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All Journal Rang Teknik Journal INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY
Ulfah, Umayya
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Energy Engineering Environmental Science

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ANALISA KENAIKAN TEMPERATUR PADA BETON BERDIMENSI BESAR (MASS CONCRETE) DENGAN SEMEN TYPE V PADA PONDASI STRUKTURAL TURBINE HOUSE Ulfah, Umayya; Sugiyanto, Sugiyanto
Rang Teknik Journal Vol 4, No 2 (2021): Vol. 4 No. 2 Juni 2021
Publisher : Fakultas Teknik Universitas Muhammadiyah Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (773.632 KB) | DOI: 10.31869/rtj.v4i2.2642

Abstract

Seiring dengan perkembangan dunia konstruksi sipil, saat ini telah banyak dilakukanpembuatanbeton dengan jumlah yang besar.Hal ini berpengaruh pada jumlah semen yang digunakan karena dimensinya yang besar dan berakibat pada tingginya suhu beton yang dihasilkan. Untuk itu salah satu cara untuk mengurangi tingginy suhu beton yang dihasilkan, pemilihan type semen menjadi krusial. Di Proyek Pembangkit Listrik Tenaga Panas Bumi Muara Laboh, banyak digunakan pondasi berdimensi besar (Mass Concrete) sebagi pondasi struktural Turbine House. Penggunaan Semen Type V dalam Design Mix Concrete Beton sebagai upaya menekan tingginya suhu beton yang akan dihasilkan, dengan komposisi semen 425 kg/m3. Pengambilan data dilakukan pada pondasi  footing type F1g titik 2G dan type F1g titik 2F yang berdimensi 6,5 x 4 x 1,85 m; pondasi footing type F1d titik 6D dan type F1f titik 6B yang berdimensi 4 x 4 x 1,25 m. dari hasil penelitian pondasi  footing type F1g titik 2G dan type F1g titik 2F menghasilkan suhu inti beton >700C yang berpotensi tinggi terjadinya salah satu cacat beton yaitu DEF (Deferred Ettringite Formation).
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 Sugiyanto - Widya, Liadira Kusuma Wijayanti, Regita Faridatunisa