Palembang Aerodrome Weather Forecast for Palembang Sultan Mahmud Badaruddin II Airport

Authors

  • Isti Ma'atun Nasichah Program Studi Magister Teknik Informatika, Universitas Bina Darma, Palembang, Indonesia
  • Tri Basuki Kurniawan Program Studi Magister Teknik Informatika, Universitas Bina Darma, Palembang, Indonesia
  • Misinem Program Studi Teknik Komputer, Fakultas Ilmu Komputer, Universitas Bina Darma, Palembang, Indonesia

Keywords:

Weather Forecast, Random Forest, Naive Bayes Classifier, Support Vector Machine, Feature Selection

Abstract

SMB II Palembang Meteorological Station is one of the weather observation points owned by BMKG in charge of carrying out weather observations, analysis, and weather forecasts at Sultan Mahmud Badaruddin II Airport Palembang. Weather information has an important role in the world of aviation, so accurate airport weather forecasts are needed. Random Forest, Naive Bayes Classifier, and Support Vector Machine methods are classification methods used to forecast rain in this study. The data used is weather parameter data from December 2012 to December 2021. Rain forecast using the SVM method produces an accuracy rate of 72%, the NBC method 66%, and the Random Forest method produces an accuracy rate of 74%. Heavy rains and very heavy rains can’t be predicted accurately using the SVM, NBC, and Random Forest methods. Based on the feature selection method, the attribute that has the most influence on rain forecasts is the average humidity.

Published

2022-12-15

How to Cite

Nasichah, I. M., Kurniawan, T. B., & Misinem. (2022). Palembang Aerodrome Weather Forecast for Palembang Sultan Mahmud Badaruddin II Airport. Journal of Data Science, 2022. Retrieved from https://iuojs.intimal.edu.my/index.php/jods/article/view/57