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All Journal POSITRON BULETIN FISIKA
Firdausi, Huriyatul
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Astronomy Energy Materials Science & Nanotechnology Medicine & Pharmacology Physics

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Analysis of Rainfall Patterns in Sulawesi Using the Empirical Orthogonal Function (EOF) Method and Composite Analysis Ariska, Melly; Setiyowati, Devi Ariska; Siahaan, Sardianto Markos; Seprina, Iin; Firdausi, Huriyatul; Taufiq, Taufiq
POSITRON Vol 15, No 2 (2025): Vol. 15 No. 2 Edition
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam, Univetsitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/positron.v15i2.91149

Abstract

topography, and ocean-land interactions, which shape weather patterns and rainfall intensity variability. This study analyzes rainfall patterns in Sulawesi Island from 1981 to 2015 using the Empirical Orthogonal Function (EOF) method and composite analysis with machine learning. The results show that the EOF method successfully identifies three primary modes of rainfall variability. EOF Mode 1 captures negative anomalies, while EOF Mode 2 and EOF Mode 3 capture both positive and negative rainfall anomalies. EOF Mode 1 is the dominant component, explaining nearly 70% of the total variance. EOF Modes 2 and 3 capture additional variations on a smaller scale, and collectively, these three modes explain 88.53% of the total rainfall variability. Meanwhile, composite analysis reveals that global factors such as ENSO and the Indian Ocean Dipole (IOD) also influence rainfall variability, impacting drought periods and extreme rainfall events. During El Niño and positive IOD phases, rainfall deficits occur, potentially leading to prolonged droughts. Conversely, during La Niña and negative IOD phases, Sulawesi experiences a significant rainfall surplus, increasing the risk of hydrometeorological disasters such as floods and landslides.
Machine Learning to Predict Climate Change in Coastal Areas of Indonesia Firdausi, Huriyatul; Ariska, Melly; Siahaan, Sardianto Marcos; Akhsan, Hamdi; Anwar, Yenny; Seprina, Iin; Taufiq, Taufiq
BULETIN FISIKA Vol. 27 No. 1 (2026): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/BF.2026.v27.i01.p05

Abstract

Indonesia's coastal regions face significant threats from climate change, including rainfall uncertainty, rising temperatures, and sea level rise. This study aims to explore the potential of machine learning algorithms in predicting climate parameter changes in the coastal areas of Minangkabau, Pesawaran, and Maritim Panjang. Daily climatological data obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) were used as the basis for model training. Three primary algorithms were tested Random Forest, XGBoost, and Long Short-Term Memory (LSTM) selected for their capability to handle complex and temporal data. The research methodology included data preprocessing, model training, cross-validation, and predictive performance evaluation using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Preliminary results show that LSTM excels in time series prediction, while XGBoost offers a good balance between speed and accuracy. These findings indicate that machine learning-based approaches have strong potential as decision-support tools for climate change mitigation and adaptation planning in Indonesia’s coastal regions.
Co-Authors Hamdi Akhsan Melly Ariska Sardianto Markos Siahaan, Sardianto Markos Seprina, Iin Setiyowati, Devi Ariska Siahaan, Sardianto Marcos Taufiq Taufiq Yenny Anwar