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All Journal Jurnal Pemberdayaan Masyarakat Madani (JPMM) INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY
Widya, Liadira Kusuma
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Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Energy Environmental Science Social Sciences Other

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Socialization and Technical Guidance of Road Specifications to Glodog Village Appliances, Palang District, Tuban Regency in the Framework of Village Road Development Sugiyanto; Widya, Liadira Kusuma; Octavia, Syariah Nur; Susanto, Harry
Jurnal Pemberdayaan Masyarakat Madani (JPMM) Vol. 7 No. 1 (2023): Jurnal Pemberdayaan Masyarakat Madani (JPMM) (DOAJ & SINTA 3 Indexed)
Publisher : Fakultas Ekonomi dan Bisnis Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JPMM.007.1.06

Abstract

Roads are an indispensable infrastructure in supporting community economic activities, both in urban and rural areas. This community service activity was carried out with the aim of providing socialization and technical guidance on road specifications to the Glodog Village Government apparatus, Palang, Tuban East Java, in the context of village road construction. This theme was chosen because of the problem of proposing village roads to Regional Apparatus Organization (OPD) who do not have the authority to handle them. The method used is to hold a face-to-face meeting followed by participants consisting of village government officials, the head of the BPD and community leaders in Glodog Village, Palang District, Tuban East Java. The results of the implementation of this community activity went quite smoothly, followed quite enthusiastically, and received a warm welcome because it provided very useful knowledge in the construction of village roads. To get more optimal benefits, it is recommended that further socialization activities involve elements of OPD and contractors so that all problems encountered can be solved in a directed and integrated manner.
FLOOD SUSCEPTIBILITY MAPPING USING MACHINE LEARNING IN KENING RIVER, SUB WATERSHED OF BENGAWAN SOLO, TUBAN Mustikaningrum, Dhina; Widya, Liadira Kusuma; Ulfah, Umayya; Wijayanti, Regita Faridatunisa
INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY VOLUME 7, NUMBER 2, OCTOBER 2024
Publisher : Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/urbanenvirotech.v7i2.18818

Abstract

Floods are natural occurrences with the potential to cause damage to ecosystems and pose significant threats to human life, resulting in the destruction of property, infrastructure, and socioeconomic challenges. In recent times, flooding in the Sub-Watershed of Bengawan Solo has been linked to the overflowing Kening River in Tuban County. Aim: This study aims to produce a flood susceptibility map to mitigate the frequency of flood occurrences as well as facilitate effective planning for flood disaster risk management. Methodology and results: Flood data is collected from 2016 to 2023 through field surveys, Sentinel-1 satellite imagery, and data from the Development Planning Agency, Tuban County. Integrating remote sensing data from satellite imagery (PlanetScope, Sentinel-2), geographic information systems (GIS), and spatial modeling techniques, a flood susceptibility map is developed for the Kening River catchment. The occurrence of floods in the Kening River area is associated with various factors (11 variables) assessed through the frequency ratio approach, including profil curvature, LS factor, aspect, rainfall, river distance, road distance, building density 100 m, road density 100 m, vegetation type, normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI). The results show flood susceptibility maps utilizing frequency ratio (FR) and convolutional neural network (CNN) techniques. The flood susceptibility map obtained through the CNN method demonstrates a notably high AUC value. The model development generated a validation AUC value of 0.857 for training and 0.856 for testing. Conclusion, significance and impact study: This research provides an understanding into the factors that influence the occurrence of floods in the Kening River catchment area. It also emphasizes the benefit of advanced machine learning approaches in mapping the susceptibility of floods. Furthermore, this study has the potential to be helpful in guiding regional policy decisions and result in enhanced flood risk management measures in Tuban County.
Co-Authors Dhina Mustikaningrum Octavia, Syariah Nur SUGIYANTO Susanto, Harry Ulfah, Umayya Wijayanti, Regita Faridatunisa