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A suitable and targeted marketing plan is required because of the intense competition in the retail drinking water sector. Customer segmentation using RFM (Recency, Frequency, and Monetary) analysis is one of the techniques employed. Additionally, K-Means clustering, a clustering technique based on machine learning, is employed. This study's goal is to present the findings in the form of graphs that can be used to examine consumer trends according to their attributes. With a value of 10286, the Calinski Harabaz index is a suitable metric to move on to the segmentation step in this study, which also tests three metrics using the clustering method. An ideal cluster is created for every cluster evaluation by dividing the Calinski Harabaz index into three more manageable clusters. This contrasts with other evaluation metrics that only yield two clusters. For instance, when XYZ drinking water sales transaction data was distributed, it was discovered that, out of the total drinking water sales, woodsale had 422 customers, diamond had 1061 customers, and star diamond had 2005 customers. The management of the XYZ drinking water company and other marketing fields are expected to encounter more intense competition as a result of the study's findings.

This training aims to equip students of to enhance the graphic design skills of students at SMK XI DKV Pangeran Antasari through Adobe Photoshop training. This activity is conducted in response to the growing need for graphic design proficiency among students in the digital age. This service adopts a practical qualitative research method with a direct approach, enabling participants to immediately apply the acquired knowledge. The methods employed include interactive workshops, practical demonstrations, and skills evaluation. The results of the activity demonstrate a significant improvement in students' abilities, with 85% of participants capable of producing simple designs independently. It is therefore expected that upon completion of this training, participants will be able to enhance their creativity in learning, particularly in the field of graphic design, as a primary foundation for becoming professional designers in the workforce."

The rapid growth of e-commerce has intensified the need to understand transaction patterns and customer purchasing behavior as a foundation for strategic decision-making. This study aims to analyze e-commerce transaction patterns and customer purchasing behavior based on demographic characteristics and transaction timing. By utilizing e-commerce transaction data, this research seeks to provide a more comprehensive understanding of customers’ purchasing tendencies and the factors influencing their behavior. This study employs Exploratory Data Analysis (EDA) as the primary method to descriptively explore data characteristics through various statistical visualizations, including histograms, bar charts, line graphs, and boxplots. The analysis conducted to identify transaction trends, the distribution of purchase values, and behavioral differences across demographic groups and specific time periods. The results indicate that e-commerce transaction patterns tend to increase during certain periods, particularly in the latter part of the observation timeframe, suggesting the influence of seasonal factors and promotional strategies. The distribution of transaction values is asymmetric, with most transactions occurring in the low to medium value range, while high-value transactions are conducted by a relatively small proportion of customers. Furthermore, variations in purchasing behavior are observed across demographic groups in terms of transaction frequency and value, despite relatively balanced transaction volumes. The findings confirm that e-commerce customer purchasing behavior is influenced by a combination of temporal factors and demographic characteristics. These results are expected to serve as a basis for e-commerce practitioners in developing more targeted marketing strategies and as a reference for future research in the field of e-commerce data analytics.

MSMEs Traditional culinary MSMEs play a vital role in the Indonesian economy through job creation, income equality, and preservation of local culture. However, limited capital, raw materials, and labor, as well as unsystematic production planning, mean that MSME production decisions are still intuitive, leading to inefficiencies and suboptimal profits. A case study of MSME Sostang Tijang Bruno, a Sundanese cireng producer, shows that cireng production is still based on experience without clear calculations, resulting in a mismatch between production and demand and waste of raw materials. This study aims to optimize cireng production volume using the Graphical Linier Programming method to maximize profits by considering constraints on raw materials, working time, and market capacity. Data were obtained through observation, interviews, and documentation. The decision variables were the production volume of original chicken-filled cireng and spicy chicken-filled cireng. The analysis results showed an optimal production combination of 2.93 kg of original chicken-filled cireng and 0.53 kg of spicy chicken-filled cireng with a maximum profit of Rp499,000 per day. This method is expected to help MSMEs make more efficient and rational production decisions

