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The development of discipline in children should begin at an early age, particularly in managing time for studying, playing, worship, and resting. Simple time management assistance activities are conducted to help children understand the importance of time regulation and to foster disciplined behavior in their daily activities. These activities include introduction sessions, material delivery, question-and-answer sessions, as well as the distribution of gifts and group photo sessions, all carried out in an interactive and participatory manner. The results indicate that children begin to organize their daily schedules, follow instructions, and demonstrate gradual improvements in discipline. The research method used is Participatory Action Research (PAR), which actively involves children, facilitators, and teachers/ustadzah in the stages of planning, implementation, observation, and reflection. This approach enables children to learn by doing, fostering a sense of responsibility, critical thinking skills, and the development of positive habits in a sustainable manner. The activity is also supported by the active participation of teachers/ustadzah and the facilitation team to enhance the effectiveness of the learning process; therefore, simple time management assistance based on the PAR method is effective in fostering discipline and orderliness in children from an early age.

Civic Education holds a strategic position within the national education system as the primary instrument for shaping the personality of citizens in alignment with the values of Pancasila. This research is motivated by the phenomenon of moral degradation and the fading spirit of nationalism among the younger generation due to the unfiltered influence of globalization. The primary objective of this study is to analyze the extent of Civic Education's role in the school environment in transforming student character to possess noble morals, broad national insight, and a deep love for the motherland. The research method employed is a literature study with a qualitative approach, where data were collected through the analysis of various curriculum documents, scientific journals, and textbooks relevant to the implementation of the independent curriculum. Research findings indicate that Civic Education is not merely a theoretical transfer of civic knowledge but rather an effective process of value internalization through behavioral habituation within the school environment. The integration of teaching materials with practical applications in school life is proven to increase students' awareness of their rights and obligations as citizens. The analysis also reveals that strengthening moral character and national insight highly depends on teacher creativity in delivering interactive learning that is relevant to the students' social reality. The implications of this research emphasize the importance of revitalizing Civic Education teaching methods to be more adaptive to modern developments without losing national identity. Schools are expected to serve as laboratories of democracy and centers for sowing patriotic values to produce a golden generation that is not only intellectually competent but also possesses strong moral integrity to maintain the integrity of the Republic of Indonesia.

The rapid development of Artificial Intelligence (AI) has brought significant changes to the criminal justice system, particularly in criminal investigations and evidentiary processes, while simultaneously raising complex legal and ethical challenges. Objective: This study aims to analyze the legal implications of the use of AI in criminal investigations, focusing on its benefits, risks, and challenges related to the admissibility of AI-based evidence, as well as the need for regulatory frameworks that ensure fairness, transparency, and accountability. Methods: This research employs a normative qualitative approach through the analysis of legal regulations, a review of legal and technological literature, and a comparative approach across jurisdictions, complemented by case studies of AI applications in law enforcement practices. Results: The findings indicate that AI enhances investigative efficiency through data analysis, crime prediction, and digital forensics; however, it also poses risks such as algorithmic bias, human rights violations, and issues concerning the reliability and transparency of evidence. Furthermore, differences across legal systems result in the absence of uniform standards for the admissibility of AI-based evidence. Therefore, adaptive regulatory frameworks grounded in the principles of fairness, transparency, and accountability are required, along with strengthened human oversight to ensure that the use of AI aligns with the principles of justice and human rights protection.

Abstract: The digital era demands that every individual possess visual literacy competencies, but in Islamic boarding schools (pesantren), access to technology is often limited to maintain focus on worship. The main problem at the Salafiyah Al-Furqon Islamic Boarding School in Sawangan, Magelang Regency, is the digital skills gap among 12th-grade students. This is caused by the boarding school's policy of only allowing mobile phone use during semester breaks, leaving students lacking practical multimedia skills prior to graduation. This community service activity aims to provide a solution through basic photography and videography training as a soft skill for alumni independence. The method used is a workshop that combines theoretical explanations and direct practice (experiential learning). The results of this activity show a significant increase in students' technical competency; participants are able to operate smartphone cameras with stable shooting techniques, understand the principles of visual composition (such as the rule of thirds and leading lines), and are able to perform basic video editing using mobile-based software applications. Through this training, Islamic boarding school graduates are expected to become individuals who are adaptive to technological developments and able to utilize visual media as a means of creative economics and digital da'wah after graduation.

