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The development of digital marketing has become a strategic factor in the growth of creative culinary businesses, particularly for micro, small, and medium enterprises facing increasing competition and changes in consumer behavior in the digital era. The use of digital platforms, such as social media and online marketplaces, enables business owners to expand market reach, enhance customer interaction, and optimize promotional strategies more efficiently. Nevertheless, various studies indicate that the implementation of digital marketing has not always produced optimal outcomes due to limitations in digital literacy, human resource capacity, and organizational adaptability. This study aims to systematically examine the role of digital marketing in the development of creative culinary businesses through a Systematic Literature Review approach. The review maps key concepts, research trends, and empirical findings related to digital marketing and organizational learning within the context of creative culinary enterprises. In addition, this study identifies existing research gaps and formulates recommendations for future research issues. The findings are expected to provide theoretical contributions in the form of comprehensive conceptual mapping as well as practical contributions for business practitioners, MSME facilitators, and policymakers in designing adaptive and sustainable digital marketing strategies.

Digital copyright piracy remains a growing legal issue in Indonesia alongside the rapid development of information technology. This practice not only causes economic losses to creators and copyright holders but also reflects a gap between legal norms and social realities. This study aims to analyze legal protection for digital copyrights and to assess the effectiveness of Law Number 28 of 2014 on Copyright. This research employs a normative legal method using a library research approach by examining statutory regulations and relevant academic literature. The findings indicate that although Indonesia has established an adequate legal framework for copyright protection, its implementation remains ineffective due to low public legal awareness, weak law enforcement, and challenges in monitoring digital platforms. These findings emphasize that effective copyright protection requires not only comprehensive regulations but also synergy between the government, law enforcement authorities, and the development of a strong legal culture within society.

User experience (UX) evaluation plays a crucial role in understanding how users interact with digital platforms and in improving product design. Traditional UX evaluation methods, such as surveys and interaction logs, often rely on a single data source, which limits the depth of analysis. This study explores the integration of multimodal data processing techniques in UX research, aiming to enhance the accuracy and comprehensiveness of UX evaluations. By combining interaction logs, visual attention data, and physiological measurements, this approach provides a more holistic understanding of user behavior, emotional responses, and satisfaction. Interaction logs offer objective data on user actions, while eye-tracking and physiological data capture users' emotional states, providing richer insights into usability and user experience. This study highlights the effectiveness of multimodal integration in identifying patterns that traditional methods overlook, such as emotional responses to interface elements and real-time feedback from users. The findings reveal that multimodal data processing improves the precision of UX assessment by combining objective behaviors with subjective emotional responses, offering a more complete view of user interactions. The study also discusses the challenges of data synchronization and the potential ethical concerns related to the use of physiological data. The integration of these data sources shows great potential for enhancing the design process, allowing designers to make informed decisions based on comprehensive insights. Finally, this research underscores the future potential of multimodal analytics in UX research, suggesting further exploration of additional data modalities and real-time applications in various digital environments.

This service examines the use of the social media platform of the tiktok application which acts as an effort to approach digital promotion for micro, small and medium enterprises in Krobokan village, Juwangi. Although MSMEs allocate an important role in sales products to the community, they do not fully understand digital media.This service applies a qualitative method by conducting interviews and socialization to the community which amounts to around 30 people consisting of 10 MSME business actors. The results of the interview identified MSME actors in Krobokan village who are not fully aware of the importance of understanding the development of social media as a marketing media tool, therefore we provide education about sales through online media because they only market products traditionally in the village environment. TikTok as a strategic means in supporting sales in today's digital era because the TikTok application is one of the highest platforms that are widely used today such as the TikTok Shop and Live TikTok features in strengthening the introduction of sales to customers through social media. This service concludes that from traditional marketing by word of mouth to marketing in the digital era that focuses on creative digital marketing, efforts have great potential in developing the community's economy in the current digital era.

