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All Journal JOIV : International Journal on Informatics Visualization Jurnal Geofisika
Dahlila, Dea
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Computer Science & IT Earth & Planetary Sciences Energy Physics

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Analisis Tensor Fase dan Pemodelan 2D Data Magnetotellurik Dahlila, Dea; Irawati, Selvi Misnia; Paembonan, Andri Yadi
Jurnal Geofisika Vol 22 No 1 (2024): Jurnal Geofisika
Publisher : Himpunan Ahli Geofisika Indonesia (HAGI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36435/jgf.v22i1.563

Abstract

Salah satu metode geofisika untuk mengetahui bawah permukaan bumi adalah metode magnetotellurik yang memanfaatkan sumber alami berupa medan magnet bumi dari medan listrik dan variasi medan magnet. Namun, data yang diperoleh dari lapangan masih dipengaruhi oleh noise atau distorsi yang akan menutupi dimensionalitas sebenarnya dari struktur bawah permukaan, sehingga dapat mengakibatkan kesalahan dalam melakukan interpretasi. Oleh karena itu perlu dilakukan analisis tensor fase untuk mengidentifikasi struktur bawah permukaan yang tidak dipengaruhi distorsi galvanik. Berdasarkan hasil yang diperoleh daerah penelitian memiliki dimensionalitas 2D dengan arah geo-electrical strike N1750E berarah Utara Barat Laut hingga Tenggara. Hasil inversi 2D menunjukkan bahwa resistivitas rendah 1-10 Ωm diidentifikasi sebagai zona alterasi atau clay cap yang berupa batuan sedimen yang berada kedalaman ~1.5 km dimana zona alterasi ini memiliki kemenerusan ke arah timur laut. Pada resistivitas 200-500 Ωm diidentifikasikan sebagai basal berpori. Pada resistivitas 50-70 Ωm diidentifikasikan sebagai riolit yang berasosiasi dengan fluida geotermal.
Multivariate Time Series Forecasting using Hybrid Vector Autoregressive and Neural Network for Coupled Roll-Sway-Yaw Motions Prediction Suhermi, Novri; Suhartono, -; Rahayu, Santi Puteri; Ali, Baharuddin; Dahlila, Dea; Aisy, Rahida Rihhadatul
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3077

Abstract

There are six types of motion referred to as the six degrees of freedom, which define the motion of a ship. For a ship to remain stable, it must be in a symmetrical position. Therefore, a ship's stability can be determined based on its motion. Ship motions can be analyzed either in an uncoupled system or a coupled system. One of the coupled motion systems that is often studied is the roll-sway-yaw motion. In this study, we apply the Hybrid Vector Autoregressive–Neural Network (VAR-NN) model to build a multivariate time series model for predicting the roll-sway-yaw motions of a prototype ship. The Hybrid VAR-NN is a data analysis technique that integrates the linear capabilities of the VAR model with the nonlinear capabilities of the NN model to capture both linear and nonlinear trends simultaneously. The dataset for this study was generated from waves in a prototype ship experiment and divided into in-sample and out-of-sample data. The model was trained using the in-sample data, and predictions were made on the out-of-sample data using the trained model. The forecast results of the VAR-NN model were compared with those from the pure VAR and pure NN models. Model selection was based on out-of-sample performance criteria, with the Root Mean Square Error (RMSE) employed as the prediction performance metric. According to the experimental results, the Hybrid VAR-NN model outperformed the other models, demonstrating its ability to improve the prediction performance of the pure models through its hybrid approach.
Co-Authors - Suhartono Aisy, Rahida Rihhadatul Andri Yadi Paembonan Baharuddin Ali, Baharuddin Irawati, Selvi Misnia Santi Puteri Rahayu Suhermi, Novri