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All Journal Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Journal of Plano Studies
Yogie Ardiwinata
Universitas Indo Global Mandiri
Computer Science & IT Education Electrical & Electronics Engineering Engineering Social Sciences

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Peningkatan Kesiapsiagaan Darurat Bencana Gedung melalui Pelatihan dan Simulasi Manajemen Bencana pada Civitas Akademika Arie Anggara; Marsidi Marsidi; Yogie Ardiwinata; Amelia Regina; Intan Pebrida
JURIBMAS : Jurnal Hasil Pengabdian Masyarakat Vol 5 No 1 (2026): Juli 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juribmas.v5i1.1046

Abstract

Indonesia merupakan negara yang rawan terhadap bencana, seperti kebakaran dan gempa bumi. Pengetahuan dan keterampilan dalam menghadapi keadaan darurat di gedung sangat diperlukan bagi civitas akademika. Kegiatan pengabdian ini bertujuan untuk meningkatkan pengetahuan, keterampilan, dan kesiapsiagaan civitas akademika di Fakultas Kedokteran Universitas Indo Global Mandiri. Metode yang diterapkan mencakup pelatihan dan simulasi praktik evakuasi serta bantuan hidup dasar, termasuk simulasi penggunaan alat pemadam api ringan (APAR), dengan melibatkan 25 peserta civitas akademika. Evaluasi dilakukan melalui kuesioner kepuasan peserta dan observasi partisipasi aktif selama simulasi. Hasil kegiatan menunjukkan bahwa 96% peserta menilai peningkatan pengetahuan terkait kesiapsiagaan darurat, dan 100% peserta menyatakan bahwa materi pelatihan dapat diterapkan dalam praktik sehari-hari. Kegiatan ini juga berhasil memperkuat keterampilan teknis, koordinasi tim, serta budaya keselamatan di lingkungan akademik.
The Effectiveness of Identifying Residential Housing using Image Recognition by Artificial Intelligence Yogie Ardiwinata; Annisa Kurnia Shalihat
Journal Of Plano Studies Vol 2 No 1 (2025)
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Kepustakaan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jops.v2i1.5476

Abstract

Residential housing identification based on satellite imagery has become an important approach in supporting urban planning, disaster management, and regional mapping. This study evaluates the effectiveness of settlement recognition techniques using high-resolution imagery and artificial intelligence (AI) models, specifically deep learning methods based on convolutional neural networks (CNN) and object segmentation. The main factors that affect identification accuracy include image spatial resolution, preprocessing quality, training data diversity, and the geographic complexity of the observed area. Based on the analysis results, the use of high-resolution imagery combined with image recognition by AI such as Google Gemini and ChatGPT can produce an accuracy of 68.4% in calculating the number of buildings. This value tends to be low to achieve a high level of accuracy in calculating the number of buildings therefore it is not recommended to calculate the number of buildings accurately, but it can be used to determine housing availability in a range of values. However, to analyze building density, AI can successfully generate complex images of building density according to the conditions of the given image. AI can be used to aid urban planning while emphasizing the importance of selecting data sources, careful preprocessing techniques, and adaptive machine learning models to improve the effectiveness of settlement recognition, especially in areas with complex spatial structures in the fields of regional and urban planning and disaster management.
Co-Authors Amelia Regina Annisa Kurnia Shalihat Arie Anggara Intan Pebrida Marsidi Marsidi