Program Pengabdian kepada Masyarakat (PKM) bertujuan untuk menganalisis dan mengeksplorasi implementasi pelatihan manajemen pengelolaan sedekah sampah rosok sebagai upaya untuk mendukung operasional Musolla Al-Ikhlas di Desa Guyangan, Kecamatan Godong, Kabupaten Grobogan. Konsep sedekah sampah rosok menjadi solusi kreatif dalam mengubah sampah yang selama ini dianggap sebagai limbah menjadi potensi ekonomi yang bermanfaat. Melalui program pelatihan ini, masyarakat desa atau jamaah musolla Al-Ikhlas diberikan pemahaman mengenai cara mengumpulkan, memilah, dan mengelola serta mmemasarkan sampah rosok (sampah non-organik seperti plastik, botol, kaleng, besi, kertas dan lainnya) agar dapat dijadikan sumber daya yang berguna. Sampah rosok yang terkumpul akan dijual untuk mendukung kebutuhan operasional musolla, seperti pembelian peralatan ibadah, renovasi, dan pemeliharaan fasilitas. Metode Pengabdian kepada masyarakat yang digunakan adalah kualitatif dengan pendekatan deskriptif, melibatkan berbagai elemen masyarakat, termasuk pengurus musolla dan warga sekitar. Pengumpulan data dilakukan melalui wawancara, observasi, dan dokumentasi selama kegiatan pelatihan. Hasil penelitian menunjukkan bahwa pelatihan ini berhasil meningkatkan kesadaran masyarakat tentang pentingnya pengelolaan sampah dan bagaimana pengelolaan sedekah sampah rosok dapat menguntungkan baik dari segi ekonomi maupun keberlanjutan lingkungan. Selain itu, pelatihan ini turut memperkuat semangat gotong royong dalam mengelola sumber daya lokal untuk mendukung kegiatan sosial keagamaan, dampak positif lainnya adalah pengurangan volume sampah yang berakhir di tempat pembuangan akhir (TPA). Dari hasil pelatihan ini, diharapkan masyarakat dapat secara mandiri mengelola sampah dan mengoptimalkan potensi sedekah sampah rosok untuk keberlanjutan operasional Musolla Al-Ikhlas. Secara keseluruhan, pelatihan ini diharapkan tidak hanya memberikan dampak positif pada aspek lingkungan dan ekonomi, tetapi juga memperkuat solidaritas sosial antarwarga desa dalam rangka menciptakan Musolla yang mandiri dan berkelanjutan. Kata Kunci: Pelatihan, Manajemen, Sedekah sampah rosok, Operasional Musolla

The COVID-19 pandemic is becoming an international threat. In order to overcome this, the Indonesian government has implemented a social distancing policy. However, the realization of the policies that exist to date has not run uniformly in relation to social distancing and social assistance that has not been received directly by the community. Therefore it is necessary to control the implementation process of policies that have been issued by the Government. This research is a normative legal research and empirical law with a normative juridical approach. The data obtained in this research is in the form of literature study and interviews. The data that has been collected is then presented in the form of descriptions which are arranged systematically following the flow of systematic discussion.

The exchange of information on the Internet requires increased protection to avoid potential threats to privacy and security. This study identified the main issues in this regard: the need for simple and effective tools for encoding and decoding messages, and the need to understand Base64 encoding algorithms and concepts. However, to overcome this problem the author developed an application to encode and decode messages/text using the Base64 algorithm and the Python programming language. This application allows users to send secret messages/text securely via and convert the data into Base64 format for secure transmission via text media. It also covers the basics of cryptography, Base64 algorithms, and how to use the Python programming language to develop secure applications. The result of this research is a simple and effective encryption and decryption application. This application provides a solution for users to protect messages or text when they want to change confidential information by converting it to Base64 format. With this application, you can send secret messages or texts with the confidence that only authorized parties can read them. Implementing message encryption and decryption using the Base64 algorithm using Python is an important step in maintaining message privacy and security in the current digital era. This research succeeded in developing an application suitable for this purpose. Therefore, the next step is to improve the security of your application by implementing stronger encryption algorithms. Additionally, we provide a more comprehensive user guide to help users better understand cryptographic concepts. Further research may focus on integrating applications with broader Internet security protocols to address increasingly complex security threats.

