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Evacuation planning in spatial networks requires the identification of critical nodes that maintain connectivity, accessibility, and flow distribution during emergency situations. Existing approaches often rely on individual centrality measures, which capture only a single structural dimension of node importance and may therefore produce incomplete or biased prioritization. To address this limitation, this study proposes a Composite Centrality Framework for identifying critical nodes in meso-scale spatial networks with semi-structured connectivity. The network is modeled as a weighted undirected graph, and Degree, Betweenness, and Closeness Centrality are integrated into a unified composite index to capture complementary structural roles. The framework is implemented in MATLAB and evaluated using a real-world campus spatial network consisting of 30 nodes and a synthetic network comprising 16 nodes with comparable structural characteristics. The results reveal a highly uneven distribution of node importance, with a small set of structurally dominant nodes consistently identified across both networks. In the campus network, node P1 achieves the highest composite centrality score (0.2195) and ranks first across the individual centrality measures, indicating its dominant role in maintaining network connectivity, accessibility, and flow distribution. Quantitative evaluation demonstrates strong agreement between the composite ranking and the individual measures, with Spearman rank correlation coefficients of 0.94, 0.89, and 0.91 for Degree, Betweenness, and Closeness Centrality, respectively. However, only one node (P1) appears simultaneously in the top five of all rankings, highlighting the complementary nature of the individual centrality measures and supporting the need for multi-criteria integration. Sensitivity analysis across three weighting scenarios yields rank correlations exceeding 0.97, confirming ranking stability and methodological robustness. Overall, the proposed framework provides a balanced and reliable approach for identifying critical nodes and demonstrates potential applicability to evacuation planning and spatial network analysis in semi-structured environments.

Misconceptions on the topic of waves are a systemic problem in high school physics learning that requires a comprehensive diagnostic instrument. Conventional diagnostic instruments up to the four-tier generation have limitations in revealing the conceptual structure behind students' thinking errors. This study aims to develop a five-tier multi-representation diagnostic instrument on the topic of waves using the 4D model (Define, Design, Develop, Disseminate). The main innovation of this instrument lies in the integration of multi-representations (verbal, pictures, graphics, mathematical) in Tier 1 and the addition of a paraphrase tier (Tier 5) that asks students to rewrite their understanding in their own words. The development process includes needs analysis, designing a grid of 16 questions, validation by two experts, and a limited trial on 34 grade XI students of SMAN 13 Kota Jambi. The results of expert validation showed an average percentage of 91.25% (Very Good) from both validators. Content validity was met with a percentage of False Positive (FP) of 6.80% and False Negative (FN) of 4.41%, both below the 10% threshold. Construct validity was confirmed through a significant Product Moment correlation (r = 0.342–0.348; sig. < 0.05) and factor analysis with six significant factors (eigenvalue > 1). The instrument's reliability was high, with a Cronbach's Alpha of 0.726. This instrument is expected to help physics teachers diagnose students' misconceptions more precisely and thoroughly on the topic of waves.

Dijkstra's algorithm is one of the algorithms in graph theory that is used to solve the problem of the shortest path of a graph at each vertex that has a non-negative value. This algorithm was discovered by Edsger Wybe Dijkstra, a scientist from the Netherlands. The search for the shortest route for product delivery can be calculated through the application of the Dijkstra algorithm in the problem being faced. The problem of decision making for selecting the shortest route is still manual, so it experiences several obstacles, including the absence of a systematic and computerized system to assist the decision-making process in determining the route for shipping goods, the determination of shipping routes still depends on manual estimates so that the time taken between deliveries becomes inconsistent, the operational costs of shipping are relatively high because there is no optimal route determination system. Facing these problems, a system is needed that can minimize delays and increase effectiveness in shipping goods, namely determining the shortest route using the Dijkstra algorithm. This system works by finding various alternative routes for shipping goods at PT AMSA to address various structured and unstructured problems using data and models. To process this data and models, a method called the Dijkstra algorithm is required. Based on the description above, researchers will create a method for determining the shortest route for shipping goods at PT AMSA using the Dijkstra algorithm to facilitate the company's process of determining the shortest route.

The increasing complexity of urban transportation systems demands intelligent and measurable navigation methods. Medan City, the capital of North Sumatra Province, has a dense road network with multiple route options that often confuse road users. Dijkstra's Algorithm, developed by Edsger Wybe Dijkstra in 1959, is a greedy-based computational approach proven effective for solving the shortest path problem on non-negative weighted graphs. This study applies Dijkstra's Algorithm to determine the shortest route from Medan Railway Station to Universitas Negeri Medan (UNIMED). The road network was modeled as an undirected weighted graph with 15 nodes and 16 edges, where edge weights represent actual road distances measured via Google Maps. The graph has a density of 0.152, confirming its sparse graph characteristic. Three alternative routes were identified and analyzed. The algorithm was implemented in Python 3 using the heapq module as a priority queue. Results show that the optimal route is A → B → C → E → F → M → N → O via Jl. M.T. Haryono, Jl. Aipda KS Tubun, Jl. Madong Lubis, and Jl. Prof. H.M. Yamin, with a total distance of 6.64 km. This achieves 99.1% accuracy compared to Google Maps, with a deviation of only 0.06 km. The optimal route is 6.25% more efficient than Alternative Route 1 (7.30 km) and 11.9% more efficient than Alternative Route 2 (7.54 km). The algorithm executes in under 1 millisecond with time complexity O((V+E) log V). These findings confirm Dijkstra's Algorithm as highly effective for medium-scale urban road network optimization.

