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All Journal JURNAL PENDIDIKAN TAMBUSAI Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Journal of Applied Mechanical Engineering and Renewable Energy (JAMERE) Journal of Climate Change Society
Yendri Sudiar, Nofi
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Analisis Struktur Bawah Permukaan Segmen Sianok Menggunakan Data Geomagnetik Melalui Pemodelan Inversi 3D Wulandari, Mia; Amir, Harman; Dwiridal, Letmi; Yendri Sudiar, Nofi
Jurnal Pendidikan Tambusai Vol. 8 No. 3 (2024)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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Abstract

Segmen Sianok merupakan salah satu segmentasi sesar di sepanjang Pulau Sumatera yang memiliki aktivitas vulkanik dan tektonik yang terbilang tinggi. Aktivitas vulkanik dan tektonik yang terjadi dapat diidentifikasi berdasarkan struktur bawah permukaannya menggunakan metode geomagnetik. Tujuan dilakukannya penelitian ini adalah untuk mendapatkan informasi mengenai persebaran anomali magnetik dan mengenai struktur bawah permukaan pada daerah yang dilewati Segmen Sianok. Penelitian ini termasuk ke dalam jenis penelitian deskriptif dengan menggunakan metode pemodelan inversi 3D. Data pada penelitian ini menggunakan data sekunder berupa data anomali magnetik yang sudah melalui koreksi variasi harian dan IGRF dan dibagi menjadi beberapa partisi. Hasil penelitian menunjukkan anomali magnetik secara keseluruhan berada pada rentang -1200 hingga 1500 nT. Anomali magnetik blok “1” hingga blok “3” berturut-turut adalah -500 nT – 1500 nT, 400 nT – 1000 nT dan -750 nT – 250 nT. Hasil pemodelan inversi 3D menunjukkan bahwa wilayah penelitian didominasi oleh batuan vulkanik berupa andesit Marapi, andesit Singgalang-Tandikat, andesit Kaldera Maninjau dan tuf batuapung.
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 Aditio, Rama Akmam Akmam Akmam Alwi Nofriandi Audia, Washilla Erianto, Fajar Darma Utama Fadila, Husnul Hamdi Hamdi Hamdi Harman Amir Letmi Dwiridal Mesi, Mesi Mutiara Novichri, Viona Nanda, Popi Nuryadi, Ari Pakhrur Razi Pratiwi Gholil Prasetiyani Samiaji, Budi Iman Sri Aulia Noprianti, Nadia Supari Supari, Supari Syafriani Syafriani, Syafriani Wulandari, Mia Yohandri Bow Yulkifli Yulkifli Yulkifli Zulhendra Zulhendra