Socio-economic challenges faced by tuberculosis (TB) survivors in Indonesia remain substantial, particularly stigma that restricts employment opportunities and hinders social reintegration. This community service program aimed to strengthen the economic capacity of TB survivors through a community-based empowerment approach and health information dissemination via social media. The program involved a TB survivor community in the partner area of the Lampung Sehat Initiative. A participatory method was applied through entrepreneurship training, digital content production assistance, and the distribution of health education materials using Instagram, TikTok, and YouTube. The results indicate that social media was effective not only as a platform for health education but also as a promotional tool for participants’ micro-business products. In addition to improving public literacy regarding TB, social media contributed to stigma reduction by delivering positive narratives and reinforcing the collective identity of the survivor community. The program also encouraged behavioral change, supported the emergence of local leadership, and expanded participants’ social and economic networks. These findings suggest that integrating social media into community-based empowerment initiatives can serve as a social transformation strategy that enhances the sustainability of economic development and social support for TB survivors.

Contemporary digital advancements have significantly impacted the manner in which individuals engage in economic activities, as evidenced by the rising volume of online transactions. While offering convenience and efficiency, electronic transactions also provide numerous legal challenges, especially regarding seller defaults, including delayed delivery, goods that do not conform to the agreement, or sellers failing to meet their duties post-payment by consumers. This scenario may result in consumer losses, necessitating sufficient legal certainty and protection. This study seeks to analyze the legal regulations pertaining to contracts and defaults in digital platform transactions and to evaluate the types of legal liabilities that may be placed on sellers. This study employs normative legal research, focusing on legislative and conceptual approaches, and utilizes primary, secondary, and tertiary legal materials as data references. The study's findings reveal that seller liability regulations in electronic transactions are defined in the Civil Code, the Consumer Protection Law, and the Law on Electronic Information and Transactions. Nonetheless, its execution has several challenges, especially concerning evidence and the efficacy of law enforcement measures. Consequently, it is imperative to enhance the function of marketplaces and establish more adaptive legislative frameworks to guarantee consumer protection and restitution for losses in online commerce.  

This study investigates the integration of high performance communication protocols with adaptive signal processing engines in multi-core systems, aiming to enhance scalability, throughput, and inter-core communication efficiency. The challenges inherent in traditional multi core architectures, such as communication overhead, latency, and scalability limitations, are addressed through the incorporation of Network-on-Chip (NoC) architectures and adaptive signal processing techniques. By using a multi-core digital signal processing (DSP) platform, the study evaluates the performance improvements achieved by this integration under varying workloads and core configurations. The experimental results show a 35% improvement in throughput and a 25% reduction in communication latency, highlighting the effectiveness of adaptive communication protocols in managing data traffic between cores and reducing bottlenecks. The integration of NoC architecture facilitates parallel data transfers, while adaptive signal processing engines ensure that data flows more efficiently across the cores, enhancing system responsiveness, especially under high data rate conditions. Furthermore, the study explores the scalability of the proposed system, demonstrating its ability to maintain high performance as core counts increase. The findings emphasize the potential of combining advanced communication protocols with adaptive signal processing for optimizing multi-core system performance. Practical implications of this research include the design of scalable, flexible, and efficient multi core architectures suitable for complex, data-intensive applications. Future research should focus on further refining communication protocols and exploring additional integration strategies to enhance the adaptability and scalability of multi-core systems in next-generation computing environments.

This study explores the integration of deep learning based approaches in real time video content analysis for intelligent human computer interaction (HCI) in multimedia systems. Traditional video analysis techniques, such as rule-based methods and offline processing, struggle with real time performance and adaptability to complex video data. In contrast, the deep learning model used in this research, particularly Convolutional Neural Networks (CNNs), provides high accuracy in object detection, feature extraction, and real time processing. The integration of CNNs with interactive visualization modules enables dynamic adjustments to video content based on user interactions, ensuring a seamless and engaging user experience. The system was benchmarked in terms of its processing speed, accuracy, and responsiveness, showing significant improvements over traditional approaches in real time video analysis. Moreover, the study demonstrates that combining deep learning with real time visualization enhances the efficiency of interactive multimedia applications, making it suitable for dynamic environments such as surveillance, security monitoring, and interactive media. Despite the system's strong performance, challenges such as computational demands in high-resolution video processing were identified, highlighting the need for further optimization. Future work will focus on optimizing the system for different hardware platforms, incorporating multimodal inputs, and refining deep learning models to address computational bottlenecks. This research contributes to advancing HCI by providing insights into the integration of deep learning for real time video content analysis, which is pivotal for enhancing the interactivity and adaptability of intelligent multimedia systems.

