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Purwati, Sintia Eka
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Computer Science & IT

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Journal : Sinkron : Jurnal dan Penelitian Teknik Informatika

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Model Random Forest and Support Vector Machine for Flood Classification in Indonesia Purwati, Sintia Eka; Yoga Pristyanto
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13973

Abstract

People, especially those living in lowland areas and along rivers. This flood phenomenon significantly affects various aspects, both in terms of economics, environment, and public safety. Flooding is a disaster that often causes problems for most people, especially those living in lowland areas and on riverbanks. This flood phenomenon significantly affects various aspects, such as the economy, environment, and community safety. This research compares the Random Forest and Support Vector Machine (SVM) methods for flood classification in Jakarta. The data used is flood data from 2016 – 2020 in Jakarta, obtained from Kaggle. Model performance evaluation is carried out using accuracy, precision, recall, and F1- Score metrics. The analysis results show that both models accurately classification floods, with Random Forest showing a more stable performance than SVM.
Co-Authors Yoga Pristyanto