The rapid development of information technology has had a significant impact on the world of education, particularly in the use of mobile applications as learning media. Developing Android-based applications using Flutter is one innovation that can increase efficiency and provide a more personalized and adaptive learning experience. In this context, educators are required to continue innovating to meet the increasingly diverse and dynamic needs of students in the digital era. The Multimedia and Information Technology Professional Association (PPMultindo), an organization that brings together technology professionals, is taking advantage of this opportunity by developing an Android-based learning platform. This platform is designed to provide easier, more flexible, and more interactive access to learning materials, as well as support the process of designing more engaging materials. Features such as learning data analysis, material recommendations, and automatic evaluation are expected to increase the effectiveness and efficiency of the teaching and learning process, while also providing a means for collaboration between members. However, the implementation of this technology still faces various challenges, such as limited technological competence of educators, inadequate infrastructure, and the need for curriculum adjustments. Therefore, collaborative efforts are needed to optimize the use of mobile technology to support educational transformation.

The rapid development of Artificial Intelligence (AI) technology requires an improvement in digital literacy and skills among students, particularly at the vocational high school level. However, many students still lack sufficient understanding of the basic concepts and practical applications of generative AI in learning. This Community Service Program (Pengabdian kepada Masyarakat/PKM) aims to enhance students’ understanding of AI technology through generative AI training at SMK Al-Muhajirin Depok. The methods used in this program include interactive lectures, demonstrations of generative AI applications, and hands-on practice sessions. The activity was attended by 30 students and supported by the school management. The results indicate a significant improvement in students’ understanding of generative AI concepts and their ability to apply the technology in a simple learning context. In addition, students gained awareness of the ethical use of AI in an appropriate and responsible manner. Therefore, this PKM activity contributes positively to improving students’ digital literacy and readiness to face technological advancements in the modern era.

Background: Automatic segmentation of the thyroid gland in ultrasonography (USG) images using deep learning requires a user-friendly interface to support diagnostic and educational processes. Purpose: This study aims to develop and implement a Graphical User Interface (GUI) that integrates a deep learning U-Net model for interactive and efficient segmentation and visualization of thyroid USG images. Method: The development method employed the Rapid Application Development (RAD) approach using MATLAB programming language. The GUI is designed to load transverse and sagittal USG images, display automatic segmentation results, and calculate thyroid gland volume based on dimensions measured automatically from the segmentation output. Testing was conducted using USG image data from 15 volunteers, and GUI functionality was evaluated using black box testing. Result: The GUI successfully displayed USG images and segmentation results with a responsive 4-panel interface; zoom, pan, and image navigation features functioned well. Automatic segmentation occurred in real-time after image input, and volume measurement results appeared automatically. Black box testing evaluation showed all GUI features operated as expected. The average Dice Similarity Coefficient (DSC) of 0.91 indicates high performance of the U-Net model in thyroid segmentation, consistent with previous findings. Statistical testing confirmed no significant difference between volume measurements using the application and manual methods (p = 0.953). Conclusion: This GUI implementation facilitates users in performing deep learning-based segmentation and visualization of thyroid USG images, improving efficiency and accuracy in thyroid volume measurement. The GUI has potential applications in clinical practice and radiology education.

