Proceedings of the 5th International Seminar on Science and Technology (ISST 2023)

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Implementation of the Fuzzy K-Nearest Neighbor in Every Class (FKNNC) Method for Tsunami Disaster Potential Classification

Authors
Zainal Mu’arif1, *, Rais Rais1, Mohammad Fajri1
1Department of Statistics, Faculty of Mathematics and Natural Sciences, Tadulako University, Palu, Indonesia
*Corresponding author. Email: zainalmuarifstory@gmail.com
Corresponding Author
Zainal Mu’arif
Available Online 5 December 2024.
DOI
10.2991/978-94-6463-520-1_11How to use a DOI?
Keywords
Classification; FKNNC; Tsunami
Abstract

Tsunamis are sea waves generated by sudden disturbances in the ocean, resulting from changes in the seabed’s shape, causing sea water to rise to the surface. Almost every year earthquakes with the potential for tsunamis always occur in all countries in the world. No one knows when a tsunami will occur, but it is possible to predict with classification techniques the potential for tsunami occurrence through the application of science and technology. Through the data contained in historical earthquakes, there is information that can be analysis and processed so that it can determine the potential for a tsunami or not after an earthquake. Analysis and processing of earthquake history data can be done with the data mining process, namely classification techniques. Therefore, this study aims to classify tsunami disaster data based on historical earthquakes with variable attributes of earthquake depth, magnitude, and intensity. The classification technique employed in this study is Fuzzy K-Nearest Neighbor in Every Class (FKNNC) which is a method with the ability to consider the ambiguous nature of the data and give strength to the decision of a class because it has a degree of membership value. In this study using data divided into training and testing data. Obtained the best accuracy value is 90.47%, this accuracy value shows good performance in classifying potential tsunami disaster data. Thus, this research helps identify and provide information on potential tsunami disasters based on historical earthquakes that have occurred.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 5th International Seminar on Science and Technology (ISST 2023)
Series
Advances in Physics Research
Publication Date
5 December 2024
ISBN
978-94-6463-520-1
ISSN
2352-541X
DOI
10.2991/978-94-6463-520-1_11How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Zainal Mu’arif
AU  - Rais Rais
AU  - Mohammad Fajri
PY  - 2024
DA  - 2024/12/05
TI  - Implementation of the Fuzzy K-Nearest Neighbor in Every Class (FKNNC) Method for Tsunami Disaster Potential Classification
BT  - Proceedings of the 5th International Seminar on Science and Technology (ISST 2023)
PB  - Atlantis Press
SP  - 64
EP  - 68
SN  - 2352-541X
UR  - https://doi.org/10.2991/978-94-6463-520-1_11
DO  - 10.2991/978-94-6463-520-1_11
ID  - Mu’arif2024
ER  -