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Muhammad Aldila Syariz
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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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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; Bakri, Bambang
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.
Comparative Analysis Of Landsat 8 And Landsat 9 Satellite Image Data In Surface Temperature Estimation, NDVI and NDBI Using Goggle Earth Engine Purwoko, Dana; Handoko, Eko Yuli; Sopaheluwakan, Ardhasena
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.v20i2.8016

Abstract

The rapid urbanization in major cities like Jakarta significantly alters land cover, which in turn impacts environmental thermal conditions and ecological quality. This research aims to analyze the spatial and temporal dynamics of Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) in DKI Jakarta during the 2023–2024 period using combined data from the Landsat 8 and 9 satellites. Cross-validation analysis shows a very high level of consistency between the sensors, validating the use of combined data for multi-temporal studies. Analysis methods include land cover classification, linear regression analysis, and temporal change analysis. The results indicate a clear Urban Heat Island (UHI) phenomenon, characterized by a strong positive correlation between LST and NDBI (R > 0.67) and a negative correlation between LST and NDVI (R ≈ -0.5). Temporal analysis indicates that thermal conditions in 2024 were generally lower than in 2023, and localized dynamics of land cover change were also identified. These findings affirm the fundamental relationship between land cover composition and the urban microclimate, and underscore the importance of vegetation in mitigating high temperatures in urban environment.
Estimation of 2021 M7.3 Flores Sea Earthquake Displacement Derived from Static GNSS Observation Maulida, Putra; Herawati, Yola Asis; Rizkiya, Putra; Rizky, Sari; Kurniawan, Akbar; Azza Laksono, Safanata; Budisusanto, Yanto
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.v20i2.8500

Abstract

On 12 December 2021, a Mw 7.3 strike-slip earthquake ruptured a previously unmapped fault in the Flores Sea, later identified as the Kalaotoa fault. The event damaged 345 buildings and displaced nearly 3,900 residents, highlighting the seismic hazard in the Sunda–Banda arc transition zone. In this study, we analyzed static GNSS data from the Indonesian Continuously Operating Reference System (InaCORS) to estimate coseismic displacements. Daily coordinate solutions, corrected for satellite orbit, ionospheric, and tropospheric errors, were processed to extract the coseismic offsets during the event. Results show horizontal displacements of up to 3.0 cm at CFLT, 2.2 cm at CMRE, and 1.9 cm at CUKA, with vertical motions reaching ~1.3 cm uplift at CUKA and ~0.9 cm subsidence at CMRE, which suggests that the earthquake not only incorporates the strike-slip movement but also the dipping movement. Stations near the epicenter moved northwestward, while northern stations moved southeastward, consistent with a right-lateral strike-slip mechanism. To validate the observations, we employed a half-space elastic dislocation model based on centroid moment tensor solutions for fault geometry. The model reproduced the general displacement patterns but showed systematic discrepancies, including overestimation of horizontal offsets by nearly a factor of two at near-epicenter stations (CFLT, CMRE, CUKA, CLWB) and underestimation of vertical motions by up to 2–3 cm. The misfit corresponds to an RMSE of ~1 cm for horizontal and ~3 cm for vertical displacement. These results indicate that a single homogeneous slip model oversimplifies the rupture, suggesting the need for more complex fault segmentation or slip inversion. Overall, this study demonstrates the capability of GNSS to capture coseismic deformation robustly and emphasizes its importance for refining earthquake source models and improving seismic hazard assessment in tectonically complex regions such as eastern Indonesia.
Bibliometric Mapping and Systematic Review of the Analytical Hierarchy Process (AHP) in Groundwater Potential Assessment Last Decade (2015-2024): Global Trend, Model Combination, Influence Factor, and Validation Samsul Rizal; T Yan W M Iskandarsyah; Hendarmawan
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.v21i1.8765

