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Muhammad Aldila Syariz
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aldilasyariz@its.ac.id
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aldilasyariz@its.ac.id
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Geomatics Engineering's Building, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
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Geoid - Journal of Geodesy and Geomatics
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 18582281     EISSN : 24423998     DOI : https://doi.org/10.12962/geoid.v20i1
Core Subject : Science, Agriculture, Social, Engineering,
Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Engineering Environmental Science
General topics of interest include: - Geodesy and geomatics development theory - Geodesy and geomatics applications - Natural Disaster - Land and Ocean Development - Natural Resources - Environment - Science and technology in Mapping and Surveying - Earth Sciences A further issue related to geodesy and geomatics engineering such as: - Optical Remote Sensing and Radar Remote Sensing - Cadastre and 3D Modeling - Geodynamics theory and application - Geospatial - Land Surveying - Geomarine - Photogrammetry
Arjuna Subject : Ilmu Bumi dan Planet (semua kategori) - Ilmu Bumi dan Planet (Lain-Lain)
Articles 516 Documents
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Study of 3D Cadastral Mapping in the Teaching Factory Building of The Vocational School, Diponegoro University Using SLAM (Simultaneous Localization and Mapping) Method Ardyan S P Pratama; Yoga K Nugraha; Mitha A Rahmawaty
Geoid Vol. 20 No. 2 (2025)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v20i2.8769

Abstract

Cadastre is a land information system based on land parcels. The growth in the number of land parcels is influenced by the increasing conversion of land into residential areas, which in turn is driven by several factors, one of which is population growth. The demand for housing initially expanded horizontally; however, due to limited land availability, it has now shifted toward vertical development. Vertical housing types such as flats or apartments are emerging, which introduce complexity into the cadastral system due to the partitioning of internal spaces. Cadastre requires high-accuracy measurements; hence, the increase in measured areas leads to a higher workload. The SLAM (Simultaneous Localization and Mapping) method offers a breakthrough in fast and accurate measurements using laser-based technology, which can be implemented in cadastral mapping to update spatial data precisely and efficiently. This method combines the flexibility of handheld operation with high data precision by employing dense laser scanning. This study utilized the SLAM method, resulting in a polygon area processing of 0.3558 m², with anaverage center-point distance deviation of 0.0658 m, a polygon circularity ratio of -0.002, and a regression value of less than 10%. When this model is applied with a tolerance of up to 10% spatial error, it can achieve verticalmeasurements up to the 43rd floor, in accordance with the Directorate General of Taxation Circular and tested basedon ISO 19113:2011 standards.
Simulation of Tidal Inundation along the Northern Coast of Central Java (Pantura) using GISBased Analysis Robbani, Hilma Wasilah; Kartikasari, Adelia Nur Isna; Pranantya, Vanadani; Kaffa, Niswah Selmi
Geoid Vol. 20 No. 2 (2025)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v20i2.8772

Abstract

The northern coast of Java Island (locally known as Pantura) is a strategically important area, particularly in the distribution sector. However, its topographical characteristics and proximity to the Java Sea make it vulnerable to the threat of tidal inundation. Moreover, environmental factors such as sea level rise, land subsidence, and coastal abrasion further exacerbate its susceptibility to flooding. The phenomenon of tidal inundation, locally referred to as rob, occurs when seawater overflows onto low-lying coastal areas during high tides. The rob phenomenon significantly impacts the socio-economic conditions of coastal communities, disrupting daily activities and damaging critical infrastructure. This study simulates potential inundation using a uniform Highest High Water Level (HHWL) scenario of 1.2 meters to estimate flood depth and spatial extent. The modeling approach applies a consistent water surface elevation across the study area, without considering storm surge and hydrodynamics, resulting in generalized inundation patterns. The methodology follows the Technical Guidelines for Disaster Risk Assessment issued by Indonesia’s National Disaster Management Agency (BNPB) and integrates various spatial datasets, including land cover data from Sentinel Land Cover by ESRI, topographic data from the Shuttle Radar Topography Mission (SRTM), Digital Elevation Model (DEM), and maximum tidal height data processed using the Admiralty method. The analysis shows that, assuming a Highest High Water Level of 1.2 meters, Kendal Regency, Brebes Regency, and Semarang City are the most affected areas in terms of both flood depth and extent. The inundated areas are estimated at 3,744.91 hectares in Kendal Regency, 2,880.58 hectares in Brebes Regency, and 513.17 hectares in Semarang City. This situation could become more severe in the event of storm surge, extreme weather, or climate anomalies if timely and effective mitigation measures are not implemented. These findings are expected to provide a strong foundation for policymakers to formulate targeted, data-driven, and sustainable mitigation strategies to protect communities and infrastructure along Java’s northern coastal region.
Development of Three.js-based 3D Scenes with Seamless Visualisation of Gaussian Splatting and Transformation to Global Coordinates Azfa Ahmad Dzulvikar; Harintaka; Ikhrom
Geoid Vol. 20 No. 2 (2025)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v20i2.8775

