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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 7 Documents
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Search results for , issue "Vol. 21 No. 1 (2026)" : 7 Documents clear
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.
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.

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