Instilling local wisdom values in early childhood is crucial for strengthening cultural identity and contextual learning. Learning media based on local wisdom needs to be developed and its effectiveness evaluated in early childhood education. This study aims to evaluate the quality of local wisdom-based instructional media for early childhood education across five aspects: systematics, language, substance, graphics, and usefulness. A descriptive quantitative survey was conducted with 100 early childhood teachers in Surakarta. The instrument was a validated questionnaire using a four-point rating scale. Reliability testing confirmed internal consistency, and ethical protocols (informed consent, anonymity) were observed. The research results show that, Findings reveal that systematics and usefulness scored highest, showing that the media is well-structured and supportive for teaching practice. In contrast, the substance and graphic aspects scored lower, indicating limited content depth and visual quality. Divergent views were noted on the use of foreign terms. Local wisdom-based instructional media is highly relevant and useful, but improvements are required in substance integration and visual design. This study contributes novelty by providing a comprehensive evaluation framework for media grounded in local culture. Practical implications highlight the importance of contextualizing media development for teachers, while future research should examine direct impacts on children’s learning outcomes.

Physics learning requires students’ multirepresentational ability to understand concepts through verbal, mathematical, pictorial, or graphical forms. However, instruction at SMAN 1 Natal is still dominated by conventional methods, resulting in less active student participation and low multirepresentational skills. This study aims to determine the improvement of students’ multirepresentational ability after the implementation of the Problem Posing learning model on the topic of sound waves. The research employed a quantitative approach with a quasi-experimental design. The sample consisted of class XI MIPA 1 as the control class and class XI MIPA 2 as the experimental class. The research instrument was a multirepresentation test administered through pre-test and post-test. Data were analyzed using the Shapiro–Wilk test, the Mann–Whitney test, and the N-Gain test. The results of the normality test indicated that the data were not normally distributed; therefore, hypothesis testing was continued using the Mann–Whitney test, which yielded a significance value of 0.00 < 0.05. This result indicates a difference in the improvement of multirepresentational ability between the experimental and control classes. The N-Gain result for the experimental class was 49.40%, categorized as moderate. Thus, the implementation of the Problem Posing learning model in the experimental class resulted in an improvement that was lower than that of the control class

The construction and infrastructure sectors are shifting toward lighter, low-emission, and sustainable materials in response to the high carbon footprint and excessive weight of common materials such as concrete and steel. One promising alternative widely developed is natural fiber–based composites. However, studies comparing mechanical properties of variations of natural fibers within a single framework remain limited. This study aims to evaluate and compare composite mechanical properties reinforced by sisal fiber, bamboo fiber, and pineapple leaf fiber to determine the optimal fiber type for sustainable infrastructure applications. The research methodology involved fabrication of composite specimens using a unidirectional fiber configuration with a resin matrix, molded following ASTM D638 Type I dimensional and geometrical requirements. Tensile testing was conducted to evaluate mechanical responses, including ultimate tensile behavior, deformation characteristics, and elastic properties, which were presented in tabular and graphical forms. The results show that incorporation of all natural fiber types significantly enhanced composite mechanical properties, exhibiting an average tensile strength of approximately 26 MPa. Pineapple leaf fiber demonstrated balanced mechanical behavior combining strength and ductility, while sisal fiber showed superior tensile resistance and rigidity. Bamboo fiber provided moderate mechanical improvement. Overall, natural fiber–reinforced composites demonstrate strong potential as environmentally friendly alternative materials for infrastructure applications, with mechanical characteristics adjustable based on reinforcing fiber type.

This study focuses on the development of an interactive learning media for introducing OpenGL in the Computer Graphics course. The research approach used is Research and Development (R&D) with the ADDIE development model, which includes the stages of analysis, design, development, implementation, and evaluation. The final product of this study is an interactive learning application that covers basic OpenGL material, simple programming exercises, evaluation quizzes, and other supporting information. The trial results show positive feedback from students. Based on the questionnaire analysis, the aspects of appearance, content, interactivity, and benefits received an average score of 85% in the "very good" category. Additionally, the effectiveness of this media is evident in the improvement of student learning outcomes, with the average score increasing from 64 to 82 after using the media, and the passing rate rising from 56% to 88%. Overall, this OpenGL-based interactive learning media proves to be effective in enhancing students' understanding and can serve as an alternative or complement in the Computer Graphics course learning process.

