TY - JOUR
T1 - Architectural Models for Predicting the Amount of Natural Disasters and their Effects Using Batch Training
AU - Ginantra, N. L.S.R.
AU - Kesuma, S.
AU - Achmad Daengs, G. S.
AU - Bhawika, G. W.
AU - Mulyani, N.
AU - Hasian, I.
AU - Siagian, Y.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2020/7/3
Y1 - 2020/7/3
N2 - The Batch Training method is one of the methods of Artificial Neural Networks that can be used to make predictions, especially in times series data. This method is able to make predictions by learning from data that has never happened before by forming the right network architecture model. Therefore, this research will discuss the best network architecture model that is appropriate for making predictions using the Batch Training method. The data used in this study is the data of Natural Disasters in Indonesia, sourced from the National Disaster Management Agency. There are 12 variables used, namely Time of disaster, Number of disasters, Death and missing victims, injured victims, victims suffering and displaced, seriously damaged houses, lightly damaged homes, submerged houses, damage to health facilities, damage to worship facilities, and damage to facilities education. Based on this data will be formed and determined the network architecture model used, including 4-5-1, 4-10-1 and 4-15-1. From these 3 models after training and testing, the best architectural model is obtained 4-10-1 with an accuracy level of 91% with MSE Training and testing values of 0.0245532940 and 0.0579265906.
AB - The Batch Training method is one of the methods of Artificial Neural Networks that can be used to make predictions, especially in times series data. This method is able to make predictions by learning from data that has never happened before by forming the right network architecture model. Therefore, this research will discuss the best network architecture model that is appropriate for making predictions using the Batch Training method. The data used in this study is the data of Natural Disasters in Indonesia, sourced from the National Disaster Management Agency. There are 12 variables used, namely Time of disaster, Number of disasters, Death and missing victims, injured victims, victims suffering and displaced, seriously damaged houses, lightly damaged homes, submerged houses, damage to health facilities, damage to worship facilities, and damage to facilities education. Based on this data will be formed and determined the network architecture model used, including 4-5-1, 4-10-1 and 4-15-1. From these 3 models after training and testing, the best architectural model is obtained 4-10-1 with an accuracy level of 91% with MSE Training and testing values of 0.0245532940 and 0.0579265906.
UR - http://www.scopus.com/inward/record.url?scp=85087775376&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1566/1/012032
DO - 10.1088/1742-6596/1566/1/012032
M3 - Conference article
AN - SCOPUS:85087775376
SN - 1742-6588
VL - 1566
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012032
T2 - 4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019
Y2 - 26 November 2019 through 27 November 2019
ER -