SASSD: A smart assessment system for sector damage post-natural disaster using artificial neural networks

Almais, Agung Teguh Wibowo, Susilo, Adi, Naba, Agus, Sarosa, Moechammad, Crysdian, Cahyo, Basid, Puspa Miladin Nuraida Safitri A, Hariyadi, M. Amin ORCID: https://orcid.org/0000-0001-9327-7604, Tazi, Imam, Arif, Yunifa Miftachul and Wicaksono, Hendro (2023) SASSD: A smart assessment system for sector damage post-natural disaster using artificial neural networks. Presented at 2023 2nd International Conference on Computer System, Information Technology, and Electrical Engineering (COSITE), 19 Sep 2023, Banda Aceh.

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Abstract

Smart Assessment System Sector Damage (SASSD) is an intelligent system for assessing the level of sector damage after natural disasters based on Machine Learning (ML) by applying the Artificial Neural Network (ANN) method. SASSD uses forward propagation in ANN. To measure the level of accuracy of the forward propagation algorithm, it is necessary to have a trial method using data pattern modelling. The optimal accrual level value can be achieved by applying 15 data pattern models and changing the structural values of the forward propagation, namely the hidden layer, and epoch. We used 100 training data and 50 testing data at the experimental stage. The training data is the processed data from Decision Support System (DSS), while the training data contains the level of damage to the sector after natural disasters collected by surveyors. The trial results demonstrate the E5 data pattern model's ideal accuracy rate of 97 percent with a Mean Squared Error (MSE) value of 0.06 and a Mean Absolute Percentage error (MAPE) of 3 percent. This model uses five hidden layers and 125 epochs. The trial results demonstrate the E5 data pattern model's ideal accuracy rate of 97 % with an MSE value of 0.06 and a MAPE of 3 %. This model uses five hidden layers and 125 epochs. Thus, the SASSD can use the 15th data pattern model (E5) to obtain optimal and accurate results.

Item Type:	Conference (Paper)
Keywords:	smart assessment; sector damage; forward propagation; decision support system
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing 04 EARTH SCIENCES > 0404 Geophysics 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing 08 INFORMATION AND COMPUTING SCIENCES > 0806 Information Systems
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Agung teguh Wibowo Almais
Date Deposited:	23 Oct 2023 14:08

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