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Journal : Jurnal Teknosains

Microsoft building footprint application To detect human exposure due to tsunami Saragi, Andes; Mardiatno, Djati; Hizbaron, Dyah Rahmawati
Jurnal Teknosains Vol 13, No 1 (2023): December
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/teknosains.79526

Abstract

Tsunami events at night are more prone to causing fatalities because humans are resting in residential buildings (houses). In this study, residential buildings were extracted using the Microsoft Building Footprint (MBF), which resulted from applying artificial intelligence technology. This study aims to analyze the number of people exposed to tsunamis at night using MBF. The tsunami modeling was carried out using the Berryman method. Sentinel 2-A Image extracted from Google Earth Engine. The results of the inundation modeling analysis show that the total inundated area is 717 Ha or 17.34% of the total area. The results of the MBF accuracy analysis on the entire data are a Precision of 99.02%, Recall of 98.40%, and F1 score of 98.71%. The results of the MBF error analysis are False Positive 0.97%, False Negative 1.60%, and Intersection of Union 0.12%. The number of people exposed is 2,749, or 6.32% of the total population.
Evaluasi kapasitas masyarakat tangguh bencana di kawasan rawan erupsi gunung api merapi Fahmi, Wikan Amarudin; Hizbaron, Dyah Rahmawati
Jurnal Teknosains Vol 13, No 1 (2023): December
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/teknosains.80157

Abstract

Disaster events have become a national issue, one of the reasons is that Indonesia is crossed by a series of volcanoes, such as Merapi Volcano. It erupted back in 2010 and caused massive impact, especially at Kepuharjo Village, Sleman Regency, Yogyakarta. This research is intended to identify the capacity of community resilience due to the Merapi Volcano eruption at Desa Kepuharjo, Kabupaten Sleman. The study collects primary data from interviews and questionnaires from unit samples of Kepuharjo Village using simple random sampling techniques. The data collected from the modified version of the questionnaire were processed using scoring techniques and analyzed using descriptive frequency. The research revealed that the questionaire to capture capacity that generally employed are not fully compliant to be used at the research area. Overall, the questionaire able to capture to assess the capacity classification which are medium (the capacity achievement is comprehensive but not significant to reduce the impact of the disaster) and high ((the capacity achievement is comprehensive and there is a commitment between the government and the community). Kepuharjo Village is classified into classes of 3-5 with a percentage of 65%. Efforts to increase capacity have been carried out by the government, assisted by the community, both by establishing institutions, physical mitigation, and non-physical mitigation.