cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
Gedung Kuliah Bersama (GKB) Lt 3 Fakultas Teknik Universitas Diponegoro- Departemen Teknik Geodesi Jl Prof Soedarto SH, Tembalang. Semarang
Location
Kota semarang,
Jawa tengah
INDONESIA
Elipsoida : Jurnal Geodesi dan Geomatika
Published by Universitas Diponegoro
ISSN : -     EISSN : 26219883     DOI : -
Core Subject : Education,
Education
ELIPSOIDA merupakan Jurnal yang memuat hasil studi dan penelitian bidang geodesi dan geomatika. Jurnal ini diterbitkan dua kali dalam setahun pada bulan Juni dan November oleh Departemen Teknik Geodesi Universitas Diponegoro. Jurnal ini bersifat terbuak ke semua ilmuwan, peneliti, mahasiswa dan cendekiawan lainnya yang ingin mempublikasihan hasil studi atau penelitiannya. Tujuan dari Jurnal ini adalah untuk menyediakan paltform bagi para ilmuwan dan akademisi untuk berbagi, bertukar dan mendiskusikan berbagai isu dan perkembangan ilmu Geodesi dan Geomatika. Jurnal ini menerima makalah dari universitas terkemuka di seluruh Indonesia, universitas luar negeri, lembaga pemerintah dan swasta lainnya. Semua naskah harus disiapakan dalam bahasa inggris atau bahasa indonesia dan harus melalui proses peer-review.Topik yang dapat disajikan pada jurnal ini meliputi : Pengembangan dan aplikasi ilmu geodesi dan geomatika, survey pemetaan dan GNSS, pertanahan, sistem informasi geografis (SIG), Penginderaan Jauh, Fotogrametri, Hidrografi, dan Kebencanaan.
Arjuna Subject : -
Articles 141 Documents
 11   12   13   14   15 
EKSTRAKSI OTOMATIS TAPAK BANGUNAN (BUILDING FOOTPRINT) PADA ORTOFOTO MENGGUNAKAN SEGMENT ANYTHING MODEL (SAM) Baroroh, Anisa; Harintaka, Harintaka
Elipsoida : Jurnal Geodesi dan Geomatika Vol 8, No 1 (2025): Volume 08 Issue 01 Year 2025
Publisher : Department of Geodesy Engineering, Faculty of Engineering, Diponegoro University,Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/elipsoida.2025.25145

Abstract

Kebutuhan peta dasar skala besar khususnya di Indonesia semakin meningkat seiring berkembangnya waktu. Salah satu fitur geospasial penting adalah bangunan. Penggunaan teknologi UAV mampu menghasilkan produk ortofoto resolusi tinggi yang dapat membantu pemetaan skala besar. Kumpulan data bangunan yang diturunkan dari data ortofoto dapat memberikan informasi untuk pemantauan dan perencanaan suatu daerah, khususnya di daerah yang tidak memiliki peta perencanaan rinci atau data kadaster. Pada umumnya, ekstraksi objek tapak bangunan dilakukan dengan digitasi manual. Namun, perkembangan terbaru menunjukkan teknologi otomatisasi dengan deep learning memiliki keunggulan lebih baik dari sisi kinerja yang lebih singkat. Teknik deep learning yang terbaru saat ini adalah SAM (Segment Anything Model). SAM merupakan pendekatan baru yang dikembangkan oleh Meta AI untuk segmentasi yang telah dilatih pada dataset sangat besar sehingga tidak memerlukan pelatihan ulang (Kirillov et al., 2023). Penelitian ini memanfaatkan SAM untuk ekstraksi tapak bangunan khususnya wilayah Indonesia. Karakteristik bangunan yang sangat bervariasi menjadi tantangan algoritma SAM dalam mengekstraksi tapak bangunan. Selain SAM, penggunaan metode regularisasi diterapkan untuk memperbaiki bentuk bangunan hasil segmentasi yang tidak teratur dan tegas. Hasil uji akurasi precision, recall, f1-score, dan IoU secara keseluruhan menunjukkan rata-rata nilainya diatas 87 %. Hasil tersebut menunjukkan bahwa SAM mampu melakukan ekstraksi tapak bangunan dengan baik.
MONITORING PERUBAHAN TUTUPAN LAHAN KABUPATEN KLATEN TAHUN 2019 DAN 2023 SELAMA PEMBANGUNAN JALAN TOL YOGYAKARTA – SOLO MENGGUNAKAN GOOGLE EARTH ENGINE (GEE) Apriyanti, Dessy; Layali, Ilfa; Gomareuzzaman, Muhammar; Pratiwi, Nova Wahyu; Martasari, Rial Dwi
Elipsoida : Jurnal Geodesi dan Geomatika Vol 8, No 1 (2025): Volume 08 Issue 01 Year 2025
Publisher : Department of Geodesy Engineering, Faculty of Engineering, Diponegoro University,Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/baf.%v.%i.%Y.%p

