Comparative Analysis of Naïve Bayes Classifier, Support Vector Machine and Decision Tree in Rainfall Classification Using Confusion Matrix

The climate in Indonesia is sometimes unstable to this day. This unstable climate change will cause difficulties in predicting rainfall conditions. With unstable climate change, an algorithm is needed that helps the public predict rainfall conditions using rainfall, temperature and humidity parameters. The research process uses daily climate data from the Indonesia Climatology Agency with time span 2018 – 2023. The classification system using the Naïve Bayes Classifier (NBC) algorithm is less able to capture complexity and complex feature interactions with an accuracy of 97%-98%, Support Vector Machine (SVM) has an accuracy of 92%-94% and fewer prediction errors than NBC and Decision Tree which experienced overfitting especially when testing sets with 50% data with an accuracy of 99%-100%. Even though the Decision Tree shows the best performance, there is still a risk of overfitting so, SVM is a stable choice in this research.

@article{Berliana2024,
  title     = {Comparative Analysis of Naïve Bayes Classifier, Support Vector Machine and Decision Tree in Rainfall Classification Using Confusion Matrix},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Elvira Vidya Berliana and Mardhani Riasetiawan},
  doi       = {10.14569/IJACSA.2024.0150755},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150755}
}