Children in Tambak Osowilangun Village have high access to digital technology, but their digital financial literacy remains very low. Technology is generally used only for entertainment and social media without an understanding of its economic value. This condition is exacerbated by limited supervision from parents, the majority of whom work in the industrial sector, leaving children vulnerable to uncontrolled consumer behavior online. This community service activity aims to equip children in Tambak Osowilangun Village with a basic understanding of modern economics. The main focus is to improve their ability to manage their finances wisely, introduce the concept of digital savings, and develop critical thinking skills so they can distinguish between needs and wants in the technology era. The program uses the Participatory Action Research (PAR) method, which involves the active participation of children through a spiral of stages: planning, action, observation, and reflection. Data was collected through participant observation to monitor the learning dynamics of participants during the educational sessions. The implementation of the activity showed a significant increase in participants' financial understanding. Children became more aware of the risks of digital fraud and began to understand how financial applications work. Through interactive discussion sessions, high enthusiasm was generated, shifting the children's mindset from mere entertainment users to financially savvy technology users. The synergy between students and the community has succeeded in fostering a disciplined and responsible economic character in the young generation in the village.

Introduction: Additive Manufacturing (AM) has revolutionized the production of complex geometries, offering flexibility, customization, and precision across various industries. However, optimizing multiple process parameters simultaneously to enhance AM performance remains a significant challenge. This study focuses on improving both mechanical properties and surface quality by utilizing multi-objective optimization techniques. Literature Review: The research reviews existing approaches in AM optimization, highlighting the limitations of single-objective optimization and the potential of multi-objective evolutionary algorithms (MOEAs). Previous studies demonstrate the difficulty of balancing competing objectives, such as tensile strength and surface roughness, within AM processes. Materials and Method: This study employs NSGA-II, MOEA/D, and SPEA2 algorithms to optimize AM parameters like layer thickness, build orientation, and infill density. The optimization aims to improve mechanical performance, including tensile strength and impact resistance, while reducing build time and surface roughness. The methodology integrates experimental validation with computational predictions to evaluate the effectiveness of these algorithms. Results and Discussion: The optimization process yielded Pareto-optimal solutions that balanced mechanical strength and surface quality. The results demonstrated improvements in tensile strength and surface finish without significantly increasing build time. Trade-off analysis highlighted the inherent conflicts between mechanical performance and surface quality, allowing for better decision-making in industrial applications. The study contributes to the AM industry by offering a comprehensive optimization framework for improving both efficiency and product quality.

The development of digital technology has encouraged micro, small, and medium enterprises (MSMEs), particularly in the culinary sector, to adapt by utilizing online applications as a strategy to increase competitiveness. This community service activity aims to improve the understanding and capabilities of business owners at Kedai Jajanan Zahira in Surabaya in utilizing online applications as a marketing and sales tool. The method used in this study is Participatory Action Research (PAR), an approach that actively involves business actors in every stage of the process. These stages include problem identification, planning, implementation, and evaluation. Data collection techniques were carried out through interviews and direct observation to obtain a realistic picture of business conditions. The results of the activity showed an increase in business owners' understanding of the importance of digitalization, improved skills in using food delivery applications and social media, and increased market reach and interaction with consumers. In addition, the use of online applications also contributed to increased operational efficiency, such as more systematic order management and transaction recording. Overall, mentoring in the use of online applications has proven effective in increasing the competitiveness of culinary businesses. This activity has a positive impact not only on increasing sales, but also on business sustainability through increased digital literacy and the ability to adapt to technological developments. Therefore, ongoing mentoring is needed so that business actors can consistently optimize the use of digital technology in developing their businesses.

