Lexicon-Based Approach for Sentiment Analysis of Technology Students in the City of Tegal toward AI Adoption in Learning - Dzulchan Abror; Angga Ardiansyah (J-INTECH ( Journal of Information and Technology), 2026)
This study aims to analyze the sentiment of technology students in Tegal City toward the adoption of Artificial Intelligence (AI) in learning. The explicit novelty of this research lies in its empirical focus on a localized tech-cluster student cohort within a regional urban area (Tegal City) who display uniquely high frequencies of digital-tool interaction, coupled with a specialized lexicon-based computational framework optimized for Indonesian academic linguistics. The research employs a descriptive quantitative approach combined with text mining techniques. Data were collected through an online survey involving 158 respondents using a questionnaire consisting of closed and open-ended questions. Sentiment analysis was conducted using a lexicon-based method to classify opinions into positive, neutral, and negative categories. The results indicate that students’ experiences with AI are dominated by positive sentiment (56.96%), reflecting its benefits in improving efficiency and understanding of learning materials. However, in terms of future expectations, there is an increase in neutral (44.94%) and negative (8.86%) sentiments, indicating a more critical perspective. The main concerns include overdependence on AI and the accuracy of AI-generated information. This study concludes that AI has significant potential to support learning, but its use should be balanced with the development of critical thinking skills and the improvement of reliable and accurate AI systems.