This study investigates directive speech acts in Chapter 1 of the Japanese manga *Dandadan*, focusing on how characters use language to influence others’ behavior. The objective is to classify the types of directive utterances and analyze their pragmatic functions based on the speaker and context. A qualitative descriptive method was employed, using pragmatic theory to analyze speech data taken directly from dialogue panels. The analysis identified nine instances of directive speech acts, which include commands, prohibitions, invitations, and requests. Ayase Momo emerges as the character who produces the most directive utterances, predominantly in the form of commands and prohibitions, indicating her dominant and assertive communication style. Okarun, in contrast, tends to use polite requests and prohibitions, suggesting a more cautious and respectful approach. The findings reveal that the types of directive speech acts used are closely related to each character’s personality and the emotional context of the scene. These results underscore the role of speech acts in character development and narrative dynamics within manga. The study’s implication lies in showing how linguistic choices in manga dialogue reflect interpersonal power relations and emotional intensity. Future research is encouraged to explore directive speech acts across multiple chapters for broader insights.

Product packaging is an important element in creating the identity and attractiveness of a product, especially for small and medium enterprises (SMEs). This research aims to develop an online-based packaging design service through the PackDe platform that offers easy access, transparent pricing, and a variety of customizable design options. The methods used include literature study, competitor observation, and social media analysis to understand market needs and current design trends. The results show that PackDe is able to provide functional and aesthetic packaging design solutions, supporting SME product branding effectively. Nevertheless, challenges such as limited human resources and marketing need to be overcome to reach a wider market. The implications of this research show that digital-based packaging design innovation can improve the competitiveness of SME products in national and global markets. PackDe is expected to be a strategic partner for SMEs in creating packaging that is not only visually appealing but also has added value to the packaged product.

Facial Emotion Recognition (FER) is a key technology for identifying emotions based on facial expressions, with applications in human-computer interaction, mental health monitoring, and customer analysis. This study presents the development of a real-time emotion recognition system using Convolutional Neural Networks (CNNs) and OpenCV, addressing challenges such as varying lighting and facial occlusions. The system, trained on the FER2013 dataset, achieved 85% accuracy in emotion classification, demonstrating high performance in detecting happiness, sadness, and surprise. The results highlight the system's effectiveness in real-time applications, offering potential for use in mental health and customer behavior analysis.