The development of information technology has become a primary pillar in enhancing corporate operational efficiency, where rapid and accurate data integration significantly determines business competitiveness. Case study in PT Gawi Anjung Bersama (GAB) revealed that the company is facing operational constraints because the management of delivery orders and invoices still relies on conventional office applications such as Microsoft Excel and Word. This procedure causes hurdles in data sharing among staff and results in low information security due to the absence of integrated access control settings. This research aims to design and develop the Gawi Anjung Bersama Information System (SIGAB), a web-based system implementing the CodeIgniter framework. The system is developed using Waterfall model, encompassing stages from analysis, design, and implementation to testing. The research flow starts with problem identification, primary and secondary data collection, and functional and non-functional requirements analysis. The system is designed using Unified Modeling Language (UML) modeling, specifically Use Case and Class Diagrams, as well as Entity Relationship Diagrams (ERD) for the database. The system interface is designed as wireframes on Figma web. This system separates access rights into three main actors namely owner, administrator, and staff, to ensure data security. Blackbox testing results, conducted after the system was hosted, indicate that all functional features operate as intended. The implementation of SIGAB is expected to optimize the management of delivery orders and invoices at PT GAB.

The internship program of Universitas Mayjen Sungkono in Begaganlimo Village introduced a training initiative focused on the Development of an Adaptive Learning Platform Based on D-PACK as an effort to enhance the quality of digital learning in Begaganlimo elementary schools. This training was designed to assist teachers in strengthening their ability to design and utilize interactive learning media supported by artificial intelligence (AI). Through this socialization activity, teachers were not only introduced to the use of adaptive technology but also guided to apply it in teaching practices so that the learning process becomes more effective, easier to understand, and more engaging for students. In addition, the socialization activities provided a deeper understanding of how technology can be used and integrated into teaching. Teachers not only learned to operate AI-based applications or devices, but they also gained insight into the pedagogical principles within the DPACK framework, which ensures that technology is applied appropriately. As a result, learning is expected to become easier to understand, more engaging, and capable of increasing students’ participation and motivation in the classroom.

This research is motivated by the escalating prevalence of illegal online lending practices in Indonesia, which generate a multitude of legal problems, particularly those concerning the validity of loan agreements and debt collection practices. The simplicity of access through smartphone applications, rapid processing times often approved within minutes and minimal administrative requirements have rendered these services immensely popular among the public. However, this accessibility also paves the way for unlicensed providers to operate unchecked, preying on desperate borrowers. The study aims to analyze the legal validity of illegal online loan agreements pursuant to the Indonesian Civil Code (KUHPerdata) and regulations issued by the Financial Services Authority (Otoritas Jasa Keuangan, OJK). Additionally, it examines the legal position of debt collection from a civil law perspective. A normative juridical method is employed, utilizing statutory and conceptual approaches, with qualitative analysis of the data. The results demonstrate that illegal online loan agreements fail to fulfill the requirements for a valid contract, especially regarding the legal capacity of the parties and lawful cause, categorizing them as null and void by operation of law. Nevertheless, in practice, unlicensed providers continue debt collection efforts, frequently employing methods that violate the law, such as harassment and intimidation. This reveals a significant gap between legal norms and field implementation. The implications emphasize the critical need for robust law enforcement, enhanced consumer protection mechanisms, and stricter oversight of fintech lenders to establish legal certainty and justice for society.

The development of digital technology has transformed interpersonal interaction patterns through online dating applications such as Tinder. Even though the use of Tinder in Indonesia has increased significantly, there are striking differences in communication styles between male and female users in managing these digital relationships. This study aims to analyze in depth the communication style of men as Tinder users in the digital dating search process. Using the framework of four main communication styles: emotive, director, reflective, and supportive style traits, this research explores how men navigate interactions that lack verbal cues and are asynchronous. The research method used is phenomenology by collecting data through participatory observation in the application and in-depth interviews with five male informants who are active Tinder users. The results of the study show that each informant applies different communication styles to build comfort and a positive self-image (selective self-presentation) to achieve their romantic goals. These findings identify that variations in men's communication styles are influenced by risk perception, privacy management, and self-presentation strategies in digital spaces. This research contributes to the literature on computer-mediated communication (CMC) in the context of online dating dynamics in Indonesia.