This community service program aims to enhance the capacity of UMKM Mentari Padangsari in developing visual content to strengthen branding and competitiveness. It is hoped that this training activity will have an impact on the development of MSMEs in Padangsari sub-district.The main problems faced by partners include limited skills in creating product photos, graphic designs, promotional videos, and managing social media consistently. The program was implemented through training, mentoring, and evaluation stages involving 14 UMKM actors in Padangsari Village, Semarang City. Training materials focused on product photography using smartphones, simple graphic design, short promotional videos, visual storytelling, and the use of digital platforms such as social media and Google Business are  considered very necessary in this era of rapid technological development. The results show increased knowledge and skills among UMKM actors in producing more attractive visual content, improving brand identity, and enhancing engagement on social media. This program contributes to strengthening UMKM competitiveness and supports sustainable digital-based marketing practices.

The gap between vocational students’ competencies and the needs of the digital creative industry requires efforts to enhance students’ technological and creative skills. This community service program aimed to improve the digital competence and creativity of vocational students through Canva-based motion graphics training. The program was conducted at SMK Swasta Nur Azizi Tanjung Morawa using lectures, demonstrations, hands-on practice, and mentoring methods. The results showed that participants were able to understand bassic motion graphics concepts and produce simple animated works using Canva. Evaluation results indicated that more than 80% of participants were able to operate Canva’s basic animation features, and all participants successfully produced at least one motion graphics project, with an average competency improvement of approximately 30% compared to pre-training levels. Overall, the training proved effective in enhancing students’ creative skills, confidence, and readiness to meet the demands of the digital creative industry.

Manual management of sports field bookings is still widely practiced and often leads to scheduling conflicts, data recording errors, and low service efficiency. This study aimed to design and develop a desktop-based sports field booking application that automates the booking process and manages schedules in a structured manner. The research employed a system design and development method using an object-oriented programming (OOP) approach. Data were collected through direct observation of the booking process, interviews with field managers, and documentation of system requirements. The application was developed using the Python programming language with the PyQt5 framework for the graphical user interface and MySQL as the database management system. The results showed that the developed application is capable of managing field data, schedules, bookings, and user information in an integrated manner while reducing recording errors and minimizing scheduling conflicts. The application of OOP resulted in a modular, well-organized, and maintainable system structure. This application is expected to improve the efficiency and accuracy of sports field booking management and provide a practical solution for implementing a computerized booking system.

The global energy crisis and climate change are driving the development of biodiesel as a renewable energy source. Graphite as an additive shows significant potential in improving the efficiency and reducing emissions of biodiesel. This study maps graphite-biodiesel research in Southeast Asia using a meta analysis of systematic reviews of 68 publications from Scopus, Web of Science, and ScienceDirect from 2015-2024. The results show that Malaysia leads in publication contributions (32%), followed by Thailand (28%) and Indonesia (18%). The optimal graphite concentration of 50 ppm increases brake thermal efficiency by 8.3% and reduces CO (15.7%), HC (12.4%), and smoke (18.9%) emissions, although there is an increase in NOx (6.8%). Palm oil methyl ester dominated the research (56%). Indonesia has strategic opportunities with abundant feedstock and graphite deposits, but faces challenges in research infrastructure, limited international collaboration, and the absence of an integrated national roadmap. Infrastructure investment, human resource strengthening, and industry academia collaboration are needed to accelerate national biodiesel research.

This community service program aimed to strengthen the digital competencies of junior high school students through a structured workshop on graphic design and photography. The activity was conducted on June 10–11, 2025 at Universitas Ma Chung and involved thirteen students from SMP Insan Amanah accompanied by their teachers. The program was designed based on prior identification of learning needs conducted through coordination with school representatives. The workshop integrated theoretical instruction and hands-on practice covering basic photography concepts such as angle, shot, triangle exposure, and three point lighting, followed by editorial layout training using Canva. Students were guided to edit photographs, arrange layouts, and select appropriate text elements. The results indicated improvements in students’ technical skills, learning engagement, and collaborative behavior. A campus tour conducted at the end of the program provided additional insight into the academic environment of higher education. Overall, the program demonstrated that practice-based learning effectively supports digital skill development among junior high school students.

