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All Journal JOIV : International Journal on Informatics Visualization
Fajrin, Rahma Annisa
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Computer Science & IT

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Assessment of Post-Disaster Building Damage Levels Using Back-Propagation Neural Network Prediction Techniques Wibowo Almais, Agung Teguh; Fajrin, Rahma Annisa; Naba, Agus; Sarosa, Moechammad; Juhari, Juhari; Susilo, Adi
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2711

Abstract

Indonesia is susceptible to natural disasters, with its geographical location being one of the contributing factors. To mitigate the harmful effects of natural catastrophes, a disaster emergency response must be undertaken, consisting of steps taken immediately following the event. These operations include rescuing and evacuating victims and property, addressing basic needs, providing protection, and restoring buildings and infrastructure. Accurate data is required for adequate recovery after a disaster. The Badan Penanggulangan Bencana Daerah (BPBD) oversaw disaster relief efforts, but faulty damage assessments slowed restoration. Surveyor subjectivity and differing criteria result in discrepancies between reported damage and reality, generating issues during the post-disaster reconstruction. The objective of this study is to develop a prediction system to measure the extent of damage caused by natural disasters to buildings. The five criteria that decide the level of building damage after a disaster are building conditions, building structure condition, physical condition of severely damaged buildings, building function, and other supporting conditions. The data used are from the BPBD of Malang city from 2019 to 2023. This system would allow surveyors to make speedy and objective evaluations. Five different models were tested using the Neural Network Backpropagation approach. Model A2 produces the highest accuracy of 93.81%. A2 uses a 40-38-36-34 hidden layer pattern, 1000 epochs, and a learning rate 0.1. These findings can lay the groundwork for advanced prediction models in post-disaster building damage evaluation research.
Co-Authors Adi Susilo Agung Teguh Wibowo Almais Agus Naba Juhari Juhari, Juhari Moechammad Sarosa