Abstract

Metode konvensional seperti klasifikasi berbasis piksel yang biasa digunakan dalam melakukan klasifikasi tutupan lahan membutuhkan waktu yang cukup lama, selain itu dibutuhkan komputer yang memiliki performa yang tinggi agar proses pengolahan citra dapat berjalan dengan lancar. Google Earth Engine (GEE) memungkinkan pengguna untuk melakukan pengolahan citra satelit ter-georeferensi yang tersimpan pada arsip (cloud) GEE dengan membangun suatu algoritma untuk menjalankannya. GEE juga mempunyai beberapa metode machine learning untuk analisis citra. Salah  satu  metode machine  learning yang popular digunakan adalah Random Forest. Random Forest (RF) telah banyak digunakan mengklasifikasikan citra satelit seperti yang dilakukan. Keunggulan dari metode RF, di antaranya non-parametrik,  mampu  menggunakan  set  data kontinyu  dan  tidak  sensitif  terhadap over-fitting. RF  adalah  metode  potensial  untuk memetakan  tutupan  lahan  dibandingkan  dengan  metode konvensional. Dilakukan penelitian tentang perubahan tutupan lahan menggunakan Algoritma Random Forest pada platform GEE di Kabupaten Klaten Jawa Tengah pada tahun 2019 – 2023 selama Pembangunan Jalan Tol Yogyakarta – Solo. Analisis perubahan tutupan lahan dilakukan menggunakan data citra satelit Sentinel 2A. Selain analisis tutupan lahan dari hasil algoritma RF, dilakukan uji akurasi menggunakan matriks konfusi. Hasil model Random Forest yang sudah dijalankan menunjukan hasil perubahan lahan masing – masing kelas tutupan lahan, dengan kelas paling banyak berubah pada bangunan bertambah sebanyak 4.972 ha serta paling berkurang pada kelas badan air 0.341 ha. hasil klasifikasi model Random Forest juga menunjukan uji akurasi dengan Kappa Accuracy 77% pada tahun 2019, serta Kappa Accuracy 84% pada tahun 2023.
IDENTIFICATION OF GREEN OPEN SPACES USING THE NDVI AND SAVI METHODS IN THE CITY OF METRO Amri, Resica Permata; Zakaria, Ahmad; Novianti, Tika Christy; Armijon, Armijon
Elipsoida : Jurnal Geodesi dan Geomatika Vol 8, No 2 (2025): Volume 08 Issue 02 Year 2025
Publisher : Department of Geodesy Engineering, Faculty of Engineering, Diponegoro University,Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/elipsoida.2025.26893

Abstract

The presence of green spaces in urban areas is a critical issue for Indonesian local authorities, prompting various initiatives aimed at achieving effective and high-quality urban growth. As stipulated by Spatial Planning Regulation No. 26 from 2007, it is mandated that each city must allocate 30% of its total area for green open spaces to foster a productive, secure, pleasant, and sustainable urban setting, with a minimum standard consisting of 20% public green areas and 10% private green areas. This research employs two methods for assessing vegetation density, specifically NDVI and SAVI. These two indices play significant roles in vegetation examination, thus employing both indices is intended to address each method’s limitations, with the expectation that they will yield precise vegetation mapping based on comparative findings. The ultimate area identified for green open spaces is 77.97 hectares, while the green space map for Metro City, derived from the Metro Regional Planning Agency, indicates a total of 110.82 hectares. This produces a percentage discrepancy of 0.70, or 70%. The two maps reveal a disparity between the Research RTH and Bappeda RTH results. The data indicates that the Green Open Space in Metro City spans an area of 110.82 hectares. The NDVI method categorizes Green Open Space with a minimum value range of -0.16 to 0.25. Meanwhile, the SAVI method classifies Green Open Space within a minimal value range of 0.36 to 0.52. Nevertheless, this situation has not been fully optimized due to the presence of rice fields and remained vegetative land cover being recorded. Following the overlay analysis of the Vegetation Density Map, Land Cover, and Rice Fields, rice paddies and urban areas are no longer recognized as Green Open Spaces. Therefore, the Green Open Space in Metro City for the year 2022 is finalized at 77.97 hectares.
ACCURACY ANALYSIS OF 3D COORDINATES FROM TERRESTRIAL LASER SCANNER (TLS) AND AIRBORNE LASER SCANNING (ALS) MEASUREMENTS (CASE STUDY: TRANSMISSION TOWER) Prasetyo, Friski Putra; Kurniawan, Akbar; Dhiaurrahman, Antony Rafie; Setiawan, Fabian Saiadin
Elipsoida : Jurnal Geodesi dan Geomatika Vol 8, No 2 (2025): Volume 08 Issue 02 Year 2025
Publisher : Department of Geodesy Engineering, Faculty of Engineering, Diponegoro University,Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/elipsoida.2025.29337

