Sentiment Analysis of BMKG Weather Information Service Using K-Nearest Neighbor Method

The Meteorology, Climatology, and Geophysics Agency (BMKG) is a government institution that provides information related to air quality, climate, dry days, satellite imagery, wave forecasts, wind forecasts, and fire potential. This information is disseminated not only through BMKG's official website but also via the social media platform X, making it easier for the public to access up-to-date information. This study aims to classify user sentiment towards the weather information services provided by BMKG using the K-Nearest Neighbor (KNN) method. Data was collected through a web crawling technique, resulting in 1,031 data points analyzed in this research. The data processing stages included Pre-Processing and sentiment calculation using Vader's Sentiment and Random Forest. The classification results using the KNN algorithm showed an accuracy rate of 96%, with an average precision of 96%, an average recall of 96%, and an average f-measure of 96%. These findings indicate that the KNN model can effectively classify user sentiment towards BMKG's services.