Rainfall Classification Analysis Using Naïve Bayes Classifier Based on Air And Wind Temperatures in Serang City

Abstract

The city of Serang experiences relatively high annual rainfall, with an average total of more than 100 mm/year. Based on data obtained from BMKG, Serang City, in 2023, there will be a shift in the rainy season due to weather anomalies, affecting the amount of rainfall. Apart from that, Serang City is one of the cities with less rain throughout September, recorded within 15 days. In this case, a determination is needed in the form of rainfall classification. However, an exemplary method is required in order to classify rainfall so that the classification results are accurate. Several studies showed that the naïve Bayes classifier method is the best classification method compared to other analysis methods; namely, it only requires a probability. The parameters used are air temperature and wind speed. So, this research aims to determine the classification of rainfall using the Naïve Bayes classifier based on air temperature and wind in Serang City. The method used is non-experimental quantitative with naïve Bayes classifier analysis. Based on the data analysis using Microsoft Excel software, the results showed that in the Serang city area, February 2024 was classified as a humid month with rainfall of 100 - 200 mm, and March 2024 was classified as a dry month with rainfall <100 mm.