This paper presents a sentiment analysis of user reviews on the Uknow.AI platform, an AI-powered educational tool aimed at assisting students in solving mathematical problems through image recognition. The study utilized a Support Vector Machine (SVM) model to classify user reviews into positive, negative, and neutral sentiments. Data was collected from user reviews on platforms like Google Play, followed by pre-processing steps including tokenization and Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. The SVM model was evaluated based on performance metrics such as accuracy, precision, recall, and F1-score. The model achieved an accuracy of 93.42% with a recall of 99.70%, indicating robust performance in sentiment classification. Word cloud visualizations also highlighted dominant positive terms like "bagus" (good) and "membantu" (helpful), emphasizing the overall satisfaction with Uknow.AI's functionality. The study found that 92.6% of the reviews were positive, reflecting the tool’s effectiveness in enhancing student learning. Future research can focus on further improving user experience by addressing any shortcomings.

The development of the Fourth Industry is driving the manufacturing industry towards the era of smart manufacturing. In this transformation, various existing equipment, machines, processes, or devices are reinstalled with various sensors, computing, and other cyber-physical systems. In the current era of Industry 4.0, the automotive manufacturing industry continues to move towards sustainable development in a digital process framework so that real-time control and monitoring can be carried out. This study aims to reduce energy consumption and carbon emissions in the automotive manufacturing industry through the implementation of a PLC-based control and monitoring system. This system uses PLC and HMI for Industrial Fan control automation. The methodology used includes collecting energy consumption data through a power meter before and after system implementation, then analyzing the data to evaluate energy savings. The results of the study show that the system is able to reduce energy consumption by 20%, equivalent to savings of 122.66496 kWh per year, and reduce carbon emissions by 10.672 TCO2 per year. The implementation of this system supports energy efficiency and contributes to the achievement of the Sustainable Development Goals target.