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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Jurnal Matematika: MANTIK Jurnal Matematika Sains dan Teknologi BAREKENG: Jurnal Ilmu Matematika dan Terapan JCES (Journal of Character Education Society) JTP - Jurnal Teknologi Pendidikan Jurnal Varian JAMBURA JOURNAL OF PROBABILITY AND STATISTICS International Journal of Global Operations Research Parameter: Journal of Statistics VARIANSI: Journal of Statistics and Its Application on Teaching and Research Jurnal Statistika dan Sains Data Jurnal Sains dan Kesehatan
Hasanah, Siti Hadijah
Universitas Terbuka, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Journal : knowledge engineering and data science

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Comparative Analysis of Deep Learning Algorithms for Predicting ENSO Based on Non-sequential Sampling Procedure Algorithms Faulina, Ria; Hasanah, Siti Hadijah; Nuramaliyah, Nuramaliyah; Fitriana, Ika Nur Laily
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

The El Niño–Southern Oscillation (ENSO) is a major climate phenomenon that significantly influences global weather patterns, particularly rainfall and temperature variability across different regions. Accurate ENSO forecasting is therefore essential to support disaster risk mitigation and strategic decision-making in climate-sensitive sectors such as agriculture, fisheries, and water resource management. This study investigates the performance of deep learning approaches for ENSO prediction using a non-sequential sampling procedure on historical climate data. Three models are comparatively evaluated: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM architecture. The results demonstrate that the hybrid CNN–LSTM model outperforms the standalone CNN and LSTM models in predictive accuracy and robustness. Specifically, the proposed model achieved the lowest Mean Absolute Error (MAE) of 13.97 and Root Mean Square Error (RMSE) of 15.76 across multiple test samples. These findings indicate that the integration of convolution-based feature extraction and sequential memory learning effectively captures complex ENSO temporal patterns. The proposed approach offers a reliable computational framework for climate forecasting and may contribute to improved anticipatory planning in climate-sensitive decision-making contexts.
Co-Authors Abdul Rahim Arif, Zainul Chairunnisa, Madiha Dzakiyyah Dela Juliarsih Rahman Dewi Juliah Ratnaningsih Eka Julianti Epin Nur Cahya Erlyne Nadhilah Widyaningrum Faulina, Ria Fitriana, Ika Nur Laily Hendrayani, Hendrayani Ika Purnamasari Isnurani Kertanah, Kertanah Komuna, Avelyn Pingkan M. Fathurahman Maulana, Muhamad Riyan Mujahidin, Ilham Nina Valentika Ningsih, Eva Lestari Nuramaliyah, Nuramaliyah Nurdiana, Dian Nurmayanti, Wiwit Pura Pangruruk , Thesya Atarezcha Pangruruk Putri, Desi Febriani Rif'an, Muhammad Siringoringo, Meiliyani Sri Enny Triwidiastuti Sri Maulidia Permatasari Wiwit Pura Nurmayanti Yuniarti, Desi