Graph theory as a branch of discrete mathematics has experienced significant development in its application to modern complex network systems, particularly in digital social networks and transportation systems. This research aims to analyze fundamental concepts of graph theory, examine characteristics of cycle detection algorithms along with their computational complexity, investigate their application in digital social network analysis, and explore their implementation in digital transportation system optimization. The research method employs a qualitative approach with library research focusing on scientific literature from 2020-2025 period from accredited academic databases such as Scopus, Web of Science, and IEEE Xplore, utilizing thematic analysis techniques to identify meaningful patterns from the examined literature. Research findings indicate that fundamental graph theory concepts including vertices, edges, and graph classifications form the foundation for relational structure modeling. Cycle detection algorithms such as Depth-First Search, Union-Find, and Tarjan demonstrate effectiveness with O(V+E) complexity for large-scale graphs. Applications in digital social networks facilitate community identification through Multi-View Clustering, centrality analysis for influencer detection, and understanding viral information dissemination patterns. Implementation in digital transportation systems demonstrates route planning optimization using Dijkstra and Bellman-Ford algorithms, vulnerability analysis through articulation point and bridge identification, and bottleneck detection with betweenness centrality. The research concludes that integration of graph theory in discrete mathematics education enhances critical thinking skills and real-world application understanding, with recommendations for algorithm development for massive dynamic graphs and machine learning integration in graph algorithm optimization.

The increasing adoption of collaborative robots in modern manufacturing environments requires reliable perception systems that can ensure both safety and operational efficiency during human–robot collaboration. This study proposes a CNN-based real-time computer vision system for object and human detection in shared robotic workspaces. The research focuses on developing and evaluating a single-stage deep learning detection model optimized for real-time performance while maintaining high detection accuracy. The proposed methodology includes dataset preparation, model training using transfer learning, real-time system implementation, and comprehensive performance evaluation. Experimental results demonstrate that the developed system achieves high detection accuracy, as reflected by strong precision, recall, and mean Average Precision (mAP) values, while maintaining low inference latency suitable for real-time operation. The system consistently operates above real-time frame-rate thresholds, ensuring timely perception updates required for safety-related decision-making in collaborative robotic environments. Graphical and quantitative analyses further confirm the stability of inference performance under dynamic interaction scenarios involving human movement and multiple objects. Compared with existing approaches, the proposed system provides a balanced trade-off between accuracy and computational efficiency, making it practical for deployment in safety-aware human–robot collaboration scenarios. Overall, the findings indicate that CNN-based real-time object detection systems can effectively support perception and situational awareness in collaborative robotics, contributing to safer and more efficient industrial automation.

Semantic interoperability remains a major challenge in large scale distributed information systems due to heterogeneous data schemas, diverse contextual interpretations, and the dynamic nature of distributed environments. Traditional metadata-based interoperability approaches are often insufficient to address these challenges, as they lack semantic expressiveness and adaptability. This study proposes a context aware knowledge graph framework to enhance semantic interoperability across heterogeneous distributed systems. The research adopts a design-oriented methodology involving requirement analysis, knowledge graph construction, ontology modeling and alignment, context aware semantic representation, and semantic reasoning. A prototype implementation is developed to evaluate the effectiveness of the proposed framework through interoperability scenarios and cross-system semantic queries. The results demonstrate that the proposed approach significantly improves semantic alignment accuracy, query precision, and recall compared to conventional metadata-based solutions. The explicit integration of contextual information and ontology-based reasoning enables adaptive semantic interpretation and reduces ambiguity across systems. Overall, the findings confirm that combining knowledge graphs with ontology modeling and context aware mechanisms provides a robust and scalable solution for improving semantic interoperability in complex distributed information systems.

