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All Journal Journal of Information Systems and Technology Research Jurnal Riset Multidisiplin dan Inovasi Teknologi Journal of Geographical Sciences and Education
Somae, Glendy
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Agriculture, Biological Sciences & Forestry Astronomy Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Mapping of Landslide Prone Areas in Huamual Sub-District, Seram Bangian Barat Regency, Indonesia Latue, Theochrasia; Latue, Philia; Rakuasa, Heinrich; Somae, Glendy; Muin, Abdul
Jurnal Riset Multidisiplin dan Inovasi Teknologi Том 1 № 02 (2023): September 2023
Publisher : Pt. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/jimat.v1i02.239

Abstract

This research aims to map landslide-prone areas in Huamual Sub-district, West Seram Regency, Indonesia. Through the collection and analysis of geospatial data, including characteristics of slope, land elevation, geology, rainfall, land cover and distance from active faults, this study successfully identified areas with high potential landslide risk. The results showed that the area in low landslide class has an area of 5,076.67 ha, the area in medium class has an area of 20,979.79 ha and the area in high landslide prone class has an area of 7,430.88 ha. The results of this study provide an important contribution in landslide risk mitigation planning, through identification of zones that need special attention, safer spatial planning, and more effective early warning system. This research provides a strong scientific basis for the government and other stakeholders to take appropriate preventive measures, so as to improve public safety and protect important assets from potential landslide hazards in Huamual Sub-district area.
Modeling Flood Hazards in Ambon City Watersheds: Case Studies of Wai Batu Gantung Rakuasa, Heinrich; Joshua, Benson; Somae, Glendy
Journal of Information Systems and Technology Research Vol. 3 No. 2 (2024): May 2024
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v3i2.836

Abstract

Flood hazard modeling in watersheds is an important step in natural disaster risk mitigation, especially in vulnerable areas such as Ambon City. This research focused on the Wai Batu Gantung, Wai Batu Gajah, Wai Tomu, Wai Batu Merah, and Wai Ruhu watersheds, using JRC Global Surface Water Mapping Layers data, NASA SRTM Digital Elevation 30 m data, and USGS Landsat 8 Level 2, Collection 2, Tier 1 data analyzed on the Google Earth Engine (GEE) platform. Prediction of built-up land in flood-prone areas was conducted by utilizing flood history analysis, hydrological modeling, and flood zone mapping. The results show that flood hazard modeling provides a better understanding of flood risk, assists in the development of safer land use planning, and increases public awareness of flood risk in Ambon City. It is hoped that the results of this research can contribute to flood risk management and sustainable regional development in the future.
Prediction of Land Cover Change in Wae Heru Watershed Ambon City Using Celular Automata Markov Chain Manakane, Susan E; Latue, Philia Christi; Somae, Glendy; Rakuasa, Heinrich
Journal of Geographical Sciences and Education Vol 1 No 1 (2023): Journal of Geographical Sciences and Education
Publisher : PT. Pubsains Nur Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69606/geography.v1i1.52

Abstract

Land cover change in the watershed area in Ambon City has an impact on land degradation, water pollution, flooding and erosion. Therefore, the utilization and efficiency of land cover in the watershed area must be improved based on sustainable land cover planning.  This study aims to analyze land cover changes in the Wae Heru watershed, Ambon City in 2013, 2018, and 2023 and predict land cover in 2028.  This study used the CA-Markov method to predict land cover in 2028. The results showed that in 2013 the built-up land had an area of 74.25 ha, in 2018 an area of 79.30 ha and in 2023 an area of 88.00 ha and the results of the 2028 prediction of built-up land were 116.96 ha, this is certainly influenced by the increasing number of residents who continue to grow every year. Agricultural land, non-agricultural land and open land continue to decrease in area. The results of this prediction are very useful for the government in making policies related to sustainable spatial planning in the future.
Co-Authors Abdul Muin Joshua, Benson Latue, Philia Latue, Philia Christi Latue, Theochrasia Manakane, Susan E Rakuasa, Heinrich