Application of K-Means Clustering in Public Opinion Analysis Based on Text Mining on Social Media - Karolus Doweng Koten; Richard Agung Orlando Berutu; Sardo Pardingotan Sipayung, (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
The development of social media platforms has made Twitter a crucial tool for understanding public opinions on various social and policy aspects. Analyzing patterns of public opinion on large and unstructured text datasets requires the use of efficient computational techniques. This study aims to explore the public views of Indonesian citizens on Twitter by applying text processing methods and k-means clustering. The data used in this research method consists of a collection of Indonesian-language tweets taken from a common dataset. The research process includes data collection, text preparation (including lowercase conversion, word separation, removal of common words, and stemming), and feature extraction through TF-IDF. Then, the k-means clustering algorithm is applied to group tweets based on the similarity of word patterns used. The results of this study show that this approach can create representative groups of opinions and help identify the main themes discussed on Twitter. These findings are expected to contribute to the study of the use of data mining and clustering techniques in social media-based public opinion analysis that has been used in the life of Indonesian society.