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Application of the Single Exponential Smoothing Method For Flood Disaster Prediction

Authors

  • Sukardi STMIK ADHI GUNA
  • Anisa Yulandari Anisa STMIK ADHI GUNA
  • Sri Khaerawati Nur Herha STMIK ADHI GUNA

DOI:

10.47709/cnahpc.v5i2.2455

Keywords:

Application; flood; prediction; rainfall data; single exponential smoothing

Dimension Badge Record



Abstract

The country of Indonesia is seen as one that is particularly vulnerable to natural catastrophes as well as calamities brought on by human activity. A disaster that may be brought on by both natural and human sources is a flood. Disasters brought on by flooding are unpredictable occurrences that frequently cause losses in the form of property damage, the theft of assets, and lost productivity at work and in school. Through this prediction information system, the people can find out the level of risk of flooding through excessive rainfall. in order to better anticipate and prepare for all possibilities that occur before the flood, the method used is Single Exponential Smoothing. This method was chosen because of the simple way the system works to find predictive values ??through past data. With this system, researchers can input rainfall data taken from the Meteorology, Climatology and Geophysics Agency, then the data is processed through the system and if rainfall gets high results. The risk of flooding will also be very high and a warning will be given to the public so that better prepared for the risk of flooding. The results obtained from this study are the results of an analysis of the exponential method single to obtain accurate rainfall prediction information with data MAD, MSE and MAPE.

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Author Biography

Sukardi, STMIK ADHI GUNA

 

 

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ARTICLE Published HISTORY

Submitted Date: 2023-06-20
Accepted Date: 2023-06-20
Published Date: 2023-07-15

How to Cite

Sukardi, S., Anisa, A. Y., & Herha, S. K. N. (2023). Application of the Single Exponential Smoothing Method For Flood Disaster Prediction. Journal of Computer Networks, Architecture and High Performance Computing, 5(2), 515-525. https://doi.org/10.47709/cnahpc.v5i2.2455