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All Journal Jurnal Pendidikan Geografi Undiksha Ulil Albab Journal of Selvicoltura Asean Journal of Loomingulisus ja Innovatsioon
Glendy Somae
Universitas Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Analisis Spasial Sebaran Sekolah Dasar Terdampak Bencana Gempa di Kecamatan Cugenang Kabupaten Cianjur Fani Setyawan; Ayu Handayani; Brigitta Maria; Dedy Swandry; Glendy Somae; Adi Wibowo
Jurnal Pendidikan Geografi Undiksha Vol. 11 No. 1 (2023): Jurnal Pendidikan Geografi Undiksha
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jjpg.v11i1.56929

Abstract

Sekolah merupakan salah satu prasarana yang berfungsi dalam pemenuhan kebutuhan pendidikan masyarakat dan lokasi pendirian sekolah harus memperhatikan kesesuaian karakteristik daerah.  Gempa yang terjadi di Kabupaten Cianjur mengakibatkan rusaknya fasilitas umum khususnya sekolah dasar di beberapa daerah, termasuk Kecamatan Cugenang.  Kerusakan parah pada sekolah dasar, berdampak pada terganggunya kegiatan belajar mengajar (KBM).  Penelitian ini bertujuan untuk mengetahui sebaran kerusakan pada Sekolah Dasar di kecamatan Cugenang, kabupaten Cianjur dengan menggunakan metode Nearest Neighbor Analysis (NNA) serta teknik proximity dengan metode Buffer dalam Sistem Informasi Geografi (SIG). Hasilnya, dari metode NNA diperoleh Pola Acak dalam menentukan lokasi Sekolah Dasar di kecamatan Cugenang, sehingga tidak dapat memberikan pola linear saling melengkapi antar sekolah yang memberikan kemudahan akses. Metode Buffer dengan jangkauan 1 km dilakukan untuk melihat pola sebaran kerusakan sekolah dasar pada potensi pengalihan proses KBM yang rusak parah.
Analisis Potensi Genangan Banjir di Kecamatan Siwalalat, Kabupaten Seram Bangian Timur berdasarkan Topographic Wetness Index Abdul Muin; Glendy Somae; Heinrich Rakuasa
ULIL ALBAB : Jurnal Ilmiah Multidisiplin Vol. 2 No. 5: April 2023
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/jim.v2i5.1502

Abstract

Kecamatan Siwalalat sering dilamda banjir di musim hujan. Banjir yang sering terjadi Kecamatan Siwalalat diakibatkan oleh luapan sungai Wayaiya, sungai Waidala, sungai Fos, dan sungai Abuleta yang diduga imbas dari pembalakan liar di daerah hulu sungai. Penelitian menggunakan data DEMNAS dan analisis menggunakan metode Topographic Wetness Index. Hasil analisis potensi genangan dibagi menjadi tiga kelas yaitu kelas potensi rendah dengan luas 46.490,34 ha, kelas sedang seluas 15.423,83 ha, dan kelas potensi tinggi seluas 2.385,11 ha serta diprediksi seluas 130,21 ha permukiman terdampak banjir. Hasil penelitian diharapkan dapat menjadi refrensi bagi pemerintah serta masyarakat dalam penanganan banjir kedepan guna meminimalisir dampk yang terjadi.
Integration of Remote Sensing Data and Geographic Information System for Mapping Landslide Risk Areas in Ambon City, Indonesia Fekry Salim Hehanussa; Philia Christi Latue; Heinrich Rakuasa; Glendy Somae
Journal of Selvicoltura Asean Vol. 1 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v1i3.1185

Abstract

This research investigates the integration of remote sensing data and Geographic Information Systems (GIS) to map landslide risk areas in Ambon City, Indonesia, a region characterized by its hilly terrain and susceptibility to landslides. Utilizing various environmental variables such as slope gradient, land use, and rainfall patterns, the study employs a multi-criteria approach to assess landslide vulnerability and distribution. The findings reveal significant correlations between anthropogenic factors, such as urbanization, and increased landslide risk, highlighting the urgent need for sustainable urban planning and disaster risk management strategies. By providing a comprehensive landslide risk map, this study aims to support local authorities in making informed decisions to enhance community resilience and mitigate the impacts of landslides in Ambon City.
Utilization of Artificial Intelligence for Spatial Decision Support System Glendy Somae; Heinrich Rakuasa
Journal of Loomingulisus ja Innovatsioon Vol. 1 No. 2 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v1i2.1260

Abstract

The integration of Artificial Intelligence (AI) into Spatial Decision Support Systems (SDM) is a transformative advancement in improving decision-making processes in various fields, including urban planning, environmental management, and disaster response. This research uses a literature review methodology to systematically collect, analyze, and synthesize existing scientific articles, conference papers, and relevant reports related to AI applications in SDSS. The findings of this study reveal that AI technologies, such as machine learning and natural language processing, significantly enhance data processing capabilities, enabling the analysis of complex spatial data and the identification of hidden patterns that may be missed by traditional methods. Despite the great benefits, challenges related to data quality, ethical considerations, and the need for capacity building among stakeholders are critical to the successful implementation of AI in SDSS. It can be concluded that while AI has the potential to revolutionize spatial decision-making, ongoing research is essential to develop best practices, address ethical implications, and foster collaboration among various stakeholders to create a more sustainable and resilient society.
Co-Authors Abdul Muin Adi Wibowo Ayu Handayani Brigitta Maria Dedy Swandry Fani Setyawan Fekry Salim Hehanussa Heinrich Rakuasa Heinrich Rakuasa Heinrich Rakuasa Philia Christi Latue