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Introducing alphabetical sign language is necessary to bridge communication between deaf and hard-of-hearing people and their surrounding environment. This research aims to develop a sign language alphabet letter detection system based on American Sign Language (ASL). The research methods include data collection, feature extraction with OpenCV and Mediapipe, model development with Random Forest algorithm, and real-time system testing. The test results show that the developed system can achieve 97% prediction accuracy in recognizing hand patterns that represent ASL letters. The system uses a webcam as real-time input, providing accurate responses in various environmental conditions. This research contributes significantly to developing communication support technology for the deaf community, with implications for increased inclusivity and social engagement.

This study explores the role of sentiment analysis as a predictive tool for understanding and forecasting product launch success in the digital market. Sentiment analysis involves the classification of consumer sentiment expressed on social media platforms such as Twitter and Instagram, and it can significantly impact businesses by predicting consumer behavior and product performance. The research highlights the relationship between social media sentiment and product success, demonstrating that positive sentiment is strongly correlated with higher sales and consumer engagement, while negative sentiment can lead to declines. Machine learning models, including Support Vector Machines (SVM) and Random Forest, were employed to classify sentiment from large volumes of social media data and correlate it with product performance indicators such as sales volume and consumer interaction. The study found that sentiment analysis models were highly effective in predicting product success, with positive sentiment generally driving product profitability and negative sentiment posing a potential threat to brand reputation. Moreover, the analysis showed that social media sentiment provides real-time insights into consumer perceptions, enabling businesses to quickly adjust marketing strategies and product development plans. These findings underscore the importance of integrating sentiment analysis into product launch evaluations and strategic decision-making. Future research should explore the integration of sentiment analysis with other predictive market models and investigate the effects of fake reviews and post-purchase consumer behaviors on product success.