Sinkron : Jurnal dan Penelitian Teknik Informatika
Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024

Model Random Forest and Support Vector Machine for Flood Classification in Indonesia

Purwati, Sintia Eka (Unknown)
Yoga Pristyanto (Unknown)



Article Info

Publish Date
02 Oct 2024

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.

Copyrights © 2024






Journal Info

Abbrev

sinkron

Publisher

Subject

Computer Science & IT

Description

Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial ...