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This community service activity aims to improve digital literacy among children and adolescents in Telang Sari Village through basic computer training and networking introduction. The main problem faced by the community is the low level of technological literacy caused by limited facilities and lack of access to structured computer education. This program was implemented during the Thematic Community Service Program (KKNT) by applying participatory and practical training methods. The activities included introduction to computer hardware and software, basic typing skills, document creation, internet literacy, and simple LAN configuration practices for junior high school students. The results show a significant improvement in participants’ understanding and confidence in using computers and basic networking concepts. Children and adolescents who previously had minimal exposure to technology were able to operate computers, create simple documents, and understand responsible internet usage. The program also contributed to increasing students’ motivation to learn technology and preparing them to face digital challenges. Overall, this activity demonstrates that basic computer training with hands-on practice can effectively enhance digital literacy in rural communities.

Green innovation has become a crucial approach to addressing sustainability challenges within global economic and environmental contexts. This study maps the development of green innovation research through a bibliometric analysis using data from the Scopus database covering the period 2021–2024. Bibliometric techniques were applied with VOSviewer and R Studio to examine publication trends, citation patterns, author collaboration, and keyword networks. The results show a significant growth of publications in the last five years, with major themes focusing on sustainable development, environmental technology, and economic implications of green innovation, while leading contributions come from China. Influential journals in innovation and environmental management are identified as key publication outlets, and keyword analysis reveals the integration of green innovation into sustainability strategies and economic policy discussions. This study contributes to a clearer understanding of the intellectual structure and emerging directions of green innovation research, offering insights for scholars, business practitioners, and policymakers in advancing sustainable innovation practices.

Rapid developments in the era of digital transformation, which refer to the emergence of business and technology innovations based on artificial intelligence, big data, and the Internet of Things (IoT), have great potential for strategic sustainability for businesses in the digital age. Efforts to transform digital business models as a global competitive advantage and provide outputs that can be oriented towards future predictions. Digital business models refer to strategic designs for creating platform networks that are implemented through relationships with consumers and cross-sector collaboration. Challenges and opportunities for development between transformation and innovation are necessary in order to create and capture competitive value and provide added value in the digital economy era. The use of bibliometric analysis in research provides direction in understanding the perspectives and issues that require further research, opens up space for exploring publication trends, and identifies the mapping of key concepts that form the basis of main ideas, thereby providing a more structured understanding and developing new research opportunities, especially in the field of digital business models. Bibliometric analysis aims to gain an in-depth understanding of research using the R studio application as a tool for processing data trends over time and VOSviewer as a knowledge map visualization tool. The research was conducted to provide an understanding of current and future developments in a dynamic environment.

This research examines the influence of road infrastructure development on local economic growth in Medan City, emphasizing its role in improving accessibility and supporting economic activities. Road infrastructure is a vital component of regional development, as it facilitates the movement of goods and services, reduces transportation costs, and enhances connectivity between economic centers. Well-developed road networks can stimulate business expansion and encourage investment by creating a more efficient economic environment. The study employed a descriptive quantitative approach using field surveys involving 20 respondents, including business actors, local residents, and government representatives. Data were collected through structured questionnaires and analyzed descriptively to capture perceptions regarding the economic impact of road infrastructure development. The results indicate that improvements in road quality, capacity, and connectivity have a positive influence on local economic growth. Respondents reported increased trade activities, improved access to markets, higher investment opportunities, and rising community income levels as direct benefits of improved road infrastructure. However, the study also identified several challenges that may hinder long-term economic outcomes. Traffic congestion, uneven infrastructure development across areas, and inadequate routine maintenance were highlighted as significant issues. These problems can reduce efficiency and limit the sustainability of economic gains. Therefore, continuous government commitment, effective maintenance strategies, and equitable distribution of infrastructure development are essential. Strengthening coordination among stakeholders is also necessary to ensure that road infrastructure development supports inclusive and sustainable local economic growth in Medan City.

End-to-end autonomous driving has emerged as a promising paradigm in which deep neural networks directly map raw visual inputs to continuous control actions. Despite its effectiveness, this approach suffers from limited transparency, posing significant challenges for deployment in safety-critical driving scenarios. This study addresses the lack of interpretability in vision-based end-to-end autonomous driving systems and aims to analyze model decision-making behavior under critical conditions such as sharp steering maneuvers and abrupt control transitions. To this end, an explainable end-to-end autonomous driving framework is proposed, combining a convolutional neural network trained via imitation learning with gradient-based visual attribution techniques, including Grad-CAM. The model predicts continuous steering, throttle, and braking commands directly from front-facing camera images, while explainability mechanisms are applied to reveal input regions influencing each control decision. Model performance is evaluated using both prediction accuracy and safety-oriented behavioral metrics. Experimental results show that the proposed explainable model achieves lower control prediction errors compared to a baseline end-to-end CNN, reducing steering mean squared error from 0.034 to 0.031, throttle error from 0.021 to 0.019, and brake error from 0.018 to 0.016. Moreover, safety-oriented analysis indicates improved driving stability, with steering variance reduced from 0.087 to 0.072 and abrupt control changes decreased from 14.6 to 10.3 events. Visual explanations consistently highlight road surfaces and lane-related structures during complex maneuvers, indicating reliance on semantically meaningful cues. In conclusion, the results demonstrate that integrating explainability into end-to-end autonomous driving not only preserves predictive performance but also correlates with smoother and more stable driving behavior. This framework contributes to the development of transparent and trustworthy autonomous driving systems suitable for safety-critical applications

The development of science and technology has encouraged the utilization of graph theory in solving optimization problems, particularly in transportation systems and tourism route planning. Medan City, as a metropolitan area with dense road networks and widely dispersed tourist destinations, faces challenges in selecting efficient travel routes. This research aims to determine the optimal route between tourist destinations in Medan City using the Minimum Spanning Tree (MST) method with Prim’s Algorithm. The research was conducted using a weighted graph modeling approach, where each tourist destination is represented as a vertex and the distance between destinations is represented as an edge weight. Distance data and estimated travel time were obtained through digital mapping using Google Maps and then analyzed through iterations of Prim’s Algorithm to produce a minimum spanning tree without forming cycles. The results show that all 23 tourist destinations are successfully connected in a single MST structure with a minimum total distance of 68.97 km and a travel time of approximately 199 minutes or 3 hours and 19 minutes. This model is expected to serve as a reference for tourism planning and support urban transportation efficiency based on mathematical computation.

The limited number of studies examining organizational communication patterns within technical government institutions a research gap, particularly in understanding how communication mechanisms are implemented by public relations units in technical based governmental organizations. This research aims to identify the communication patterns applied by the Public Relations Unit of the Geological Survey Center (PSG) in Bandung, as well as the supporting factors and challenges encountered in its implementations. The study employs a qualitative descriptive approach wih data collected through interviews, observations, and literature review. The findings reveal that the organizational communication pattern operates in multiple directions using both formal communication channels, such as official letters and scheduled meetings through the Nadine system, and informal channels, including face-to-face interactions and digital messaging through WhatsApp. Supporting factors in the communication process include leadership support, a well-defined organizational structure, a collaborative work culture, and the availability of adequate communication facilities and media. However, several constraints remain, including technical and non-tehcnical challenges such as varying work priorities, inconsistents responses times, and unstable network connectivity during field activities.

Village entrepreneurship is an initiative designed to stimulate regional economic growth by accelerating the development of rural areas, which have long been perceived as slow, less productive, and lagging behind urban centers. The creation of entrepreneurial villages offers a concrete strategy to enhance community productivity because it encourages all components of the village government, residents, and local institutions to participate in structured and collaborative entrepreneurial activities. Such villages are expected to generate new economic opportunities, strengthen local industries, and increase community independence. However, initial observations from this Community Service program show that partner villages still face significant barriers, including limited land area, geographical remoteness, and inadequate human and natural resources. Many peripheral villages have not yet utilized digital technology effectively, making it difficult to expand markets or access broader economic networks. In addition, challenges related to financial access, limited talent development, socio-cultural constraints, and weak market infrastructure continue to hinder economic progress. These conditions demonstrate the urgent need for strategic assistance, capacity-building programs, and stronger support systems to help villages transform into sustainable entrepreneurial ecosystems capable of contributing to long-term regional development.

This community service program aims to provide management assistance to micro and small batik industries in optimizing digital platform and social media-based marketing. This allows these micro and small batik industry players to reach a wider network and ensure their business sustainability in the future after the pandemic. This community service program uses a qualitative descriptive approach, which aims to capture the real-world conditions related to the management of batik industry players in Mojokerto. This mentoring program was attended by micro and small batik industry players, primarily small and craft industries, including 10 batik artisans and industry managers residing in Mojokerto. The mentoring method was implemented through four stages: a) identifying mentoring needs for micro and small batik industry players, b) selecting management literacy priorities, c) designing a management literacy mentoring program, and d) implementing the management mentoring program for micro and small batik industries. The management mentoring program indicates that micro and small batik industry players require ongoing marketing and business development management mentoring. After entering the COVID-19 pandemic, batik industry players in Mojokerto have not been able to recover to pre-COVID-19 levels in 2019. The management mentoring program includes four stages: a) delivering material on the role of management, business sustainability, business problems, and formulating solutions; b) identifying relevant management problems discussed in a focus group discussion; c) mentoring on marketing aspects and optimizing the role of cooperatives within batik industry groups; d) mentoring on business sustainability management through digital business optimization. Through this structured management mentoring program, micro and small batik industry players will be able to improve their management and business management.

Although Experience Quality has been extensively researched, comprehensive studies remain limited. This research aims to conduct a Systematic Literature Review (SLR) and bibliometric analysis of existing empirical work on Experience Quality. The methodology used is SLR, with data collected from the Scopus database using the search term "Experience Quality" in the title, abstract, and keywords. This search yielded 1,297 publications from 1965 to 2025 (evaluated on October 24, 2025). The data were analyzed bibliometrically using VOSviewer. The findings confirm that experience quality has a significant effect on brand perception and customer satisfaction, which consequently impacts customer loyalty. Practically, this implies that brand and service managers must treat experience quality as a strategic variable, as good products or services alone are insufficient for long-term loyalty. Socially, positive experiences are shown to strengthen social networks, increase community involvement, and promote inclusion. This research is limited by its reliance solely on the Scopus database; future research should integrate additional databases, such as Web of Science, for broader generalization. Despite the global development of this topic, in-depth, structured studies are scarce. This research is valuable and original as it provides a comprehensive summary and a research roadmap through SLR and bibliometric analysis.

Soft skills are essential competencies that teachers must possess to create an effective, communicative, and student-centered learning process. However, the geographical conditions of SD Negeri 01 Saonek, which is located in an island area, present unique challenges, particularly related to limited access to travel outside the island and unstable communication networks. These limitations have resulted in teachers at the school not receiving adequate training in soft skill development, including communication, classroom management, and self-reflection abilities. To address this need, a socialization and training program based on microteaching was implemented as an effort to enhance teachers’ professional competencies. The microteaching method was chosen because it provides opportunities for direct practice, structured feedback, and simplified yet effective teaching simulations. The training was designed to help teachers understand good teaching techniques, build confidence, and strengthen interpersonal skills in instructional interactions. In addition, the collective feedback sessions offered participants chances to learn from one another and improve their teaching strategies based on peer observations. The results of the activity indicated an improvement in teachers’ ability to design lesson scenarios, manage classrooms, deliver material more clearly, and communicate more effectively with students. Teachers also reported increased motivation and a deeper understanding of the importance of soft skills in supporting their professionalism. Thus, the microteaching training provided new insights into the capacity-building process for teachers at SD Negeri 01 Saonek and served as a relevant solution for island schools facing limited access to conventional training. This program is expected to continue and become a model for empowering teachers in 3T regions to sustainably improve education quality.

Electronic Medical Record (EMR) is an essential component of hospital digital transformation aimed at improving administrative efficiency and service quality. This study aims to analyze the factors influencing the implementation of EMR in outpatient registration at a private hospital in North Jakarta. A mixed-methods design was employed, combining quantitative data collected from questionnaires completed by 30 registration staff with qualitative data obtained through structured interviews and direct observation. The logistic regression analysis revealed that Training and Staff Competence (X2) had a significant positive effect on outpatient registration (OR 30.663; p=0.040), while System Integration and Data Security (X4) also showed a significant effect (OR 15.121; p=0.047). In contrast, Technological Infrastructure Readiness (X1) was positive but not significant (p=0.112), and Organizational Culture and Managerial Support (X3) was negative and not significant (p=0.954). Simultaneously, the model explained 72.1% of the variation in registration effectiveness (Nagelkerke R²=0.721) with a classification accuracy of 93.3%. Qualitative findings supported the quantitative results, highlighting insufficient staff training, persistent technical issues in BPJS system bridging, and difficulties faced by elderly patients in adapting to digital registration. Elderly patients still required staff assistance and simple educational media such as tutorial videos displayed in hospital waiting areas. This study concludes that staff competence and system integration are the key determinants of successful EMR implementation in outpatient registration. It is recommended that hospitals strengthen continuous training programs, improve network and server stability, and expand patient education initiatives to ensure effective, efficient, and patient-friendly digital services.