Education is one of the most important things for people to have. Many people are competing for education to increase their abilities. Technology plays a big role in developing access to education to make it easier with many online applications and online classes education becomes easier. However, there are still many unresolved problems in this field of education, namely the emergence of the phenomenon of incompatibility between educators and students so that student interest decreases dramatically because of this. And also, the lack of learning materials taught that are not school subjects such as programming. Therefore, the author and team designed an application where this application can find students a learning mentor outside of school so that they can increase their knowledge. The application also provides a matching feature based on the student's personality so that the student can find a suitable tutor.

The study aims to implement Data Mining with Apriori Algorithm and Association Methods (shop cart analysis) to analyze the sales pattern of Kaffa Beauty Shop stores as a case study. Sales information obtained from stores is used to find out the repeated buying habits of cosmetic products. This analysis provides store owners with valuable information to make more useful decisions about product inventory management, marketing strategies, and other aspects of their business. The Apriori Algorithm implementation follows steps including data preprocessing, subsetting, frequent dataset search, and strong association rules (strong Association Rules). The results of the analysis show that there are important purchasing patterns among some cosmetic products that can be the basis of a more effective sales strategy. The study helps understand how data mining and Apriori Algorithms can be applied in business contexts such as Kaffa Beauty Shop stores. Therefore, the results of this analysis are expected to contribute greatly to improving business efficiency and optimizing marketing strategies for store owners and stakeholders. The research is also expected to show the enormous potential of data analysis to support optimal business decision making.

Cyber-Physical Systems (CPS) are increasingly deployed in critical infrastructure yet remain vulnerable to adversarial attacks that manipulate sensor data to mislead AI-based decision-making. These threats demand not only high-accuracy detection but also transparency in model reasoning. This study proposes a Hybrid Explainable AI (XAI) Framework that integrates Convolutional Neural Networks (CNN), SHAP-based feature interpretation, and rule-based reasoning to detect adversarial inputs in CPS environments. The framework is tested on two simulation scenarios: industrial sensor networks and autonomous traffic sign recognition. Using datasets of 10,000 samples (50% adversarial via FGSM and PGD), the model achieved an accuracy of 97.25%, precision of 96.80%, recall of 95.90%, and F1-score of 96.35%. SHAP visualizations effectively distinguished between normal and adversarial inputs, and the added explainability module increased inference time by only 8.5% over the baseline CNN (from 18.5 ms to 20.1 ms), making it suitable for real-time CPS deployment. Compared to prior methods (e.g., CNN + Grad-CAM, Random Forest + LIME), the proposed hybrid framework demonstrates superior performance and interpretability. The novelty of this work lies in its tri-level integration of predictive accuracy, explainability, and rule-based logic within a single real-time detection system—an approach not previously applied in CPS adversarial defense. This research contributes toward trustworthy AI systems that are robust, explainable, and secure by design.

Banners are a simple yet highly effective promotional strategy for MSMEs. With attractive designs and strategic placement, banners can increase business visibility and attract more customers, thus contributing to local economic growth. The installation of banners as a promotional strategy for Micro, Small, and Medium Enterprises (MSMEs) has been proven effective in increasing product visibility and sales. The discussion in this Community Service activity report describes the activities to promote MSMEs that take place in the Sepinggan Baru village. So, to support these activities, visual media is needed that can help optimize the promotion of MSMEs by installing banners. Banners are designed in various sizes that can be attached to MSMEs, so that villagers who have MSMEs can easily place the position of the banners to be installed. KKN students of Sepinggan Baru Village who are banner instructors and help install banners and RT.17 community members are very enthusiastic and grateful for the banners we provide, because it makes it easier for them to promote their businesses. The results of this activity show that the objectives of the activity were carried out well and the residents of RT.17 Village Sepinggan Baru who have MSMEs were very appreciative.