Abstract

Transmission towers on high-voltage power lines serve as supporting structures for electrical conductors and insulators, requiring routine maintenance to ensure safety and reliability. This study aims to analyze the 3D coordinates of transmission towers using Terrestrial Laser Scanner (TLS) and Airborne Laser Scanning (ALS) methods. The calculation of the Root Mean Square Error (RMSE) against Total Station (TS) measurements showed that TLS achieved higher accuracy, with an RMSE of 0.0037 m, compared to ALS at 0.0136 m. Statistical testing using the t-distribution on 21 data points showed that the t-values for TLS and ALS were 1.967255 and -0.385437, respectively, both of which fall within the critical value range at a 5% significance level. It was therefore concluded that there was no significant difference compared to the Total Station (TS) measurements. The confidence interval analysis at a 95% confidence level indicated that 95% of the TLS data and 61% of the ALS data fell within the acceptable range. In terms of visualization, TLS produced a denser and precise point cloud with texture details, while ALS excelled in point cloud color representation. Each method has its advantages, with TLS being superior in detailed accuracy and ALS being efficient for large-area data acquisition. Keywords: Airborne Laser Scanning, Point Cloud, Terrestrial Laser Scanner, Transmission Tower
MAPPING OF ADMINISTRATIVE BOUNDARIES OF URUTSEWU VILLAGE, AMPEL DISTRICT, BOYOLALI REGENCY USING THE CARTOMETRIC METHOD Muharrom, Ahmad Shahrul
Elipsoida : Jurnal Geodesi dan Geomatika Vol 8, No 2 (2025): Volume 08 Issue 02 Year 2025
Publisher : Department of Geodesy Engineering, Faculty of Engineering, Diponegoro University,Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/elipsoida.2025.27897

Abstract

Mapping of village administrative boundaries is one of the key aspects supporting governance, public services, and regional development planning. This research goal is to map the administrative boundaries of Urutsewu Village, Ampel Subdistrict, Boyolali Regency, with high accuracy using a cartometric method in accordance with Ministry of Home Affairs Regulation Number 75 of 2014 concerning Guidelines for Determining and Confirming Village Boundaries. The cartometric method was used to integrate various data sources, including historical maps, satellite imagery, field surveys, and administrative documents, to produce an objective and accurate boundary analysis. The results is that the administrative boundary of Urutsewu Village consists of four main segments connecting boundary node points. Differences in segment lengths were found between the existing indicative village boundary and the newly delineated boundary, resulting in a change in the village’s area from approximately 245.473 ha to 290.982 ha and in its perimeter from 8,210.949 meters to 8,918.779 meters. The boundary verification process also involved community participation as a validation step to ensure conformity with field conditions. The outputs of this research include a 1:5,000 scale boundary map of Urutsewu Village in both printed and digital formats. Keywords : village boundary, cartometric method, boundary confirming, scale, community participation
LANDSLIDE VULNERABILITY ZONE MODELING BASED ON AHP-GIS AND GEOELECTRIC METHOD VERIFICATION: AN EARLY DETECTION STRATEGY IN SEBULU DISTRICT, KUTAI KARTANEGARA REGENCY Fadillah, Rizky; Djayus, Djayus; Khoirunisa, Nanda
Elipsoida : Jurnal Geodesi dan Geomatika Vol 8, No 2 (2025): Volume 08 Issue 02 Year 2025
Publisher : Department of Geodesy Engineering, Faculty of Engineering, Diponegoro University,Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/elipsoida.2025.29468