The purpose of this scientific paper is to design and implement a knowledge-based expert system to assist students in determining their major specializations at Vocational High Schools (SMK) more objectively and accurately. A major problem frequently faced by SMK students is the mismatch in department selection caused by a lack of understanding regarding their own potential and the specific criteria of each competency. To address this issue, the system was developed using the Forward Chaining method as the inference engine. This method operates in a data-driven manner, beginning with the collection of facts such as interests, academic abilities, and psychological aspects, which are then matched against a Knowledge Base consisting of IF-THEN logical rules. Based on system testing results, the inference engine is capable of processing the list of facts to generate a specialization diagnosis for SMK students and provide the most relevant departmental recommendations, such as Computer and Network Engineering (TKJ), Accounting, Office Automation and Governance (OTKP), or Marketing. The research results indicate that the implementation of the Forward Chaining method is effective in accelerating the decision-making process and minimizing placement errors. This expert system is expected to serve as a tool for Guidance and Counseling (BK) teachers in providing appropriate career direction for students based on structured empirical data.

Background: Mental health during pregnancy is influenced by multiple factors, including the social environment. Family support, peer interactions, and community engagement are critical determinants of psychological well-being, yet their impact local community health settings remains underexplored. Objective: This study aimed examine the influence the social environment the mental health pregnant women Tilongka Billa Community Health Center. Methods: A quantitative correlational study was conducted with 70 pregnant women selected through purposive sampling. Data were collected using structured questionnaires assessing demographic characteristics, social support (family, peer, and community), and mental health status (Perceived Stress Scale and Edinburgh Postnatal Depression Scale). Descriptive statistics summarized the participants’ characteristics, while Pearson Spearman correlation tests analyzed relationships between social environment factors mental health outcomes (p < 0.05). Results: The results indicated that family support had the highest mean score (4.1 ± 0.7), followed by peer support (3.8 ± 0.8) and community involvement (3.5 ± 0.9). Mental health assessments revealed moderate stress levels (18.2 ± 5.0) and mild depression risk (9.5 ± 4.3). Correlation analysis showed significant negative relationships between social support and mental health indicators, with family support exhibiting the strongest correlation with reduced stress (r = -0.48, p = 0.002) and depression (r = -0.52, p = 0.001). Conclusion: The study demonstrates that supportive social environment, particularly family and peer support, plays crucial role in reducing stress and depressive symptoms among pregnant women. Strengthening social support networks through counseling, peer groups, and community engagement programs may enhance maternal mental health and promote positive pregnancy outcomes.

This Community Service (PkM) activity focuses on the implementation of Quail Cultivation Counseling as a strategic effort to advance entrepreneurship among the Millennial Farmer Generation in Krebet Hamlet, Srengat, Blitar. This activity was motivated by the high economic potential of Quail and the need to improve the entrepreneurial competence of the younger generation in the livestock sector. The main goal of the program is to transform traditional cultivation into a structured, efficient, and sustainable modern business unit. The implementation method combines theoretical knowledge transfer with practical assistance in the field. The counseling material includes three pillars: 1) Business Scale Management, including capital management and strategies for increasing livestock population; 2) Quality Management and Livestock Health, emphasizing cage sanitation, feed, and quality vitamins; and 3) Marketing strategies to expand the distribution network. The results of the activities showed high effectiveness. Quantitatively, 80% of Millennial Farmer participants succeeded in implementing post-extension livestock population increases. Qualitatively, this activity fosters awareness, motivation, and a strategic entrepreneurial mindset so that participants are able to face production and market challenges. With this strategic practice, quail businesses are expected to increase profitability and competitiveness. This PKM serves as a catalyst in supporting economic independence and entrepreneurial development of the younger generation in the agribusiness sector.

The old bridge structure, are subjects that must be monitored in bridge maintenance. Bridge detection supervision is very necessary to determine structural deformation caused by normal operations or environmental impacts such as temperature, humidity and heavy vehicle loads. Monitoring the structure as a whole also needs to be carried out after extreme conditions occur, such as an earthquake. Research on structural dynamics analysis of steel frame bridge conditions to determine the behavior of the structure. Vibration data is read using a wireless sensor network or accelerometer. The vibration mode signal obtained is processed using Fast Fourier Transform (FFT) analysis via MATLAB software, which will produce a graph of the relationship between the frequency domain and the time domain. Through this graph, the dynamic characteristics of the bridge structure can be analyzed, with several variations in the condition of the bridge structure.

