PREDICTING PUBLIC ACCEPTANCE OF BIOTECHNOLOGICAL CROPS USING MACHINE LEARNING: DIGITAL INFORMATION EXPOSURE AND SUSTAINABILITY - Purnamadewi, Yeti Lis; Syahwani, Asty Khairi Inayah; Tanjung, Dahri; Ayuningtyas, Gilang (Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 2026)
The development of biotechnological crops plays a significant role in enhancing food security and sustainability in tropical regions. However, public acceptance remains a major challenge, particularly in the context of rapid digital information dissemination. This study aims to predict public acceptance of biotechnological products by integrating socio-cognitive factors such as knowledge, perception, digital information exposure, trust, and sustainability awareness. A cross-sectional survey employing purposive sampling was conducted with 500 respondents, drawn from diverse stakeholder groups including academics, government officials, entrepreneurs, farmers, and students. Data were analyzed using a machine learning approach comprising logistic regression, Random Forest, Gradient Boosting, and XGBoost algorithms, evaluated against metrics of accuracy, precision, recall, F1-score, and AUC-ROC. The results show that approximately 70% of respondents have limited knowledge of biotechnology, while about 75% agree that biotechnology can improve agricultural productivity, economic conditions, and food security. Furthermore, 76% of respondents indicate willingness to purchase biotechnological products. The predictive analysis reveals that knowledge and perception are key determinants of acceptance, while digital information exposure and sustainability awareness further strengthen positive attitudes. Individuals with greater access to reliable information tend to exhibit higher trust and a stronger likelihood of purchasing biotechnological products. These findings highlight the importance of strengthening digital communication strategies and sustainability narratives to improve public understanding and acceptance. This study contributes to tropical bioresource research by supporting data-driven decision-making within the One Health and sustainable agriculture framework.