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All Journal Communications in Science and Technology Journal of Geospatial Science and Technology
Ruli Andaru, Ruli
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Earth & Planetary Sciences Engineering

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Deteksi Deformasi Vertikal Permukaan Jembatan Mulya Agung Tahun 2021 – 2022 Menggunakan Data Mobile Laser Scanner Wulan Ratna Mayangsari; Andaru, Ruli
Journal of Geospatial Science and Technology Vol 3 No 1 (2025): Journal of Geospatial Science and Technology
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jgst.v3i1.10253

Abstract

The Terbanggi Besar - Pematang Panggang - Kayu Agung Toll Road (TBPPKA) is a part of the Trans-Sumatra Toll Road (JTTS) that has been operational since November 2019, necessitating road maintenance. Routine maintenance is carried out on all toll road assets, including the Mulya Agung Underpass Bridge on the TBPPKA Toll Road at KM 240+822. This study aims to analyze vertical deformations on the bridge's surface using Mobile Laser Scanner (MLS) as a step in bridge monitoring and maintenance efforts. MLS technology was chosen for its capability in fast and accurate scanning, making it effective for multi-epoch measurements. Data generated from the MLS measurements come in the form of a point cloud, which still needs to be registered using the Iterative Closest Point (ICP) method to ensure the data is in the same coordinate system. Based on the research results, the RMS accuracy value in the registration process is 0.021 meters out of a total of 596,963 points sampled. To determine the presence of vertical deformation, a significant test was conducted using the Student's t-test. Based on the significant test results with a 95% confidence level, the calculated t-value (2.705) was found to be greater than the tabulated t-value (2.010), indicating a significant change in elevation. The maximum elevation increase is 9.1 cm on the south side of the bridge, while the minimum decrease is -3.4 cm on the north side of the bridge. Vertical deformations occur at the end of the bridge connected to the road, which is attributed to repairs involving asphalt addition. The MLS method has proven to be effective in detecting vertical deformations on the bridge with a level of detection of 2 cm.
Leveraging machine learning and open accessed remote sensing data for precise rainfall forecasting Cahyono, Bambang Kun; Ummah, Muhammad Hidayatul; Andaru, Ruli; Andika, Neil; Pamungkas, Adjie; Handayani, Hepi Hapsari; Atmodiwirjo, Paramita; Nathan, Rory
Communications in Science and Technology Vol 10 No 1 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.1.2025.1638

Abstract

Rainfall forecasts are essential for human activities enabling communities to anticipate any impacts. Rainfall events correlate with other natural and hydro-meteorological phenomena, which can be used in modeling and prediction. This study used daily CHIRPS for the Gajahwong watershed in Yogyakarta, Indonesia as the precipitation data. It also used Sea Surface Temperature, Land Surface Temperature (Day and Night), Minimum and Maximum Temperatures, Solar Radiation, Wind Speed (U and V components), Cloud Pressure (Top and Base), and Cloud Height (Top and Base) as the parameters. Further, data processing was performed by means of the Google Earth Engine (GEE) platform. Machine learning methods, including Support Vector Regression, Gradient Boosting Regression, Random Forest, and Deep Neural Networks, were applied. The correlation analysis revealed that only the Wind Speed V-component showed significant correlation with rainfall, other seven parameters showed moderate and four showed weak ones. Meanwhile, accuracy assessments indicated that Support Vector Regression had the most accurate predictions accompanied by Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), R2, and Coefficient Correlation (CC) at 1.366, 0.947, 1.866, 0.948 and 0.982 respectively. This study demonstrated that utilizing openly accessible atmospheric datasets processed through the GEE could yield reliable rainfall predictions, facilitating informed decisions on a wide scale. The methodology is adaptable and can be reproduced for any comparable research or operational purposes.
Co-Authors Adjie Pamungkas Andika, Neil Hepi Hapsari Handayani, Hepi Hapsari Nathan, Rory Paramita Atmodiwirjo Ummah, Muhammad Hidayatul Wulan Ratna Mayangsari