Law enforcement against narcotics crimes in Indonesia faces various complex problems, both from legal aspects, institutional structure, and community legal culture. Even though Law Number 35 of 2009 concerning Narcotics has been enacted, the circulation and abuse of narcotics is still increasing every year. This study aims to analyze problems in law enforcement against perpetrators of narcotics crimes and identify the factors that affect them. The research method used is normative juridical with a legislative approach and a case approach. The results of the study show that the main obstacles lie in the lack of coordination between law enforcement agencies, weak integrity of the apparatus, overlapping regulations, and low public legal awareness. In addition, the limitation of supporting facilities and infrastructure such as rehabilitation facilities, forensic laboratories, and early detection technology also hinders the effectiveness of case handling. The high modus operandi of increasingly sophisticated narcotics networks is also a challenge for the authorities. Therefore, strengthening regulations, increasing the capacity of human resources, utilizing modern technology, and more intensive collaboration between agencies need to be optimized as a comprehensive effort to strengthen the effectiveness of law enforcement in the narcotics sector.

The use of Search-Based Software Engineering (SBSE) for optimizing software architecture has evolved from fully automated to interactive approaches, integrating human expertise. However, current interactive tools face limitations: they typically support only single decision-makers, confine architects to passive roles, and induce significant cognitive fatigue from repetitive evaluations. These issues disconnect them from modern, team-based software development, where collaboration and consensus are crucial. To address these shortcomings, we propose "ArchEvolve," a novel framework designed to facilitate collaborative, multi-architect decision-making. ArchEvolve employs a cooperative coevolutionary model that concurrently evolves a population of candidate architectures and distinct populations representing each architect's unique preferences. This structure guides the search towards high-quality consensus solutions that accommodate diverse, often conflicting, stakeholder viewpoints. An integrated Artificial Neural Network (ANN) serves as a preference learning module, trained on explicit team feedback to act as a surrogate evaluator. This active learning cycle substantially reduces the number of required human interactions and alleviates user fatigue. Empirical evaluation on two industrial case studies (E-Commerce System and Healthcare Management System) compared ArchEvolve to a state-of-the-art interactive baseline. Results indicate that ArchEvolve achieves statistically significant improvements in both solution quality and consensus-building. The preference learning module demonstrated over 90% accuracy in predicting team ratings and reduced human evaluations by up to 46% without compromising final solution quality. ArchEvolve provides a practical, scalable framework supporting collaborative, consensus-driven architectural design, making interactive optimization a more viable and efficient tool for real-world software engineering teams by intelligently integrating cooperative coevolutionary search with a preference learning surrogate.

The 2024 simultaneous regional elections reaffirm the persistent underrepresentation of women in local politics, despite a gradual rise in female political participation. Amid the dominance of patriarchal political structures, the victory of Dilla Hich as Regent of Tanjung Jabung Timur emerges as a significant phenomenon that reflects a shift in local electoral dynamics while opening broader opportunities for women’s political representation. This study aims to analyze the political strategies employed by Dilla Hich and examine how her identity and social positioning as a woman contributed to her electoral success. Using a qualitative approach with a case study method, data were collected through in-depth interviews, field observations, and documentation from official sources. The findings indicate that Dilla Hich’s success was not solely rooted in her social capital and extensive political networks but also in the adaptive combination of offensive and defensive political strategies she implemented. Offensive strategies were reflected in the expansion of new voter bases and the strengthening of her personal image, while defensive strategies focused on maintaining loyal supporters and consolidating internal networks. Additionally, the strategic use of gender identity enhanced public acceptance of her leadership. This study concludes that Dilla Hich’s victory demonstrates how structured, inclusive, and adaptive political strategies can challenge patriarchal dominance and create greater opportunities for women in local electoral politics.

This community service program aims to enhance students’ understanding of narrative language features at SMKN 3 Palangka Raya through the implementation of the Detective Case-Solving Game model. The method applied was participatory learning, involving 36 eleventh-grade Computer and Network Engineering students in two sessions. The first meeting focused on introducing the game concept, including rules, character roles, and examples of narrative clues, while the second meeting emphasized the application of more complex case scenarios that required deeper analysis and storytelling skills. The results revealed a significant improvement in students’ ability to identify narrative structures, apply past tense, and use direct–indirect speech correctly. Moreover, students showed higher enthusiasm, actively engaged in group discussions, demonstrated teamwork, and were able to provide linguistic reasoning during presentations. In conclusion, this model effectively strengthens language skills while fostering 21st-century competencies, including critical thinking, communication, collaboration, and creativity, making the learning process more interactive, enjoyable, and meaningful for vocational students.