<![CDATA[The news summary process is critical in the news analysis process. However, there are frequently barriers to the summary process, such as the large number of news articles and the requirement for news classification. The goal of this study is to develop a news summary and categorization model that will be extremely valuable in the news analysis process. Textrank is the suggested summarizing approach, and KNN will be utilized for news classification. The resulting model can be used to automatically summarize and group news, making content analysis easier. Sports news will be used as the study object from July to August 2023, and the supervised category will be used to identify whether the news comprises sports news in three branches, soccer, badminton / tennis, or basketball. Classification is carried out using the KNN algorithm by training the model using 500 categorized news data. Modeling using k = 3 and k = 5 shows that the precision is around 0.9866 and 0.9666 respectively. The model's implementation on unknown text demonstrates that the model can properly predict text categories as long as the news content falls into the three specified categories, but fails for news content that does not fall into these categories.]]>
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