The increasing demand for real time parallel processing in cloud computing environments necessitates the development of more efficient and fault-tolerant scheduling algorithms. Traditional scheduling methods, such as static algorithms, often fall short when handling dynamic workloads and system failures, leading to increased task latency and reduced system performance. In contrast, adaptive scheduling algorithms dynamically adjust to changes in system conditions and workloads, ensuring timely task completion and optimized resource utilization. This study evaluates the performance of adaptive scheduling algorithms in real time cloud environments, focusing on key factors such as task latency, system resilience, and fault tolerance. Simulation experiments were conducted using cloud computing models that incorporate fault injection scenarios, including network failures and virtual machine crashes. The results show that adaptive algorithms significantly outperform traditional static schedulers in terms of task latency reduction and improved system resilience. These algorithms demonstrated better fault recovery times and ensured consistent real time performance, even under failure conditions. The findings highlight the advantages of adaptive scheduling in cloud environments, particularly for applications requiring rapid data processing and high system reliability. Despite the promising results, challenges remain regarding the scalability and complexity of these algorithms in large-scale cloud systems. Further research is needed to optimize adaptive scheduling algorithms for efficiency, scalability, and comprehensive performance evaluation, taking into account factors such as energy consumption, cost, and reliability. This research contributes to advancing cloud computing infrastructures that can dynamically handle real time tasks and maintain high performance under varying workloads and failures.

Micro-entrepreneurs, particularly small grocery shop owners, commonly encounter challenges in managing business finances and implementing effective marketing strategies. Limited skills in financial recordkeeping often make it difficult for business owners to accurately monitor cash flow and assess their financial condition, which may lead to suboptimal business decisions. In addition, the utilization of digital technology as a marketing tool remains limited, thereby restricting opportunities for market expansion. This community service program aims to strengthen the capacity of micro-enterprises in applying practical basic financial management and optimizing digital platforms for product promotion. The program also seeks to increase awareness of the importance of structured financial management as a foundation for sustainable business development. The activities were conducted through several stages, including the delivery of material on the significance of financial recording, hands-on training sessions, mentoring in preparing simple financial records, and digital marketing simulations using WhatsApp Business and various social media platforms. The results indicate an improvement in participants’ understanding and skills in recording income and expenses, managing business capital more systematically, and utilizing digital features to support promotional activities. Furthermore, participants demonstrated a more positive attitude toward the adoption of technology in their daily business operations. Overall, this program is expected to enhance financial independence, support sustainable business growth, and expand the marketing reach of micro-enterprises

User reviews on the Shopee e-commerce platform represent an important source of information for understanding consumer perceptions of products and services. Sentiment analysis is commonly applied to classify user opinions into positive, neutral, and negative sentiment categories based on textual data. This study aims to analyze the performance of the Long Short-Term Memory (LSTM) method in sentiment classification of Shopee user reviews. The dataset used in this study consists of Indonesian-language user reviews that have undergone preprocessing stages, including case folding, text cleaning, tokenization, and stopword removal. The LSTM model was trained using preprocessed text represented as word sequences. Model performance was evaluated using overall accuracy and class-wise classification results. The experimental results indicate that the LSTM method achieved an overall accuracy of 87.62%. In addition, the classification performance for the positive sentiment class reached 95.27%, the neutral class achieved 4.96%, and the negative class reached 74.26%. These results demonstrate that the LSTM method performs well in classifying sentiment in Shopee user reviews, particularly for positive sentiment. This study is expected to provide insights and references for the application of deep learning methods in sentiment analysis of Indonesian e-commerce review data.