Edge-Internet of Things (Edge IoT) systems are increasingly integral to applications that require real time signal processing, particularly where low latency and energy efficiency are critical. This paper explores the design and performance evaluation of a heterogeneous microprocessor architecture aimed at optimizing energy consumption and real time performance. The heterogeneous architecture integrates multiple types of cores, such as Central Processing Units (CPUs), Digital Signal Processors (DSPs), and Graphics Processing Units (GPUs), to allocate tasks based on computational demand. The proposed design significantly reduces energy consumption, particularly during high-performance tasks, while maintaining real time processing guarantees. Simulation-based performance evaluation was conducted to assess the energy efficiency, latency, and overall system performance under varying workloads, including real time Digital Signal Processing (DSP) benchmarks. The results showed that the heterogeneous architecture outperformed traditional homogeneous processors, demonstrating up to a 19-fold improvement in energy efficiency. Furthermore, the system reduced latency by up to 45% in real time applications, making it particularly suitable for Edge IoT environments such as industrial automation and smart healthcare, where both performance and energy efficiency are critical. Despite some trade-offs in task scheduling complexity, the heterogeneous design was able to balance power consumption and computational performance effectively. The findings suggest that this architecture can serve as a foundation for future Edge IoT systems, providing significant advantages in terms of energy efficiency, real time processing, and scalability. Future work will focus on further optimization of the architecture and exploring its application across various IoT environments.

 Autonomous robot navigation in dynamic and unstructured environments remains a critical challenge due to unpredictable obstacles, sensor uncertainty, and limited adaptability of traditional planning algorithms. Although conventional navigation methods such as graph-based, potential field–based, and sampling-based approaches have been widely adopted, their performance under real-time dynamic conditions is still constrained. This study aims to design and implement a comprehensive experimental framework to evaluate the effectiveness and limitations of conventional navigation algorithms for autonomous mobile robots operating in dynamic unstructured environments. The research adopts an experimental and comparative methodology by implementing A*, Dijkstra, Artificial Potential Field (APF), and Rapidly-Exploring Random Tree (RRT) algorithms in simulated static and dynamic scenarios. Performance is assessed using quantitative metrics including path length, computation time, success rate, collision rate, and path smoothness. The experimental results demonstrate that graph-based algorithms achieve high success rates and optimal path efficiency in static environments but exhibit limited adaptability to dynamic changes. APF offers fast computation but suffers from high collision rates due to local minima, while RRT shows better adaptability in dynamic environments at the cost of longer and less smooth paths. These findings confirm that conventional navigation methods are insufficient for robust autonomous navigation in highly dynamic and unstructured environments. The study highlights the necessity of adaptive and learning-based navigation frameworks, such as deep reinforcement learning, to enhance real-time decision-making, robustness, and autonomy in future robotic systems.

The Indonesia Smart Program (Program Indonesia Pintar/PIP) is a government initiative aimed at ensuring equal access to education for students from underprivileged families, including those at the junior high school (SMP) level. However, at the school level, the management of PIP recipient data still faces several challenges, particularly in data searching and utilization, due to the increasing volume of data and the use of simple or manual search methods. These conditions can lead to delays in obtaining information and reduce the accuracy of decision-making. Therefore, an effective information retrieval system is needed to manage and search PIP recipient data efficiently. This study aims to design and develop an Information Retrieval System for PIP recipient data at the junior high school level using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The TF-IDF method is applied to assign weights to terms in each document, enabling the system to identify and rank documents based on their relevance to user queries. The test results show that the system is able to measure document relevance accurately, where documents D3 and D4 obtain the highest similarity value of 0.099586089 and are classified as highly relevant, while other documents show lower similarity values down to zero. These results are also supported by graphical visualization, which helps users compare relevance levels more clearly. Thus, the implementation of the TF-IDF method has proven to be effective in supporting accurate, efficient, and systematic searching and management of PIP recipient data at the junior high school level.

Equitable vaccine distribution is a global issue that has received increasing attention, especially since the increasing need for vaccines in the face of modern pandemics. This study aims to analyze the inequality in vaccine distribution and the factors influencing vaccine hesitancy through a descriptive qualitative approach, utilizing literature studies and supporting data in the form of graphs. The analysis results show that high-income countries have significantly greater access to vaccines than middle- and low-income countries, creating inequalities that impact public health protection. Furthermore, levels of vaccine hesitancy were found to vary across social groups, with adolescents being the group with the highest rate of rejection due to the influence of misinformation and low trust in health institutions. These findings confirm that the success of a vaccination program is determined not only by the availability of equitable distribution but also by public acceptance, which is influenced by social, psychological, and ethical factors. Overall, this study emphasizes the importance of applying bioethical principles such as justice, beneficence, and autonomy in formulating effective and inclusive vaccination policies.