This research was conducted based on the problem of many students at elementary school CIBOGO who have not mastered the basic movements of floor gymnastics cartwheels, this research attempts to apply learning that can foster students’ interest and self-confidence in learning floor gymnastics cartwheels, the method used is to apply floor gymnastics cartwheel learning through rubber rope media. This research is a classroom action research with the research subjects of grade IV students of elementary school CIBOGO. The results of the study showed an increase in student learning outcomes after the implementation of learning through the rubber media from cycle I to cycle II. The results after implementing cycles I and II showed that student scores increased from the initial number of students who had completed only 17 students or 53.12% with the lowest score of 67.5. increased significantly to 28 students or around 87.5% of students who completed. And students who had not completed were 4 students or 12.5%. So it can be concluded that the method of learning floor gymnastics cartwheels through rubber rope media can improve student learning outcomes in learning physical education, health and sports, material floor gymnastics cartwheels.

Object recognition, particularly fruit classification, plays a crucial role in various fields, ranging from agricultural automation to digital marketplaces. This study proposes a fruit classification system based on RGB images, developed using a Convolutional Neural Network (CNN) architecture consisting of convolutional layers, pooling layers, fully connected layers, and dropout for model stability. The model was trained using the Adam optimization algorithm on an augmented dataset to enhance data variation and reduce overfitting. The resulting model achieved an average accuracy of 98%, demonstrating the reliability of CNNs in pattern recognition tasks. To enhance usability, the model was integrated into a graphical user interface (GUI) built with MATLAB R2023b App Designer, allowing users to add datasets, train the model, and predict new images without writing any code. The findings highlight that while the model performs well, its accuracy remains dependent on consistent image backgrounds; therefore, expanding the variety of fruit types and background conditions in the dataset is essential to improve the system's robustness in real-world applications.

Technology companies face the dynamic challenge of improving financial performance while meeting sustainability expectations. This research aims to analyze the contribution of ESG (Environmental, social, and governance) disclosure, corporate governance, operational efficiency, and the use of AI to the financial performance of technology companies. The research method uses a quantitative approach with multiple linear regression analysis, using secondary data from annual reports of technology companies listed on the IDX during 2018–2023. The research results show that ESG disclosure positively influences a company's reputation and access to capital. Good governance increases transparency and accountability, while operational efficiency and the application of AI have proven significant in optimizing productivity and innovation. In conclusion, these four factors support each other in creating added value and competitiveness for technology companies in the global market. These findings imply the importance of an integrated strategy in managing sustainability, operational and technological aspects to achieve sustainable financial performance.