Implementation of Knowledge Graph as a Representation of Public Sentiment Analysis Toward AI-Generated Art - Finna Nur Nandia; I Kadek Dwi Nuryana (Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai, 2026)
The rapid advancement of generative AI technology has elicited diverse public responses on social media, particularly toward AI-generated art, which has significantly impacted Indonesia's creative industry. This study aims to analysed the sentiment of Indonesian society on platform X toward AI-generated art and to represent the interconnections among sentiment entities through a Knowledge Graph (KG). The approach integrates three primary methods within the Knowledge Discovery in Databases (KDD) framework: sentiment classification using the IndoBERT model, topic modelling using Latent Dirichlet Allocation (LDA), and KG construction based on Social Network Analysis (SNA). The dataset consists of 4,765 Indonesian-language tweets that underwent pre-processing. Sentiment analysis results indicate a dominance of negative sentiment (45.4%) over positive sentiment (42.5%), with the IndoBERT model achieving 69% accuracy on a three-class classification task. Topic modelling produced 18 distinctive topics (9 negative, 9 positive), validated through Two-Stage Similarity validation. Negative topics are dominated by issues of economic impact on illustrators, copyright infringement, and artistic style theft, while positive topics reflect appreciation for AI as a creative tool. The constructed KG comprises 144 nodes and 329 edges with a modularity score of 0.4556, reflecting 7 meaningful thematic communities. SNA evaluation reveals that 'ilustrasi' (illustration) is the most central entity (degree centrality = 12.621), while negative issues dominate the central positions among topic nodes. This study demonstrates that the integration of IndoBERT, LDA, and KG is capable of uncovering hidden relational patterns in public opinion that cannot be obtained through conventional sentiment analysis alone.