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All Journal Sewagati Geoid - Journal of Geodesy and Geomatics
Irbah, Nafisatus Sania
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Journal : Geoid - Journal of Geodesy and Geomatics

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Spatial Analysis of Flood Inundation From Sentinel-1 Imagery Using Google Earth Engine (Case Study: Bengawan Jero Lamongan Regency) Irbah, Nafisatus Sania; Jaelani , Lalu Muhamad
GEOID Vol. 19 No. 2 (2024)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v19i2.1207

Abstract

Flooding is a natural disaster due to rivers that are no longer able to accommodate excessive rainwater so that water overflows and inundates the surrounding area. During the rainy season, many areas in Indonesia experience flooding, one of which is the Lamongan Regency. In early 2022, seasonal flooding occurred due to runoff from Bengawan Jero which caused many houses, agricultural land and access roads to be submerged in water. To improve disaster mitigation activities, it is necessary to identify flooding areas using remote sensing. The distribution area of flood inundation was identified using change detection and threshold methods. The change detection method is carried out by using ratio images from Sentinel-1 image data. The results of land cover in Lamongan Regency resulted in 9 land cover classes. Where is dominated by agricultural class land cover with an area of 1057.94 km2 with a percentage of the total area of Lamongan Regency is 60.53%. While the smallest land cover area is the mangrove class covering an area of 101.237 km2 with a percentage of the total area of 0.058%. Extraction of the inundation area was carried out with two different threshold values obtained from equations and statistical calculations. The flood inundation area generated on January 31, 2022, for the first threshold value is 54.932 km2 with an overall accuracy of 97% with a kappa coefficient is 0.94. While the flood inundation area with the second threshold value is 90.330 km2 with an overall accuracy of 94% and a kappa coefficient is 0.88.
Land Value Modeling Using Log-Linear Multiple Regression Irbah, Nafisatus Sania; Putri, Intan Permata; Deviantari, Udiana Wahyu
GEOID Vol. 20 No. 1 (2025)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v20i1.2725

Abstract

Land value is an assessment of land based on its economic potential. It is influenced by various factors, including public facilities, road networks, and proximity to supporting infrastructure. Land value information plays a crucial role in infrastructure development, budget planning, and site selection for new infrastructure projects. According to the Surabaya City Regional Spatial Plan (Rencana Tata Ruang Wilayah – RTRW) 2014–2034, the development of Teluk Lamong Port, located in the Tambak Osowilangun Subdistrict, aims to enhance national logistics efficiency by alleviating traffic congestion at Tanjung Perak Port, which has exceeded its maximum capacity. This development is expected to affect land values in the subdistrict. Therefore, an objective land valuation is necessary, which can be achieved through modeling. This study employs a Multiple Linear Regression (MLR) approach with a log-lin model to determine land values. The modeling was conducted using 87 land sale and purchase transaction records, which were adjusted based on Circular Letter of the Directorate General of Taxes No. SE-55/PJ.6/1999. The independent variables used in the model include Land Area (LT), Land Use (PL), Distance to Road (JJ), Distance to Port (JPTL), Distance to the Central Business District (JCOP), and Distance to the Terminal (JTTO). The model was evaluated using statistical tests, including the coefficient of determination, partial test, simultaneous test, multicollinearity test, and Coefficient of Variation (CoV) for model evaluation. The resulting land value model is expressed as: Ln NTE = 9.305184 + (1.053730 × PL) + (-0.000450 × JCOP) + (0.000823 × JPTL). The CoV value obtained remains acceptable as it is below 20%, indicating the model's reliability.
Co-Authors Akbar Kurniawan Hariyanto, Irena Hana Husnul Hidayat, Husnul Indah Trisnawati Dwi Tjahjaningrum Intan Permata Putri Jaelani , Lalu Muhamad Maulida, Putra Mukmin, Muchammad Faisal Muryono, Mukhammad Nurwatik, Nurwatik Nusantara, Candida Aulia De Silva Syariz, Muhammad Aldila Udiana Wahyu Deviantari, Udiana Wahyu