The rapid evolution of industrial robotics has been significantly influenced by the integration of computational data and social media, enabling robots to become more adaptive and responsive in collaborative work environments. This study investigates the role of social media and computational data in enhancing the adaptability of industrial robotics through machine learning techniques. By integrating sentiment analysis from social media with sensor data from industrial robots, this study examines how real-time data functions can improve robot decision-making and human-robot collaboration. Experimental results show a 23% increase in operational efficiency, an 89% accuracy rate in social interaction classification, and a 15% reduction in prediction errors. Furthermore, 62% of public sentiment toward adaptive robots is positive, highlighting the growing acceptance despite concerns over the impact of automation on jobs. These findings suggest that leveraging social media and computational data can significantly enhance the adaptability of robots, leading to a more efficient and socially conscious industrial ecosystem.

Fieldwork Practices were carried out at Pati National Vocational High School, from January 25, 2021 to February 20, 2021. In this Fieldwork Practice, the authors created an efficient application and management system for student data, especially for new students in a computerized manner. By using this web-based programming system, it can simplify data collection, automatic ranking based on National Exam scores, reduce paper usage, reduce cheating and are expected to overcome the obstacles experienced by both the committee for admission of new students and the parents of the students themselves. . This research produces a system that can handle the admission process of new students with the hope that it is more efficient and effective than the previous method.