cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Muhammad Aldila Syariz
Contact Email
aldilasyariz@its.ac.id
Phone
+6282131726693
Journal Mail Official
aldilasyariz@its.ac.id
Editorial Address
Geomatics Engineering's Building, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Location
Kota surabaya,
Jawa timur
INDONESIA
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 504 Documents
 47   48   49   50   51 
Land Cover Projection of Jember Irrigation Area Using MOLUSCE QGIS Kartikasari, Adelia Nur Isna; Prasojo, Sri Irawan Laras; Robbani, Hilma Wasilah; 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.v20i2.8071

Abstract

Jember Regency has the third largest agricultural area in East Java Province. However, the agricultural area has decreased due to the expansion of built-up areas in line with population growth. This indicates the need for special attention to controlling the expansion of built-up land in Jember Regency. This study focuses on predicting agricultural land loss and the increase in built-up land in Jember Regency. It examines land cover changes in the regency from 2017 to 2021. Sentinel-2 imagery was used to obtain land cover data for Jember Regency in 2017 and 2021. The 2017 and 2021 land cover maps will serve as reference maps to determine the 2025 land cover using the MOLUSCE plugin in QGIS. The obtained 2025 land cover map will be used to validate the model's accuracy by comparing it with the actual 2025 land cover using Kappa Accuracy. This model's Kappa Accuracy is 91%. The validated model will then be used to predict land cover for 2045. The analysis indicates a predicted reduction in agricultural area of 5.675 hectares and a predicted increase in built-up area in irrigated areas of 6.348 hectares during the 2025–2045 period. Over the next 20 years, irrigation areas under the authority of the regency are predicted to experience the highest growth in built-up land, at 46.1%. This is followed by areas under provincial authority, which are predicted to grow by 34.6%, and areas under central authority, which are predicted to grow by 110% of the total agricultural area in Jember Regency. These findings are important for local governments and stakeholders in land management and urban planning. They also contribute to the monitoring of agricultural land use and the development of effective policy strategies.
Study of 3D Cadastral Mapping in the Teaching Factory Building of The Vocational School, Diponegoro University Using SLAM (Simultaneous Localization and Mapping) Method pratama, Ardyan Satria Putra; Nugraha, Yoga K; Rahmawaty, Mitha A
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.v20i2.2611

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 an average 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 vertical measurements up to the 43rd floor, in accordance with the Directorate General of Taxation Circular and tested based on ISO 19113:2011 standards.
Simulation of Tidal Inundation along the Northern Coast of Central Java (Pantura) Using GIS-Based Analysis Robbani, Hilma; 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.v20i2.7807

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—which causes shoreline retreat—further exacerbate the region’s susceptibility to flooding. The rob phenomenon significantly impacts the socio-economic conditions of coastal communities, disrupting daily activities and damaging critical infrastructure such as residential housing and road networks. This study aims to simulate the impact of tidal flooding in terms of inundation depth and spatial extent, using the assumption of the Highest High Water Level (HHWL). The simulation results are intended to serve as an initial reference for the development of coastal flood mitigation strategies. 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.
Detection of River Change in Modeling Flood Vulnerability using Support Vector Machine (SVM) Methods in Tallo River Makassar City Izzaty, Atika; Aprian, Syahra Dewi; Wijayanti, Regita Faridatunisa; Iffaty, Athiya; Bakri, Bambang; Karamma, Riswal
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.v20i2.7536

Abstract

The transformation of river morphology and the rising frequency of flooding in urban environments have emerged as increasingly concerning environmental challenges, particularly in Makassar City. The Tallo River, one of the primary waterways traversing the city, exhibits notable dynamic changes driven by both natural processes. In the contemporary era, flooding stands as one of the most recurrent natural disasters, occurring unpredictably and posing serious risks, especially in major metropolitan areas. Such events frequently disrupt daily activities, leading to traffic congestion and obstructing ground transportation. Residential zones situated near riverbanks are particularly vulnerable to its impacts. Moreover, climate change exacerbates these conditions by contributing to increasing environmental unpredictability and need through a monitoring. The purpose of this research is to analyze river morphology changes and assess flood susceptibility in the Tallo River, Makassar City, using Support Vector Machine (SVM) classification methods. Approximately, there are 20% of the area experienced significant changes during 2018 in Tallo River. As water discharge continues to increase, the volume of water mass also rises accordingly. To detect the spatial distribution of flood vulnerability along the Tallo River, which flows through Makassar City, this study utilizes Land Use and Land Cover (LULC) data from 2017 and 2024. These datasets were classified using the Random Forest model, achieving accuracies of 0.89 and 0.87, respectively values that meet the standards for land use change accuracy. Flood vulnerability is also influenced by low elevation values, particularly areas below 0 meters, which are classified as wetland zones. In the Tallo River area, which is part of the Jeneberang Watershed, the dominant class is moderate flood vulnerability, covering approximately 138.48 hectares. Remote sensing technology combined with machine learning approaches especially supervised classification techniques widely used for both binary and multivariate classification tasks, demonstrating high accuracy in detecting and classifying flood vulnerability.

Page 51 of 51 | Total Record : 504

 47   48   49   50   51 

Filter by Year

2007 2025


Reset
Filter By Issues
All Issue Vol. 20 No. 2 (2025) Vol. 20 No. 1 (2025) Vol. 19 No. 3 (2024) Vol. 19 No. 2 (2024) Vol. 19 No. 1 (2023) Vol. 18 No. 2 (2023) Vol. 18 No. 1 (2022) Vol. 17 No. 2 (2022) Vol. 17 No. 1 (2021) Vol. 16 No. 2 (2021) Vol. 16 No. 1 (2020) Vol. 15 No. 2 (2020) Vol. 15 No. 1 (2019) Vol. 14 No. 2 (2019) Vol. 14 No. 1 (2018) Vol. 13 No. 2 (2018) Vol. 13 No. 1 (2017) Vol. 12 No. 2 (2017) Vol. 12 No. 1 (2016) Vol. 11 No. 2 (2016) Vol. 11 No. 1 (2015) Vol. 10 No. 2 (2015) Vol. 10 No. 1 (2014) Vol. 9 No. 2 (2014) Vol. 9 No. 1 (2013) Vol. 8 No. 2 (2013) Vol. 8 No. 1 (2012) Vol. 7 No. 2 (2012) Vol. 7 No. 1 (2011) Vol. 6 No. 2 (2011) Vol. 6 No. 1 (2010) Vol. 5 No. 2 (2010) Vol. 5 No. 1 (2009) Vol. 4 No. 2 (2009) Vol. 4 No. 1 (2008) Vol. 3 No. 2 (2008) Vol. 2 No. 2 (2007) More Issue