The rapid advancement of deepfake technology has raised significant concerns regarding misinformation, privacy breaches, and digital fraud. Existing deepfake detection models, particularly those based on deep learning, often require high computational resources, making them unsuitable for real-time applications on mobile devices. This study aims to develop a lightweight deepfake detection model that enhances accuracy while maintaining computational efficiency. To achieve this, we propose a hybrid approach that integrates Fast Fourier Transform (FFT), MobileNet, and an Attention mechanism. The FFT component enables frequency-domain analysis to detect subtle deepfake artifacts, while MobileNet provides a lightweight convolutional backbone, and the Attention layer enhances feature extraction. The proposed model was evaluated on a benchmark deepfake dataset, and the results demonstrated its superior performance compared to the standard MobileNet model. Specifically, the model achieved an accuracy of 94.2%, an F1-score of 93.8%, and a computational efficiency improvement of 27.5% in comparison to conventional CNN-based approaches. These findings indicate that the integration of FFT and Attention mechanisms significantly enhances the model's capability to distinguish real and manipulated media while reducing computational overhead. The contribution of this study lies in presenting a deepfake detection model that balances accuracy and efficiency, making it suitable for deployment in mobile and resource-constrained environments. Future research should explore further optimization for energy efficiency, the adoption of lightweight Transformer architectures, and extensive testing on diverse datasets to improve robustness against real-world variations.

The aim of this research was to determine and assess the level of physical condition of male futsal extracurricular students at State High Schools in Batu City. This type of research uses a quantitative descriptive survey design, the variable studied is the level of physical condition. In this study, the sample taken from the entire population who took part in the men's futsal extracurricular at State High Schools in Batu City amounted to 90 students using a total sampling technique. The results of the physical condition of male futsal extracurricular students at SMAN Se-Kota Batu include the "Very Good" category showing a frequency of 7 students with a percentage of 8%, for the "Good" category showing a frequency of 19 students with a percentage of 21%, while the "Fair" category shows the frequency was 32 students with a percentage of 36%, while the "Less" category showed a frequency of 28 students with a percentage of 31%, and the "Very Less" category showed a frequency of 4 students with a percentage of 4%. The results of this research can be concluded that the physical condition of male futsal extracurricular students at Batu City State High Schools is dominant in the "Enough" category. Suggestions from researchers are that the training material be more varied and that students' physical condition should be paid more attention and needs to be improved.

This study aims to analyze the relationship between physical activity patterns and mood changes in adolescents using an objective measurement approach. This cross-sectional study involved 50 adolescent participants. Physical activity was measured using the ActiGraph GT3X accelerometer, while mood changes were assessed using the Brunel Mood Scale (BRUMS). Data were analyzed using Spearman’s correlation test with a significance level of p < 0.05. Findings indicate that participants spent most of their time engaged in sedentary behavior (M = 1194.42 minutes/day, SD = 42.543), while moderate-to-vigorous physical activity (MVPA) levels were relatively low (M = 47.06 minutes/day, SD = 17.873). Negative mood states were more dominant than positive ones, with the highest scores recorded for tension (M = 65.22, SD = 12.255) and anger (M = 63.06, SD = 10.033). Correlation analysis revealed no significant relationship between total MVPA and mood (r = -0.021, p = 0.886). However, sedentary behavior showed a significant negative correlation with moderate physical activity (r = -0.626, p < 0.01) and total MVPA (r = -0.635, p < 0.01), indicating that higher sedentary time was associated with lower levels of physical activity in adolescents. This study found no significant relationship between physical activity and mood changes in adolescents. The low levels of MVPA and high sedentary behavior may contribute to the dominance of negative mood states. Further longitudinal studies are needed to explore other factors that may influence adolescent emotional well-being.