Visualisasi Data Analisa Sentimen RUU Omnibus Law Kesehatan Menggunakan KNN dengan Software RapidMiner - Tupari, Tupari; Abdullah, Syaukani; Chairani, Chairani (Jurnal Informatika: Jurnal Pengembangan IT, 2023)
The government's decision to discuss the RUU Omnibus law on health has become a controversial topic in society, especially among users of the Twitter social media platform. Users express their opinions regarding their stance on the RUU Omnibus law through tweets on Twitter. With diverse comments from users, it is essential to classify and visualize them into useful information about the positive and negative sentiments towards the RUU on health. This is crucial to understand the public's response to this policy. A total of 2406 sentiment data from Twitter users were collected using the RapidMiner software. Before analyzing the data using the K-Nearest Neighbors (KNN) algorithm, data preprocessing was carried out. After preprocessing, 2.406 data points were obtained, which were then divided into 1.684 tweets for testing and 722 tweets for training. The data was then processed using the KNN algorithm model executed in the RapidMiner software. The results of the data processing were presented in the form of tables, graphs, and word clouds, aligning with the research objective of providing clear and easily understandable visualizations about the RUU on health. This facilitates understanding for stakeholders without technical backgrounds to grasp the meaning and sentiments expressed. The research results indicate that the testing of K-Nearest Neighbors (KNN) yielded a high accuracy value, making it well-visualized at 84.58%. This indicates that the KNN model is highly successful in analyzing Twitter users' opinions on the Health Omnibus Law based on the data used and its ability to visualize effectively