Application of Bayesian network model in determining the risk of building damage caused by earthquakes

Sari, Devni Prima; Rosadi, Dedi; Effendie, Adhitya Ronnie; Danardono,

doi:10.1109/icoiact.2018.8350776

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…A Directed Acyclic Graph (DAG) is the fundamental building block of the Bayesian Network construction. The Conditional Probability Table (CPT), the quantitative portion of the collection of conditional probability distributions for each variable based on the graph [13], [14], [15] is the following element. The Bayes theorem has the following generic form:…”

Section: Naïve Bayes Classifiermentioning

confidence: 99%

Determination of Bank Indonesia Scholarship Recipients Using Naïve Bayes Classifier

Febri,

Sari

2023

BAREKENG: J. Math. & App.

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A scholarship is a grant given to students as financial aid for education. One of the most sought-after scholarships is the scholarship from Bank Indonesia. Currently, the selection process for Bank Indonesia scholarship recipients still involves verifying the completeness of the administrative documents of the prospective recipients. Manual administrative verification requires a long time for data processing and re-verification. Therefore, there is a need for a data classification system to assist in the decision-making process for Bank Indonesia scholarship recipients. This study aims to implement the naïve Bayes classifier method to classify Bank Indonesia scholarships accurately. The variables used include gender, semester, parental income, grade point average (GPA), achievement, organizational activity, and the number of dependents. This research found that the naïve Bayes classifier method for classifying Bank Indonesia scholarship recipients can be done accurately with an accuracy rate of 86,84%.

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Section: Naïve Bayes Classifiermentioning

confidence: 99%

Determination of Bank Indonesia Scholarship Recipients Using Naïve Bayes Classifier

Febri,

Sari

2023

BAREKENG: J. Math. & App.

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“…BN has been developed in various fields, including in medical [3,4], chemical [5], financial [6], and technical field [7]. As well as Bayesian networking applications in terms of minimizing the risk of natural disasters: floods [8], tsunamis [9,10], earthquakes [11][12][13][14]. Disaster risk is essentially interesting to model, due to limited knowledge about when a disaster occurs.…”

Section: Introductionmentioning

confidence: 99%

K-means and bayesian networks to determine building damage levels

Sari¹,

Rosadi²,

Effendie³

et al. 2019

TELKOMNIKA

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Many troubles in life require decision-making with convoluted processes because they are caused by uncertainty about the process of relationships that appear in the system. This problem leads to the creation of a model called the Bayesian Network. Bayesian Network is a Bayesian supported development supported by computing advancements. The Bayesian network has also been developed in various fields. At this time, information can implement Bayesian Networks in determining the extent of damage to buildings using individual building data. In practice, there is mixed data which is a combination of continuous and discrete variables. Therefore, to simplify the study it is assumed that all variables are discrete in order to solve practical problems in the implementation of theory. Discretization method used is the K-Means clustering because the percentage of validity obtained by this method is greater than the binning method.

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“…In addition to everything else, the use of ARL in this research is related to seismic vulnerability in [1,13]. Various methods for predicting the level of damage to buildings were made in the works [14,15], one of which is the Bayesian network model. The assessment of the level of destruction is based on the Bayesian network and allows you to accurately establish a causal relationship between variables, and also reflects the relationship between states.…”

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confidence: 99%

International Journal of Electronics and Telecommunications

Wójcik

Karmenova

Smailova

et al. 2020

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Assessment of seismic vulnerability of urban infrastructure is an actual problem, since the damage caused by earthquakes is quite significant. Despite the complexity of such tasks, today's machine learning methods allow the use of "fast" methods for assessing seismic vulnerability. The article proposes a methodology for assessing the characteristics of typical urban objects that affect their seismic resistance; using classification and clustering methods. For the analysis, we use kmeans and hkmeans clustering methods, where the Euclidean distance is used as a measure of proximity. The optimal number of clusters is determined using the Elbow method. A decision-making model on the seismic resistance of an urban object is presented, also the most important variables that have the greatest impact on the seismic resistance of an urban object are identified. The study shows that the results of clustering coincide with expert estimates, and the characteristic of typical urban objects can be determined as a result of data modeling using clustering algorithms.

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Application of Bayesian network model in determining the risk of building damage caused by earthquakes

Cited by 6 publications

References 7 publications

Determination of Bank Indonesia Scholarship Recipients Using Naïve Bayes Classifier

Determination of Bank Indonesia Scholarship Recipients Using Naïve Bayes Classifier

K-means and bayesian networks to determine building damage levels

International Journal of Electronics and Telecommunications

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