Abstract

Existing scholarly literature on the Gaussian Splatting algorithm has predominantly concentrated on improving the rendering and reconstruction of three-dimensional objects, as well as exploring its applications in various academic disciplines, such as medicine, robotics, and mapping, while being limited to local coordinate systems. This study describes the development of a 3D scene modelled using the Gaussian Splatting algorithm, featuring accurate distance and position geometry based on three.js. The developed 3D scene was then evaluated with precise position and distance coordinates in the field and compared to the established SfM-MVS (Structure from Motion-Multi View Stereo) algorithm. The findings demonstrate that the proposed development successfully generated three.js-based 3D scenes with global coordinate compatibility, utilising the Gaussian Splatting algorithm, achieving the same level of position and distance accuracy as the SfM-MVS algorithm, with a 95% confidence level using a T-test. This research concludes that the developed approach is successful and can be further expanded for various scientific fields that require accurate position and distance information using the Gaussian Splatting Algorithm.
Evaluation of Google Earth Engine Embedding Dataset for Remote Sensing Image Classification Wijaya, Calvin; Harintaka
Geoid Vol. 21 No. 1 (2026)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v21i1.8151

Abstract

Google Earth Engine (GEE) has emerged as one of the most powerful cloud-based platforms for processing and analyzing remote sensing imagery. By integrating vast Earth observation archives with scalable computational resources, it provides an accessible environment for researchers, practitioners, and decision-makers. In 2025, Google’s AlphaEarth Foundation introduced a novel embedding model trained on diverse Earth observation datasets available on the GEE server. This model, generated from annual time-series imagery and offered in an analysis-ready format, enables general-purpose applications such as classification, clustering, regression and change detection. Despite its potential, the performance and capabilities of this embedding model remain largely underexplored. This study evaluates the effectiveness of the embedding datasets in GEE for supervised classification method. Comparative experiments were conducted against widely used remote sensing imagery, including Sentinel-2 and Landsat 9 imagery, using multiple algorithms such as K-Neural Network (KNN), Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART), and Object-Based Image Analysis (OBIA). In addition, a case study was carried out to examine the use of embedding datasets for mangrove classification. Validation using overall accuracy demonstrates that embedding datasets achieve superior results compared to conventional imagery. Classification using the embedding dataset achieved an average overall accuracy of 94%, outperforming Landsat 9 (83.1%) and Sentinel-2 (82.5%). Moreover, the embedding dataset produced a classification pattern similar to OBIA, even without the need for image segmentation. The findings highlight the potential of embedding datasets to enhance classification accuracy and broaden the scope of remote sensing applications, suggesting new opportunities for leveraging advanced machine learning representations in geospatial analysis.
Spatio-Temporal Analysis of Carbon Monoxide (CO) Distribution According to Deforestation in West Kalimantan, Indonesia Ramadhania, Nurya; Murdawati; Devika Rahma Damayanti Yusuf; Widodo Eko Prasetyo
Geoid Vol. 21 No. 1 (2026)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v21i1.7840

Abstract

Carbon monoxide (CO) is a harmful air pollutant primarily produced through biomass burning, including forest fires and deforestation activities. West Kalimantan Province, which has undergone massive land cover change, is a crucial area for examining the link between deforestation and the increase in atmospheric CO concentrations. This study aims to analyze the spatial and temporal relationship between CO distribution and deforestation throughout 2024. CO data were obtained from Sentinel-5P satellite imagery, while deforestation detection was carried out using the Normalized Burn Ratio (NBR) and the Normalized Difference Vegetation Index (NDVI), derived from Sentinel-2A imagery. The NBR index was used to detect areas affected by fire or land conversion, while the NDVI reflects vegetation health conditions. The analysis results show that regions with increased NBR and decreased NDVI tend to have high CO concentrations. The Pearson correlation between NBR and CO indicates a very strong positive relationship, while the correlation between NDVI and CO shows a weak to moderate negative relationship. However, the dominance of cloud cover in most Sentinel-2A imagery in West Kalimantan potentially affects the quality and representativeness of the resulting vegetation data. This study highlights that deforestation significantly contributes to the decline in air quality, demonstrating that satellite-based remote sensing is an effective tool for air pollution monitoring and supporting environmental mitigation policies.
Prediction of Erosion Hazard Level in Tripe Jaya District Using the Universal Soil Loss Equation (USLE) Method Murdawati; Yusuf, Devika Rahma Damayanti; Nurya Ramadhania; Nadya Novi Rahmadana
Geoid Vol. 21 No. 1 (2026)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v21i1.7919

Abstract

The Tripe Jaya Subdistrict in Gayo Lues Regency features a highly vulnerable landscape where steep slopes, intense rainfall, and limited vegetation cover collectively contribute to severe erosion risk. Erosion in this region threatens soil fertility, agricultural productivity, slope stability, transportation infrastructure, and riverbank integrity. This study aims to predict and map erosion hazard levels using the Universal Soil Loss Equation (USLE) integrated with Geographic Information System (GIS) analysis, based on rainfall, soil type, slope, and land cover data. The results classify the study area into five erosion hazard categories: very light (2,909.09 ha), light (20,669.38 ha), moderate (10,880.66 ha), heavy (432.99 ha), and very heavy (6,922.76 ha), with the most critical zones concentrated in steep and intensively utilized areas. These findings emphasize the substantial erosion risk in Tripe Jaya and provide an essential reference for mitigation planning, land-use regulation, and infrastructure protection, particularly for road segments adjacent to riverbanks.

Page 52 of 52 | Total Record : 516

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