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All Journal JURNAL ITENAS REKAYASA REKA GEOMATIKA Journal of Degraded and Mining Lands Management Indonesian Journal of Geospatial Journal of Mathematical and Fundamental Sciences ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Serambi Engineering
Dewi Kania Sari
Jurusan Teknik Geodesi - FTSP, Institut Teknologi Nasional (Itenas) Bandung
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Physics

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Utilization of PS-InSAR for Analyzing Land Subsidence in the Bandung Basin, Indonesia using Sentinel-1A Data SARI, DEWI KANIA; KUNCORO, HENRI; NURTYAWAN, RIAN
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 12, No 4: Published October 2024
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v12i4.1116

Abstract

ABSTRAK Cekungan Bandung, yang terletak di Provinsi Jawa Barat, Indonesia, rentan terhadap penurunan muka tanah. Penelitian ini menganalisis laju penurunan muka tanah di Cekungan Bandung selama tahun 2019 menggunakan metode PS-InSAR yang diterapkan pada citra satelit Sentinel-1A. Sebanyak delapan citra Sentinel1A, yang diperoleh antara Januari hingga Desember 2019, diproses menggunakan perangkat lunak SNAP dan STAMPS. Hasil analisis menunjukkan bahwa laju deformasi permukaan tanah di Cekungan Bandung berkisar dari -133 hingga 98 mm/tahun, dengan penurunan paling signifikan terjadi di Kota Bandung dan Kabupaten Bandung. Perbandingan hasil PS-InSAR dengan data survei GPS dari sembilan titik pemantauan menunjukkan korelasi yang kuat (R=0,75), mengonfirmasi keandalan metode PS-InSAR untuk pemantauan penurunan tanah. Temuan ini menegaskan pentingnya pemantauan berkelanjutan dan pengelolaan sumber daya secara bijak guna mengurangi dampak penurunan muka tanah di Cekungan Bandung. Kata kunci: penurunan muka tanah, PS-InSAR, Cekungan Bandung, Sentinel-1A  ABSTRACT The Bandung Basin, located in West Java Province, Indonesia, is highly susceptible to land subsidence. This study analyzes land subsidence rates in the Bandung Basin during 2019 using the PS-InSAR method applied to Sentinel-1A satellite imagery. Eight Sentinel-1A images, acquired between January and December 2019, were processed using SNAP and STAMPS software. The results indicate that deformation rates in the Bandung Basin range from -133 to 98 mm/year, with the most significant subsidence occurring in Bandung City and Bandung Regency. A comparison between PS-InSAR measurements and GPS survey data from nine monitoring points revealed a strong correlation (R=0.75), confirming the reliability of the PS-InSAR method for land subsidence monitoring. These findings underscore the need for continuous monitoring and sustainable resource management to mitigate land subsidence in the Bandung Basin. Keywords: land subsidence, PS-InSAR, Bandung Basin, Sentinel-1A
Identification of Sugarcane Fields and Classification of Their Growth Stages using Random Forest on Google Earth Engine SARI, DEWI KANIA; NOVAL, MUHAMMAD SAPUTRA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 13, No 3: Published July 2025
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v13i3.300

Abstract

Remote sensing technology, particularly Sentinel-2 imagery, offers an efficient and large-scale solution for monitoring sugarcane crop growth phases. This study aims to identify sugarcane fields and classify their growth phases in Jatitujuh District, Majalengka Regency, using the Random Forest algorithm on the Google Earth Engine (GEE) platform. Three vegetation indices—NDVI, NDRE, and CIRE—were used as input variables in the classification process. The results indicate that the stem elongation phase is the most dominant, followed by the maturation, fallow, and sprouting phases. The developed classification model achieved an overall accuracy of 80%, with a Kappa coefficient of 65% and an F1-Score of 68%. This study is expected to contribute to the optimization of sugarcane production in Indonesia and serve as a reference for more effective land management and planning.
Assessing the impact of training samples overlap and density in random forest for landslide susceptibility mapping: Implications for degraded land management in Bandung Regency, Indonesia Nugroho, Hary; Sari, Dewi Kania; Safitri, Sitarani; Azmi, Naufal
Journal of Degraded and Mining Lands Management Vol. 12 No. 5 (2025)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2025.125.8933

Abstract

Landslide susceptibility mapping is essential for disaster mitigation and land management in degraded mountainous regions. Machine learning algorithms, particularly Random Forest (RF), have been increasingly applied due to their robustness in handling complex, non-linear relationships. However, classification performance is often affected by the quality of training samples, especially when landslide and non-landslide points exhibit spatial overlap. This study investigated how varying densities of fully overlapping samples influence RF performance in Bandung Regency, West Java, Indonesia, an area characterised by steep slopes, rapid land-use change, and post-mining degradation. Balanced datasets ranging from 50 to 700 samples per class were evaluated with hyperparameter tuning. The highest validation accuracy (89%) was achieved with 500 samples at a max_depth of 2, while training accuracy was approximately 10% lower, indicating the algorithm’s difficulty in separating overlapping classes. A more stable trade-off was obtained with 300 samples and a max_depth of 4, suggesting that moderate densities enhance generalisation. To translate these findings into practice, we propose an ensemble zoning and uncertainty mapping framework that integrates multiple model outputs to identify consensus zones for slope stabilisation, vegetation restoration, and adaptive spatial planning. This approach improves the reliability of susceptibility maps and provides actionable insights for managing degraded and landslide-prone landscapes.
Analisis Hubungan Faktor Sosial Ekonomi dan Sebaran Tindak Kriminalitas di Jawa Barat Tahun 2024 dengan Pendekatan Statistika Spasial Hermawanti, Lisma; Dewi Kania Sari
Jurnal Serambi Engineering Vol. 10 No. 4 (2025): Oktober 2025
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

West Java Province has a population of more than 50 million people with complex socio-economic dynamics. Factors such as population density, poverty rate, open unemployment, and the Human Development Index (HDI) are suspected to influence the level of crime. According to the National Crime Information Center (2024), there were 43,616 criminal cases in West Java with a crime rate of 86.63 per 100,000 population. This study aims to analyze the relationship between socio-economic factors and crime distribution in West Java in 2024 using a spatial statistics approach, namely Global Moran’s I, Local Indicator of Spatial Association (LISA), and Ordinary Least Squares (OLS) regression. The results show that crime distribution tends to be random, with weak clusters in urban areas. The OLS analysis reveals that among the four independent variables, only HDI has a positive and significant effect on crime, while population density, poverty, and open unemployment are not significant. These findings indicate that higher human development is not always associated with a reduction in crime, particularly in urban areas with intensive socio-economic activities.
Co-Authors Agung B. Harto Agung Budi Harto Agung Budi Harto Azmi, Naufal BAIHAQI, THORIQ Ganny Indrajid Hary Nugroho Hary Nugroho Hermawanti, Lisma Hernawati, Rika Indrajid, Ganny Ishak H. Ismullah Ishak Hanafiah Ismullah Kuncoro, Henri Masyitah Ginting NOVAL, MUHAMMAD SAPUTRA Rian Nurtyawan Safitri, Sitarani Susy Hendriawaty Widyo N. Sulasdi Widyo Nugroho Sulasdi