Abstract

Landslides and soil movement frequently occur in Sebulu District, especially on the main access road. This study aims to analyze the vulnerability to landslides in Sebulu District, Kutai Kartanegara Regency, using the Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) approaches, with verification through the geoelectric method. The advantage of this integrated approach is its ability to combine multi-criteria spatial modeling with physical validation of subsurface conditions, thereby producing a more comprehensive and reliable analysis. The secondary data used includes geological information, rainfall, slope gradient, soil type, and land cover. By applying AHP weighting, landslide vulnerability values were obtained with "low," "medium," and "high" categories, covering areas of 230.31 ha, 301.99 ha, and 309.30 ha, respectively. Verification using the geoelectric method identified a weak zone with a thickness of 1–5 meters, which could trigger soil movement towards the southwest. These results provide a clearer understanding of landslide-prone areas, which can serve as the basis for early detection strategies in disaster mitigation.Keywords: susceptibility zones, landslide, geographic information system, AHP, geoelectric resistivity.
MONITORING OF VERTICAL GROUND SURFACE MOVEMENT IN THE COASTAL AREA OF CILACAP REGENCY USING THE SMALL BASELINE SUBSET INTERFEROMETRY SAR (SBAS INSAR) METHOD Busoroh, Luluk Sabdu; Panuntun, Hidayat
Elipsoida : Jurnal Geodesi dan Geomatika Vol 8, No 2 (2025): Volume 08 Issue 02 Year 2025
Publisher : Department of Geodesy Engineering, Faculty of Engineering, Diponegoro University,Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/elipsoida.2025.28688

Abstract

Cilacap Regency on the south coast of Java Island is prone to land movement due to its risky rock formation structure. Previous research found land subsidence in the coastal area of Cilacap District at -70 mm/year in the satellite view direction. The movement in the satellite view direction is not representative enough to show vertical movement directly. Thus, vertical movement in the coastal area of Cilacap Regency was calculated using the InSAR method. The data used is an unwrapped interferogram image of Sentinel-1 ascending and descending orbit from 2014 to 2024. Noise correction due to tropospheric delay is done with GACOS data. The data was processed using the Small Baseline Subset Interferometry SAR method with LiCSBAS software. Vertical movement was obtained via 2.5-D extraction from ascending and descending Line of Sight (LOS) displacement results and validated with GNSS CORS data of the CCLP station. The results show uplift on the south coast and subsidence in the northern part of Cilacap, with cumulative vertical displacement between -42.823 mm and 50.968 mm and velocities between -4.070 mm/year and 6.349 mm/year. Validation shows 2.5-D InSAR estimates are consistent with GNSS data.Keywords : Vertical Displacement, InSAR, LiCSBAS, Time Series, Cilacap
COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE ON LANDSAT 8 AND HLS-L30 USING MONO WINDOW AND SPLIT WINDOW ALGORITHMS (CASE STUDY: WKP MOUNT UNGARAN) Nababan, Yolanda Stevany; Putri, Rizki Amara; Bashit, Nurhadi; Hadi, Firman; Ihsanudin, Taufiq
Elipsoida : Jurnal Geodesi dan Geomatika Vol 8, No 2 (2025): Volume 08 Issue 02 Year 2025
Publisher : Department of Geodesy Engineering, Faculty of Engineering, Diponegoro University,Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/elipsoida.2025.28716

Abstract

Advancements in remote sensing technology have enabled the use of satellite imagery, such as Landsat 8 and HLS-L30, for the spatial and temporal estimation of Land Surface Temperature (LST) with improved resolution. In the context of geothermal exploration, the availability of thermal infrared bands in these datasets facilitates more efficient and cost-effective mapping and identification of surface temperature anomalies, particularly across large and inaccessible areas. This study aims to compare LST estimations derived from Landsat 8 and HLS-L30 imagery using the Mono Window Algorithm (MWA) and Split Window Algorithm (SWA) at 18 geothermal manifestation points within the Mount Ungaran Geothermal Working Area (WKP). A Focal Statistic process was applied to 20 LST datasets, resulting in a total of 100 LST layers. From each layer, LST values were extracted at the 18 manifestation points, producing a total of 1,800 data points. A binary logistic regression analysis was conducted using these LST values alongside those from 20 randomly selected comparison points. The results indicate that the median LST derived from HLS-L30 imagery using the Split Window Algorithm with the minimum Focal Statistic yielded the most optimal performance in classifying geothermal manifestation presence. This method achieved statistical significance (p = 0.028), indicating its capability to effectively distinguish between manifestation and non-manifestation points. However, the pseudo-R² value of 0.107 suggests that the model explains approximately 11% of the variance in the data. These findings underscore the potential application of satellite-based LST analysis in the early detection and assessment of geothermal surface anomalies within WKPs.Keywords :  Geothermal, LST, Landsat, HLS-L30, Ungaran
SPATIOTEMPORAL ANALYSIS OF MANGROVE DENSITY DYNAMICS USING SENTINEL-2 IMAGERY AND CLOUD COMPUTING BASED MACHINE LEARNING ALGORITHMS (CASE STUDY: DEMAK REGENCY) Nirwanawati, Raya; Bashit, Nurhadi; Lazuardi, Wahyu
Elipsoida : Jurnal Geodesi dan Geomatika Vol 8, No 2 (2025): Volume 08 Issue 02 Year 2025
Publisher : Department of Geodesy Engineering, Faculty of Engineering, Diponegoro University,Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/elipsoida.2025.28959

Abstract

Mangrove forests are vital coastal ecosystems that protect shorelines and help maintain coastal environmental balance. Mangrove forests undergo changes every year due to degradation or restoration, so monitoring needs to be carried out. This study analyzes the spatiotemporal dynamics of mangrove density in Demak Regency from 2020 to 2025 using Sentinel-2 imagery and the CART (Classification and Regression Tree) algorithm processed through the Google Earth Engine (GEE) platform. Six spectral indices were used as classification inputs, namely NDVI, NDWI, MVI, MI, AMMI, and CMRI. AMMI is the best index in identifying mangroves as a whole, both in narrow and large areas. Based on classification, the mangrove cover area has been restored or significantly increased from 1644.011 ha in 2020 to 2530.522 ha in 2025. The largest increase occurred in the high canopy density class of 960.157 ha. Meanwhile, the medium and low canopy density classes showed a decrease in area. Accuracy assesment showed an overall accuracy value of 99.92% for 2020 and 99.91% for 2025, with kappa accuracy above 97% in both years. These results show that the classification method with the support of cloud computing can be relied on in spatiotemporal monitoring of mangrove changes efficiently and accurately. Keywords:  Mangrove, Sentinel-2, Machine Learning, Classification and Regression Tree, Cloud Computing
Analysis of Building Density Using Deep Learning Model Semantic Segmentation Nuranda, Kris Junida Herindra; Awaluddin, Moehammad; Hadi, Firman
Elipsoida : Jurnal Geodesi dan Geomatika Vol 8, No 2 (2025): Volume 08 Issue 02 Year 2025
Publisher : Department of Geodesy Engineering, Faculty of Engineering, Diponegoro University,Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/elipsoida.2025.27502

Abstract

Densely populated settlements are one of the urban problems with building density that requires special attention. This research aims to detect and analyze the spatial distribution of building density, especially in detecting building density in residential areas using the Semantic Segmentation deep learning model method with a research dataset sourced from the entire DKI Jakarta Province area. The analysis was conducted using typology criteria in the form of building density levels based on PERMEN PUPR No. 14 of 2018 concerning the Prevention and Improvement of the Quality of Slums and Slum Settlements, which was processed through the Kaggle Notebook and Google Colaboratory platforms using the Python programming language and based on the U-Net architecture. The segmentation results show that using the U-Net architecture is capable of classifying image pixels with an accuracy of 70% in distinguishing between dense and Sparse buildings, which indicates fairly good accuracy performance. The output produced in this final project research is a web interface for detecting dense and Sparse buildings that can be used as a tool to aid in decision-making for regional planning. This research shows that the Semantic Segmentation deep learning model approach can be an efficient and objective solution in satellite image-based spatial analysis. Keywords:  Deep Learning, Building Density, Semantic Segmentation       

Page 14 of 15 | Total Record : 141

 11   12   13   14   15 

Filter by Year

2018 2025


Reset
Filter By Issues
All Issue Vol 8, No 2 (2025): Volume 08 Issue 02 Year 2025 Vol 8, No 1 (2025): Volume 08 Issue 01 Year 2025 Vol 7, No 2 (2024): Volume 07 Issue 02 Year 2024 Vol 7, No 1 (2024): Volume 07 Issue 01 Year 2024 Vol 6, No 2 (2023): Volume 06 Issue 02 Year 2023 Vol 6, No 1 (2023): Volume 06 Issue 01 Year 2023 Vol 5, No 2 (2022): Volume 05 Issue 02 Year 2022 Vol 5, No 1 (2022): Volume 05 Issue 01 Year 2022 Vol 4, No 02 (2021): Volume 04 Issue 02 Year 2021 Vol 4, No 01 (2021): Volume 04 Issue 01 Year 2021 Vol 3, No 02 (2020): Volume 03 Issue 02 Year 2020 Vol 3, No 01 (2020): Volume 03 Issue 01 Year 2020 Volume 03, Nomor 01, Tahun 2020 Vol 2, No 02 (2019): Volume 02 Issue 02 Year 2019 Vol 2, No 01 (2019): Volume 02 Issue 01 Year 2019 Vol 1, No 02 (2018): Volume 01 Issue 02 Year 2018 Vol 1, No 01 (2018): Volume 01 Issue 01 Year 2018 Volume 01, Nomor 02, Tahun 2018 More Issue