Article Sidebar

Submitted
Jan 25, 2022
Accepted
Mar 21, 2022
Published
Sep 30, 2022
Download
pdf
Statistic
Read Counter : 266 Download : 127

Downloads

Download data is not yet available.

Main Article Content

Abstract

Disaster mitigation is a series of efforts to reduce disaster risk. One of the disaster mitigation efforts is the supervision of the implementation of spatial planning. Knowing the level of damage to buildings in a region in the event of a disaster can supervise the implementation of spatial planning. To predict the level of damage to buildings in an area, we can use the Bayesian network (BN). There are several types of BN based on the variable type; discrete, continuous, and hybrid BN. A discrete BN is a model in which all the variables involved are discrete. Therefore, if there is a continuous variable, it is necessary to discretize the variable. In this paper, modifications are made to the algorithm commonly used in the clustering process to be used in the discretization process. The algorithm used is the K-Medoids algorithm, where this algorithm uses existing data as a representative of the cluster center. Then, the BN model and the K-Medoids algorithm were used to determine the level of damage to buildings due to the earthquake that occurred in West Sumatra in 2009. From 25,000 house damage data used in this study, we obtain an accuracy rate is 95.17%.

Keywords

Mitigation, K-medoids, bayesian networks, earthquake, damage.

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
1.
Sari DP, Rosha M, Rosadi D. Disaster Mitigation Efforts Using K-Medoids Algorithm and Bayesian Network . EKSAKTA [Internet]. 2022Sep.30 [cited 2024Apr.25];23(03):231-4. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/304

References

  1. J. Han, M. Kamber, and J. Pei. (2011). Data Mining: Concepts and Techniques, 3rd ed. Morgan Kaufmann Publishers.
  2. D. Berrar. (2018). Bayes theorem and naive bayes classifier. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, vol. 1–3, pp. 403–412,
  3. S. Kabir and Y. Papadopoulos. (2019). Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review, Safety Science, vol. 115, pp. 154–175.
  4. D. P. Sari, D. Rosadi, A. R. Effendie, and Danardono. (2018). Application of Bayesian network model in determining the risk of building damage caused by earthquakes, in 2018 International Conference on Information and Communications Technology, ICOIACT 2018, 2018, vol. 2018-Janua.
  5. D. P. Sari, D. Rosadi, A. R. Effendie, and D. Danardono. (2021). Discretization methods for bayesian networks in the case of the earthquake, Bulletin of Electrical Engineering and Informatics, vol. 10, no. 1, pp. 299–307.
  6. D. P. Sari, D. Rosadi, A. R. Effendie, and Danardono. (2019). Disaster mitigation solutions with Bayesian network, AIP Conference Proceedings, vol. 2192.
  7. D. P. Sari, D. Rosadi, A. R. Effendie, and Danardono. (2019). K-means and bayesian networks to determine building damage levels, Telkomnika (Telecommunication Computing Electronics and Control), vol. 17, no. 2, pp. 719–727.
  8. A. L. Madsen, C. J. Butz, J. S. Oliveira, and A. E. dos Santos. (2022). Simple Propagation with Arc-Reversal in Bayesian Networks, Proceedings of Machine Learning Research, vol. 72. PMLR, pp. 260–271.
  9. S. García, J. Luengo, J. A. Sáez, V. López, and F. Herrera. (2013).A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 4, pp. 734–750.
  10. X. S. Yang. (2019). Introduction to algorithms for data mining and machine learning, Introduction to Algorithms for Data Mining and Machine Learning, pp. 1–173.
  11. P. Arora, Deepali, and S. Varshney. (2016). Analysis of K-Means and K-Medoids Algorithm for Big Data, Physics Procedia, vol. 78, pp. 507–512.
  12. R. Nagarajan, M. Scutari, and S. Lèbre. (2013). Bayesian Networks in R: with Applications in Systems Biology. Springer. New York.
  13. Y. Y. Bayraktarli and M. H. Faber. (2011). Bayesian probabilistic network approach for managing earthquake risks of cities, Georisk, vol. 5, no. 1, pp. 2–24.
  14. Y. Y. Bayraktarli, U. Yazgan, A. Dazio, and M. H. Faber. (2006). Capabilities of the Bayesian probabilistic networks approach for earthquake risk management. vol. 120, pp. 3320–3344.
  15. L. F. Li, J. F. Wang, H. Leung, and S. Zhao. (2012). A Bayesian Method to Mine Spatial Data Sets to Evaluate the Vulnerability of Human Beings to Catastrophic Risk, Risk Analysis, vol. 32, no. 6, pp. 1072–1092.
  16. L. F. Li, J. F. Wang, and H. Leung. (2010). Using spatial analysis and Bayesian network to model the vulnerability and make insurance pricing of catastrophic risk, International Journal of Geographical Information Science, vol. 24, no. 12, pp. 1759–1784.
  17. K. Kaur and R. K. Dhaliwal, Dalvinder Singh; Vohra. (2019). Statistically Refining the Initial Points for K-Means Clustering Algorithm, International Journal of Advanced Research in Computer Engineering & Technology, vol. 2, no. 11, pp. 2972–2977.