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Wawan Joko Pranoto
Universitas Muhammadiyah Kalimantan Timur, Samarinda,Indonesia
Computer Science & IT

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The application of particle swarm optimization (PSO) to improve the accuracy of the naive bayes algorithm in predicting floods in the city of Samarinda Faldi Faldi; Trisha NurHalisha; Wawan Joko Pranoto; Hendra Saputra; Asslia Johar Latipah; Sayekti Harits Suryawan; Naufal Azmi Verdikha
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v6i3.148

Abstract

This study focuses on the implementation of Particle Swarm Optimization (PSO) to enhance the accuracy of the Naive Bayes algorithm in predicting floods specifically in the city of Samarinda. The aim is to improve the efficiency and precision of flood prediction models in order to mitigate the impact of flooding in the area. The results of this research highlight the effectiveness of PSO in optimizing the Naive Bayes algorithm, showing promising potential for more accurate flood prediction and proactive measures in Samarinda. The accuracy value obtained from testing using the Naive Bayes method alone is 91.12%. However, there is an improvement in accuracy after conducting testing with the optimization technique based on Particle Swarm Optimization (PSO) and the Naive Bayes algorithm. The conducted testing achieved an accuracy value of 94.38%. This accuracy result is higher compared to testing without optimization.
Co-Authors Asslia Johar Latipah Faldi Faldi Hendra Saputra Naufal Azmi Verdikha Sayekti Harits Suryawan Trisha NurHalisha