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All Journal JUKI : Jurnal Komputer dan Informatika
Marzuki Sinambela
State College of Meteorology, Climatology, and Geophysics (STMKG), Tangerang
Computer Science & IT

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The DEVELOPMENT OF AN INTEGRATED OPEN-SOURCE GEOPHYSICAL MODELING PLATFORM INCORPORATING DEEP LEARNING FOR MULTI-METHOD SUBSURFACE INTERPRETATION Riko Riko; Marzuki Sinambela; Muchamad Rizqy Nugraha; Hapsoro Agung Nugroho
JUKI : Jurnal Komputer dan Informatika Vol. 8 No. 1 (2026): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v8i1.2674

Abstract

Geophysical interpretation commonly relies on multiple independent software packages, making data processing, visualization, and interpretation inefficient for educational and research purposes. This study presents the development of an integrated open-source geophysical modeling platform that combines gravity, magnetic, and Very Low Frequency (VLF) electromagnetic methods within a single application. The proposed software incorporates deterministic forward modeling together with a Convolutional Neural Network (CNN)-based deep learning module to support rapid subsurface interpretation. The platform was developed using Python and integrates numerical computation, interactive visualization, and AI-assisted inversion into a unified graphical user interface. For VLF processing, Fraser and Karous–Hjelt filters are implemented to enhance conductive anomaly detection, while gravity and magnetic modeling employ prism-based forward calculations with Root Mean Square Error (RMSE) evaluation. In addition, Model Performance and Efficiency Index (MPEI) and Model Resolution Index (MRI) are incorporated to quantitatively assess model quality and computational efficiency. The resulting software provides an integrated workflow from data preprocessing to visualization and model evaluation, reducing interpretation time while improving usability for geophysical education and preliminary subsurface investigations. The proposed platform demonstrates that integrating conventional geophysical modeling with modern deep learning techniques offers a flexible, transparent, and extensible framework suitable for academic research and practical applications.
Cross-Regime Transfer Learning for Dekadal Rainfall Prediction in Indonesia Using a Transformer Encoder Tonny Wahyu Aji; Marzuki Sinambela; Hapsoro Agung Nugroho; Edward Trihadi; Tonni Limbong
JUKI : Jurnal Komputer dan Informatika Vol. 8 No. 1 (2026): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v8i1.2684

Abstract

. Rainfall prediction across regions with different rainfall regimes remains challenging because a model trained in one climatic domain can fail when applied to another domain. This study evaluates transfer learning for dekadal rainfall prediction using a Transformer encoder trained on 10 rainfall sites in West Java and adapted to three target regions representing different rainfall patterns: Kupang (monsoonal), Padang (equatorial), and Ambon (local). Three transfer strategies were evaluated: zero-shot inference, head-only fine-tuning, and staged unfreezing. The source model used 22 meteorological and climate-index features, site embedding, a 36-step input window, and H1-H6 forecasting horizons. Zero-shot transfer produced negative R2 on all targets, indicating substantial domain shift. Staged unfreezing consistently improved the zero-shot model, reducing RMSE by 49.83% in Kupang, 9.13% in Padang, and 24.76% in Ambon. On the raw millimeter scale, the final transferred model obtained RMSE values of 69.30 mm, 168.36 mm, and 92.91 mm for Kupang, Padang, and Ambon, respectively. Compared with simple baselines, the transferred model outperformed persistence on all targets but outperformed climatology only in Ambon. These results indicate that transfer learning is useful for adapting deep rainfall models to new regions, but reliable deployment still requires target-domain fine-tuning, raw-scale evaluation, and comparison against simple climatological baselines.
Co-Authors Edward Trihadi Hapsoro Agung Nugroho Muchamad Rizqy Nugraha Riko Riko TONNI LIMBONG Tonny Wahyu Aji