Litopenaeus vannamei is a brackish-water aquaculture commodity that is highly favored by the Indonesian community and represents one of the country’s leading aquaculture products, with production continuing to increase in response to rising domestic and export demand. One of the main factors triggering disease in L. vannamei is the presence of Vibrio sp. bacteria, which are recognized as serious pathogens in various aquaculture organisms and serve as causative agents of bacterial diseases. These bacteria naturally inhabit marine environments and are categorized as opportunistic, meaning they become pathogenic when environmental conditions decline and host health is compromised. Disinfectants are chemical substances used to inhibit or eliminate microorganisms such as bacteria, viruses, and fungi (excluding bacterial spores) on non-living surfaces. One disinfectant material is Smart Care (Intracare B.V., The Netherlands), which contains stabilized hydrogen peroxide with a prolonged-release mechanism. This study employed an experimental method using a Completely Randomized Design (CRD) with four treatments and five replications. The treatments included a control (without H₂O₂) and applications of H₂O₂ at concentrations of 1 ml/L, 2 ml/L, 3 ml/L, and 4 ml/L. The results indicated that the addition of hydrogen peroxide at different concentrations did not produce statistically significant differences in suppressing the growth rate of Vibrio sp. Nevertheless, treatment C (3 ml/L H₂O₂) showed the highest inhibitory effect, with no bacterial colony growth observed. Therefore, the application of H₂O₂ at 3 ml/L demonstrates potential in reducing Vibrio sp. growth and supporting sustainable shrimp aquaculture practices.

Prodeo legal aid is one of the ways in which the state fulfils its responsibility to guarantee access to justice for economically disadvantaged members of the public. However, its implementation still faces challenges, particularly budgetary constraints, meaning that not all applications for prodeo legal aid can be processed. This issue arises in Industrial Relations Court (PHI) cases at the Banjarmasin District Court. This study aims to examine the implementation of prodeo services in PHI cases and to analyse these budgetary constraints from a constitutional law perspective. The methodology employed is a normative legal approach with an empirical focus, utilising a review of legislation, interviews, observations, and a literature review. The research findings indicate that prodeo is a service for litigation at no cost, funded by the state through the State Budget Allocation (DIPA). In 2025, a budget of Rp. 33,728,000 was only sufficient to handle 13 cases, meaning the service depends on the availability of funds. This situation reflects a gap between the constitutional guarantee of access to justice and practice on the ground. Therefore, improvements are needed in budget planning, allocation, and management, as well as the strengthening of the role of Legal Aid Posts and Legal Aid Institutions to enhance access to justice for the underprivileged.

This study examines the application of innovation in public services based on digital technology as a strategic approach to improve the effectiveness, efficiency, transparency, and overall quality of government services. The digitization of service delivery through e-government initiatives, smart governance frameworks, and various applications such as SEPP, E-Lapor, JAKI, and OpenSID has been proven to accelerate administrative processes, simplify complex bureaucratic procedures, and significantly expand public access to services. In addition, these digital platforms contribute to more inclusive and user-oriented public service systems. The purpose of this study is to analyze the effectiveness of technology in improving service delivery and to identify strategic steps that can be taken to address the challenges associated with service innovation in the public sector. Qualitative research methods were applied, utilizing data collection techniques through an extensive literature review of relevant academic and policy sources. The findings illustrate that technology-based innovation not only supports improved service performance but also strengthens accountability, responsiveness, and public participation in governance processes.

As a priority program that utilizes a large portion of the state budget in its implementation, the Free Nutritious Meals (MBG) program that requires transparent and accountable financial management. This study examines transparency and state financial oversight in the implementation of the Free Nutritious Meals Program. It employs a qualitative approach based on a literature review of various policy sources, government reports, and relevant news coverage. The results of the analysis indicate that the government has made efforts to strengthen governance through the development of digital reporting mechanisms and by enhancing the capacity of program implementers via e-learning systems and financial reporting applications for a partners of the Nutrition Service Units (SPPG). Strengthening and greater transparency of information regarding the allocation and implementation of the program’s budget are needed to ensure public access, thereby realizing transparent and accountable public financial management in the implementation of the Free Nutritious Meals Program.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.