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All Journal Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Journal of Climate Change Society
Samiaji, Budi Iman
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Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Physics Social Sciences Other

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The Influence of Indonesian Throughflow on the Dynamics of Climate and Waters in the Indonesian Region Sulistiyono, Andi; Samiaji, Budi Iman
Journal of Climate Change Society Vol. 2 No. 2 (2024)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jccs/Vol2-iss2/33

Abstract

Journal references provide an overview of the current between the Pacific and Indian Oceans, referred to as the Indonesian Throughflow (Arlindo), which is part of the global thermohaline circulation that impacts the dynamics of the atmosphere and oceans by changing the circulation and characteristics of the sea water masses of the two oceans. Determining the features and impact of Arlindo on water dynamics and climate phenomena in the Indonesian region particularly in the area where this current crosses is the aim of this study. The research uses the Systematic Literature Review (SLR) method in which the literature data has been carried out Quality Assessment in accordance with the study topics that will be raised. The reference results revealed that the Indonesian Throughflow can affect nutrient distribution and climate variability through changes in surface temperature and sea water density. The characteristic properties of the Indonesian Throughflow change seasonally and accompany climatic phenomena such as ENSO, Dipole Mode and Madden-Julian Oscillation. The Indonesian Throughflow is an important component that significantly influences climate conditions and variability in the Indonesian region. Therefore, it is necessary to understand the implications of the Indonesian Throughflow on water dynamics, especially in the current crossing areas
Prediction of Tropical Cyclone Trajectory and Intensity Using a Particle Motion Based Machine Learning Framework in the Southern Indian Samiaji, Budi Iman; Yulkifli, Yulkifli; Yohandri, Yohandri; Yendri Sudiar, Nofi; Supari, Supari
Prisma Sains : Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Vol. 14 No. 2: April 2026
Publisher : Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/j-ps.v14i2.19982

Abstract

Tropical cyclones in the Southern Indian Ocean pose severe threats to coastal infrastructure and socio-economic stability, yet predicting their recurving trajectories and intensity remains a significant meteorological challenge. This study evaluates the performance of a particle-motion-based machine learning framework, utilizing the Trackpy library, to forecast cyclone behavior. Leveraging historical data from 2018 to 2025 (JTWC and IBTrACS), the model treats cyclones as physical particles with temporal inertia, employing a multi-lag feature to capture movement momentum. Evaluation using a dataset of 115 cyclones (78:22 train/test ratio) reveals that the Trackpy framework achieves high spatial precision, with Mean Squared Error (MSE) values of 0.1728 for latitude (±33.3 km) and 1.0250 for longitude (±53.2 km). While the intensity prediction yielded a higher MSE of 47.7544 (approximately 6.9-knot deviation), the model successfully captured major strengthening and weakening phases across prominent cyclones, including TC Wallace and TC Neville. These findings demonstrate that integrating temporal inertia is highly effective for maintaining trajectory consistency, establishing Trackpy as a robust architectural foundation for operational forecasting. Further optimization via hybrid models and additional meteorological variables is recommended to enhance intensity accuracy.
Co-Authors sulistiyono, andi Supari Supari, Supari Yendri Sudiar, Nofi Yohandri Bow Yulkifli Yulkifli