<![CDATA[Weather is an important factor that influences various sectors of life, such as agriculture, transportation, and community activities. North Sumatra Province has diverse weather characteristics due to differences in geographical conditions; therefore, analytical methods are required to identify weather patterns based on historical data. This study aims to analyze weather patterns in North Sumatra Province using the K-Means clustering method. The data used consist of 50 daily weather records, including air temperature, humidity, and rainfall parameters.The research stages include data collection, data preprocessing, determination of the number of clusters, implementation of the K-Means algorithm, and analysis of the clustering results. The number of clusters used is K = 3 to represent different weather patterns. The clustering results indicate that the cluster representing clear to partly cloudy weather with low rainfall is the dominant cluster, accounting for 40% of the data, followed by the cluster representing humid weather with relatively lower temperatures at 36%, and the cluster representing rainy weather with high humidity at 24%. These results demonstrate that the K-Means algorithm can effectively group weather data based on the similarity of their characteristics. The information generated is expected to support decision-making related to activity planning and weather analysis in North Sumatra Province.]]>
