Analisis Persepsi Publik Terhadap Pilkada Jakarta 2024 dengan Clustering dan Sentimen pada Artikel Berita - Zavira, Anggi Nur; Fathiyarahmani, Ilma; Nadhifa, Khansa; Putri, Kinanti Anindia; Mulyadi, Widya Amanda; Syawaliana, Zalfa (JDMIS: Journal of Data Mining and Information Systems, 2025)
This study analyzes the pattern of mass media coverage related to the 2024 Jakarta Gubernatorial Election using a text mining approach with the K-Means Clustering algorithm and Lexicon-based sentiment analysis. Data were obtained through web scraping from Google News RSS Feeds, resulting in 100 articles that were analyzed after undergoing preprocessing processes such as tokenizing, filtering, and stemming. The K-Means algorithm was used to cluster the articles into eight clusters based on dominant themes, such as political support, candidacy failures, and strategic issues related to the election. This algorithm works by calculating the distance between data points and centroids, which are continuously updated until an optimal cluster is achieved based on the Silhouette Score method. Sentiment analysis revealed that most articles had a neutral sentiment, reflecting media objectivity, although some clusters showed positive and negative sentiments, indicating potential bias in the coverage. These findings provide insights into the role of media in shaping public opinion and the influence of news coverage on public perceptions in the democratic process. This study is expected to enhance political literacy among the public and encourage more critical participation, while also opening opportunities for further development of analysis methods to better understand media bias