Abstract

The analytical hierarchy process (AHP) model has been deemed by researchers with various backgrounds as an alternative solution due to the rapid, flexible, cost-effective, and high accuracy of groundwater potential assessment based on expert judgment, especially in complex geological settings. This paper specifically reviews research trends, key influence factors, model techniques, and validation process in AHP for groundwater availability assessment using bibliometric mapping and systematic literature review (SLR). The result reveals that AHP has been consistently utilized over the past decade (2015-2024), commonly combined, and integrated with statistical and machine learning models to enhance accuracy. Thirty-eight influence factors were observed and categorized into 5 groups (geology, hydrogeology, geomorphology, hydrology, and socio-environmental). The five most influential factors with significant normalized weight values are lithology, geomorphology, drainage density, rainfall, and lineament density, respectively. Well yield and groundwater level are most validation data using receiver operating characteristic (ROC) and area under curve (AUC) approach to evaluate the model. Considering hydrogeological insight, multicollinearity, validation, and sensitivity analysis are crucial to reduce bias and enhance better understanding of site-specific factors.
Land Cover Mapping and Prediction Using Cellular Automata and Markov Chain (Case Study: Depok City, Indonesia) Muhammad Arya Pradipta; Megivareza Putri Hanansyah; Filsa Bioresita; Noorlaila Hayati; Lalu Muhamad Jaelani; Bangun Muljo Sukojo, Husnul Hidayat
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.v21i1.8766

Abstract

Depok City, a satellite city of Jakarta, is experiencing massive urbanization due to Jakarta's role as an economic hub, leading to significant land-use changes. This study analyses land cover in Depok City annually from 2017 to 2024 across five categories: Built-up Area, Vegetation, Agricultural Land, Bare Land, and Water Body. This process utilizes the Extreme Gradient Boosting algorithm applied to Sentinel-2 Level-1C satellite imagery for the specified period. Subsequently, we predict Depok City's land cover conditions for the year 2042 using a Cellular Automata-Markov Chain simulation. This simulation incorporates historical land cover maps, which were generated previously, along with driving factors such as distance from main roads and distance from health and educational facilities. The year 2042 was chosen to coincide with the expiration of Peraturan Daerah Nomor 9 Tahun 2022, law product concerning the Depok City Spatial Plan for 2022-2042. The final outputs of this research are land cover maps of Depok City for each year from 2017 to 2024, as well as a predicted land cover map for Depok City in 2042. The study found that from 2017 to 2024, the built-up area and vegetation land cover category showed an increasing trend in extent, while the remaining land cover categories decreased. Prediction model of year 2042 shows predicted expansion of Built-Up land and Vegetation land cover categories, while other land cover categories predicted to decrease.
Analysis of Seasonal Patterns of Atmospheric Water Vapour and Rainfall in East Kalimantan and North Kalimantan Using the Lomb–Scargle Periodogram Method Agus Ariyanto; Eko Yuli Handoko; Putra Maulida
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.v21i1.8767

Abstract

This study examines the seasonal trends of Precipitable Water Vapour (PWV) obtained from GNSS data (2021–2023) and decadal rainfall data from BMKG (2001–2020) in East and North Kalimantan, employing the Lomb–Scargle Periodogram (LSP) method. The findings indicate that PWV is mostly influenced by an equatorial semi-annual cycle (about 0.5 years), while precipitation typically adheres to a monsoonal annual pattern (around 1 year). The correlation between PWV and precipitation is not wholly linear, exhibiting significant local variability in coastal areas. The LSP approach is effective in identifying dominant frequencies, albeit it exhibits reduced sensitivity to non-stationary fluctuations in atmospheric signals.
Identification of the Best Semivariogram Model for the Blending of In-Situ and ERA5-Land Air Temperature Data Using the Kriging with External Drift Technique Fatchiyah; Eko Yuli Handoko; Ardhasena Sopaheluwakan; Robi Muharsyah
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.v21i1.8768

Abstract

Accurate air temperature monitoring is essential for understanding climate dynamics and microclimates, particularly in regions with diverse topography. The limited number of observation stations often results in data that do not fully represent actual conditions. To address this gap, combining in-situ measurements with ERA5-Land reanalysis presents a promising alternative, although ERA5-Land may still exhibit biases in mountainous or urban areas. This study applies Kriging with External Drift (KED) to improve temperature estimation, focusing on identifying the most suitable semivariogram model. Daily and monthly analyses were conducted, with performance evaluated using RMSE, MAE, and MSE. The results indicate that the Spherical model consistently performs best for average and maximum temperatures, while the Exponential model provides better estimates for minimum temperature at the daily scale, and the Linear model at the monthly scale. These findings demonstrate that KED can significantly enhance temperature estimation in areas with sparse observations, while also highlighting the most reliable semivariogram models for different temperature parameters.

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