Stunting is a public health issue that can adversely affect children's growth and future prospects. Stunting poses a threat to the achievement of the second global Sustainable Development Goal (SDG). One effort to address this problem is by providing knowledge to the community about stunting. This effort can be pursued through activities such as stunting seminars. A seminar was held in Kedungjambe Village on November 20, 2024, with the theme "United Against Stunting: Realizing a Golden Generation." The methods employed included observation, solution formulation and planning, implementation, and evaluation. The outcome of this community engagement activity showed high enthusiasm among seminar participants regarding stunting issues, accompanied by a solid understanding of the material presented.

The creation of the website of MaQis Alhamidy 4 Islamic Boarding School aims to provide an effective means of information and promotion to support the Qismart self-service store managed by the pesantren. In today's digital era, the existence of a website is very important for organizations, including Islamic educational institutions such as Pondok Pesantren MaQis Alhamidy 4. This website is designed not only to convey information about pesantren activities and services, but also to promote the pesantren self-service which serves as one of the main sources of income. Website development is carried out through several stages, namely observation, identification, implementation, and evaluation. The KKN Inovasi Pesantren Group 02 team, consisting of 16 members, was directly involved in this process. In the observation stage, the team observed and recorded the phenomena that occurred in the pesantren. Problem identification was conducted through interviews with pesantren administrators to understand the needs and challenges faced, especially related to the self-service information and promotion system. The implementation stage involved information collection and assistance in using the website as an information medium. Evaluation was conducted to reflect on the process and results achieved. The results of this project show that the developed website successfully created an effective platform to introduce and promote Qismart Supermarket to the community. The website facilitates access to important information and strengthens the brandi.

Purpose: Face recognition has become an increasingly intriguing field in artificial intelligence research. In this study,   This study aims to explore the application of CNNs, implemented through TensorFlow, to develop a robust model for enhancing facial recognition accuracy in student attendance systems. The focus of this research is the development of a model capable of recognizing student faces under various lighting conditions and poses in an academic environment, using a multi-class dataset of student images collected from internship attendance records at the Computer Science Department.
Methods: The dataset, comprising facial images from 19 students, served as the foundation for training and validating the CNN model. The dataset, sourced from the computer science department's internship attendance records, included a total of 231 images for training and 59 images for validation. The preprocessing phase included facial area detection and categorization, resulting in a well-organized dataset for training and validation. The CNN architecture, consisting of seven layers, was meticulously designed to achieve optimal performance.
Result: The model exhibited exceptional accuracy, reaching 93% on the validation dataset after 300 training epochs. Precision, recall, and F1-score metrics were employed for a detailed evaluation across diverse classes, highlighting the model's proficiency in accurately categorizing facial images. Comparative analyses with a VGG-16-based model showcased the superiority of the proposed CNN architecture. Moreover, the implementation of a web service demonstrated the practical applicability of the model, providing accurate predictions with a remarkable response time of less than 0.3 seconds.
Novelty: This comprehensive study not only advances face recognition technology but also presents a model applicable to real-world scenarios, particularly in student attendance systems.