<![CDATA[Precise rainfall classification is most important for meteorological forecasting and disaster risk mitigation, particularly in regions such as Yogyakarta, which are vulnerable to extreme weather events. Although previous studies have examined rainfall classification through the lens of meteorological variables, a notable lack of research has systematically evaluated the effectiveness of diverse machine learning algorithms for categorizing rainfall types within this specific locale. This study aims to rectify this gap by incorporating essential weather variables, specifically temperature, humidity, atmospheric pressure, and precipitation, into predictive models that utilize K-Nearest Neighbors (KNN), decision trees, and logistic regression techniques. Among the evaluated models, the decision tree demonstrated the highest degree of accuracy across both training and testing datasets. An examination of feature significance indicated that precipitation emerged as the most pivotal variable, aligning with the fundamental physical mechanisms associated with rainfall. This study contributes significantly to the evolving field of weather informatics by illustrating the utility of machine learning approaches in classifying regional rainfall. However, the parameters of this research are limited to specific meteorological variables and do not account for spatial or temporal variations, which could potentially influence the model’s broader applicability. Future research endeavors could augment this framework by integrating remote sensing data and methodologies for spatiotemporal modeling.]]>