The rapid development of digital technology has brought significant changes to the business landscape, transforming how products are marketed, services are delivered, and business relationships are built. In this context, students, as future members of the workforce and potential entrepreneurs, are required to possess strong digital literacy skills in order to effectively face challenges and seize emerging business opportunities. This study aims to analyze the importance of digital literacy in supporting students’ readiness to respond to future business trends. The research employed a descriptive approach using a literature review and observations of the entrepreneurship learning process in vocational schools. The findings indicate that digital literacy plays a crucial role in enhancing students’ creativity, adaptability, and ability to utilize digital platforms for online marketing, branding, and business communication. Furthermore, digital literacy helps students understand market dynamics, analyze consumer behavior, and adopt innovative business models that align with technological developments. Students with adequate digital literacy are better prepared to face rapid changes in the business environment and demonstrate higher confidence in applying technology to entrepreneurial activities. In conclusion, the integration of digital literacy into entrepreneurship education is essential to produce competitive, innovative, and adaptable graduates who are capable of thriving in the digital era and contributing to sustainable economic development.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.

Technology-based learning media have now become a primary choice in supporting the learning process in elementary schools, in line with the development of the digital era and the growing demand for more innovative learning approaches. The utilization of technology in education is expected to enhance the quality of teaching and learning processes and encourage active student engagement. One technology-based learning medium considered effective and engaging is BrainPOP, a digital learning platform that presents educational content through interactive animated videos, quizzes, simulations, and various supporting activities designed to suit the characteristics of elementary school students. The selection of BrainPOP is based on its advantages in increasing students’ learning interest, facilitating the understanding of abstract concepts by making them more concrete, and enhancing students’ motivation and retention of learning materials. The visual, communicative, and interactive presentation of content enables students to learn in a more enjoyable and less monotonous manner. This medium is capable of creating an engaging, effective, and student-centered learning atmosphere, thereby fostering an active, creative, and enjoyable learning environment. Therefore, the use of BrainPOP in the learning process is expected not only to improve students’ learning outcomes but also to optimally and sustainably develop their interest, motivation, and engagement in learning.

This study aims to analyze the influence of the @starbucksindonesia account's use of Instagram as a communication medium on brand image following the pro-Israel boycott. The boycott arose from Starbucks' alleged involvement in the Israeli-Palestinian conflict, which triggered a decline in its reputation and negative sentiment among Indonesians. In this situation, Instagram, as a visual-based social media platform, was utilized as a primary means of shaping public opinion and responding to the crisis. The study employed a quantitative approach using an online questionnaire survey. Respondents were 100 followers of the @starbucksindonesia Instagram account, aged 18–35, and former Starbucks customers. Data analysis was conducted using validity and reliability tests, simple linear regression, t-tests, and coefficients of determination using SPSS version 27. The results showed that all research instruments were valid and reliable. The main finding demonstrated that Instagram use had a statistically significant effect on Starbucks' brand image. The coefficient of determination value indicated a strong relationship, indicating that the majority of changes in respondents' perceptions were influenced by communication via Instagram. This research supports the Uses and Effects theory, which states that social media not only serves as an information provider but also has the ability to shape consumer perceptions and attitudes. Therefore, Instagram plays a strategic role in digital communications for crisis management and brand image restoration.

The implementation of the zoning-based admission policy (PPDB) in Semarang City continues to face challenges related to the accuracy of distance measurement and the transparency of information provided to the public. This study aims to examine the application of Geographic Information Systems (GIS) in defining zoning boundaries for public junior high schools in Semarang City and integrating the results into a web-based information platform. A quantitative descriptive approach was employed, incorporating spatial analysis through a 3-kilometer buffer radius using QGIS software. The results indicate that buffer analysis is effective in delineating priority domicile zones based on school coordinate data. These findings are integrated into a GIS-based website that presents visual information on school locations, enrollment capacity, and final score calculation mechanisms in accordance with current regulations. The proposed system contributes to improving information transparency, enabling the public to better understand admission opportunities while supporting government decision-making in promoting equitable access to education. For future development, the use of road network analysis is recommended to obtain more realistic distance estimations.

