnosis that can be utilized in clinical settings to improve patient care. The study highlights the model's potential for real-time clinical application, although further validation on more extensive and diverse datasets is necessary to confirm its generalizability and robustness. This research contributes to the field by integrating advanced SDAEs with deep learning, paving the way for more accurate and efficient ECG signal classification systems
Co-Authors Andriani, Wresti Gunawan Gunawan Gunawan