This community service activity aimed to strengthen teachers’ competencies in utilizing digital platforms to support project-based learning at a vocational high school. The background of this activity was the limited ability of teachers to manage project-based learning in a structured and systematic manner using digital platforms. The method employed was participatory mentoring, involving needs analysis, activity planning, implementation of digital platform mentoring, monitoring and evaluation, and reflection with follow-up actions. The participants were teachers of SMK Negeri 1 Tukak Sadai, Bangka Barat. The results indicated an improvement in teachers’ understanding and skills in designing, managing, and evaluating project-based learning using digital platforms. Teachers became more confident in integrating technology into learning processes and demonstrated better organization of project activities. This activity contributed to enhancing teachers’ digital competencies and improving the quality of project-based learning practices in vocational education. The findings suggest that participatory mentoring is an effective approach to supporting sustainable digital learning innovation in vocational schools.

Diarrhea and Tuberculosis (TB) remain significant public health problems in Indonesia, including Rembang Regency. This study aims to analyze the spatial distribution and temporal changes of Diarrhea and TB cases in Rembang Regency during 2023–2024 using a Geographic Information Sistem (GIS) approach. A descriptive-analytic method was applied using secondary data, including administrative boundary shapefiles, disease case data, and health facility data at the sub-district level. Spatial processing and analysis were conducted using QGIS to produce thematic maps and compare disease distribution patterns between years. The results indicate that Diarrhea and TB cases were unevenly distributed across sub-districts and formed distinct spatial patterns. Case changes between 2023 and 2024 varied spatially, reflecting dynamic disease trends. The integration of disease maps with health facility distribution shows that areas with high case numbers were not always supported by adequate health facilities. The implementation of GIS-based maps into a web platform enhances accessibility and supports spatial-based public health decision-making.

This study compares Grammarly and Paraphraser.io in supporting students’ academic writing and rewriting skills based on Flower and Hayes’s (1981) cognitive process theory, which includes the stages of planning, translating, and reviewing. Using a descriptive qualitative method supported by quantitative data, the research involved 32 English Education students who had used both tools in academic writing. Data were collected through questionnaires and analyzed descriptively to identify students’ perceptions of grammatical accuracy, rewriting ability, feedback quality, and overall writing improvement. The results of this study indicate that 54.4% of students use Grammarly and 45.6% use Paraphraser.io, showing that Grammarly is the slightly more dominant and trusted tool for improving grammatical accuracy and clarity, while Paraphraser.io functions as a complementary platform for paraphrasing and generating new sentence structures; together, both tools contribute effectively though through distinct roles to enhancing students’ academic writing performance. The comparative analysis revealed that Grammarly was most effective in enhancing analytical skills during the planning and reviewing stages, improving grammar, coherence, and clarity. In contrast, Paraphraser.io was most beneficial for generative skills in the translating stage, directly enhancing originality and sentence variation. Most respondents rated their writing improvement highly, confirming the positive impact of both tools on academic performance. In conclusion, the findings confirm that Grammarly and Paraphraser.io serve distinct, yet complementary roles: the former ensures linguistic precision, while the latter supports structural creativity, ultimately helping students produce highly accurate and original academic texts.

Abstract. The development of digital media, especially YouTube, has made this platform not only a means of entertainment, but also a source of information, education, and self-reflection for students. Viewer satisfaction in consuming YouTube content is influenced by the quality of content presented by creators. This study aims to evaluate the impact of the quality of Nadia Omara's YouTube channel content on viewer satisfaction among students at Muhammadiyah University Kotabumi. This study applies a quantitative approach through a survey method. Data were collected through a closed questionnaire with a Likert scale distributed to 98 respondents selected using purposive sampling. The independent variable in this study is content quality, while the dependent variable is viewer satisfaction. Data analysis was conducted through validity testing, reliability testing, linearity testing, and simple linear regression analysis. Hypothesis testing was performed using the t-test (partial test) with the help of SPSS version 26 software. The results showed that the quality of Nadia Omara's YouTube channel content had a positive and significant effect on viewer satisfaction. This is evidenced by a regression coefficient value of 0.865 and a t-value of 11.937 with a significance level of 0.000 (< 0.05). These findings support the Uses and Gratifications Theory and Expectation Confirmation Theory, which emphasize the active role of the audience in evaluating media based on the fulfillment of needs and conformity with expectations empirically among college students.