The ability to describe graphs accurately in English is an essential academic skill for EFL learners, particularly in higher education contexts where students are required to interpret and communicate visual data. However, many learners still experience difficulties in producing grammatically accurate graph descriptions. This study aims to investigate the level of grammatical accuracy demonstrated by EFL learners in graph description tasks and identify the grammatical aspects that require further improvement. Employing a descriptive qualitative approach, the research analyzed 30 samples of students’ written graph descriptions produced by undergraduate learners. The analysis focused on several grammatical aspects, including subject–verb agreement, tense consistency, article usage, sentence structure, prepositions, and punctuation. The findings reveal that students frequently struggle with subject–verb agreement and article usage, while punctuation errors occurred less frequently. The study highlights that grammatical accuracy remains an important challenge for EFL learners in academic writing tasks involving data interpretation. Therefore, it is recommended that grammar instruction be integrated more intensively into graph description activities in English classes. The findings are expected to contribute to the development of more effective teaching strategies in academic writing instruction for EFL learners.

The emergence of Large-Format Printing (LFP) technology has transformed global visual communication by enabling the production of large-scale, high-quality printed materials, significantly influencing Nigeria’s graphic design industry. This study aims to examine the impact of LFP on graphic design practice (GDP) in 21st-century Nigeria and propose strategies for sustainable development while addressing emerging challenges. A mixed-method approach was employed, with a primary qualitative focus through interviews and participant observation, supported by quantitative data collected using a Likert-scale questionnaire to assess designers’ perceptions of LFP’s influence. The findings reveal a dual impact. On the positive side, LFP has enhanced design scale, print quality, creative flexibility, and production efficiency, effectively overcoming the limitations of earlier methods such as letterpress printing, manual clamp offset printing, and hand-painted signboards. However, several challenges persist, including the oversaturation of practitioners, increased design piracy, and issues of color inconsistency, which undermine quality outcomes. These challenges highlight gaps in professional standards and regulation within the industry. The study concludes that while LFP has driven innovation and growth in Nigeria’s graphic design sector, its optimal benefits are constrained by the proliferation of unaccredited freelance designers. Therefore, the study recommends the implementation of targeted training programs and stricter professional accreditation systems through relevant regulatory bodies to ensure sustainable development and maximize the transformative potential of LFP technology.

The increasing complexity of urban road networks demands intelligent navigation systems capable of determining optimal routes efficiently. This research implements the Dijkstra Shortest Path algorithm to optimize route search on a location navigation system in Medan City. The system models a road network as a weighted graph comprising 57 strategic locations and over 90 road connections, represented using adjacency list data structures. The Dijkstra algorithm, implemented in Python using the heapq module for priority queue management, achieves an optimal time complexity of O((V+E) log V). The system features five main functions: shortest route search, popular routes, location listing, dynamic location addition, and dynamic road connection addition. System testing using a case study from Kualanamu Airport to the University of North Sumatra (USU) yielded an optimal route of 16.5 km through 4 road segments. Results demonstrate that the system successfully determines the most efficient route, provides accurate distance and travel time information for multiple transport modes (motorcycle, car, walking), and presents step-by-step journey guidance. This research contributes as a practical reference for applying shortest path algorithms in urban areas and serves as a foundation for developing more complex navigation applications in the future.

The increasingly complex global energy problem drives the need for efficient, low-cost, and environmentally friendly energy storage systems. This study aims to analyze the power performance of two symmetric supercapacitor prototypes using Nitrogen-doped Graphene-Like Carbon (N-GLC) as the electrode material and 1 M  as the electrolyte, with different electrode substrates: aluminum and copper. Both prototypes were tested through charging and discharging processes using resistive loads of 470 ohms and 560 ohms for 5 minutes. The analyzed parameters include voltage, current, power, and output energy. The results show that the aluminum electrode achieved a higher maximum charging power of up to 18 mW; however, its energy discharge efficiency was very low at only 0.87%. In contrast, the copper electrode demonstrated a more balanced charging and discharging performance with an energy discharge efficiency of 19.4%. Analysis also indicates that the copper substrate maintains better voltage stability after 6 hours of storage compared to aluminum, which experienced significant degradation. Thus, the copper electrode is superior in maintaining the power and stability of a simple N-GLC-based symmetric supercapacitor system.

This study aims to analyze students’ mathematical reasoning abilities in quadratic function material through the use of Desmos. The research employed a qualitative descriptive approach using a case study and usability testing method. Data were collected through post-tests and interviews involving students’ responses in solving quadratic function problems. The analysis focused on several indicators of mathematical reasoning, including procedural skills, conceptual understanding, and analytical ability. The results show that students generally demonstrate adequate procedural reasoning, particularly in substitution and computation tasks. They are also able to relate algebraic representations to geometric interpretations, such as determining intercept points and analyzing the direction of parabolas. However, limitations were found in conceptual understanding, especially in identifying graph characteristics without relying on procedural steps. The use of Desmos significantly supports the development of students’ mathematical reasoning by providing real-time visualization, facilitating exploration of function parameters, and enabling verification of results. Overall, Desmos contributes to enhancing procedural, conceptual, and analytical dimensions of mathematical reasoning, although its effectiveness depends on proper instructional design.

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.

This research aims to design and develop a simple Dino-themed game based on a microcontroller, with the display using an I2C LCD. The game is inspired by the offline Dino game on Google Chrome, adapted to run on an Arduino Uno microcontroller. The research method used is Research and Development (R&D), consisting of stages such as needs analysis, system design, simulation using Wokwi, hardware assembly, programming, and testing. The system uses push buttons as input and a 16x2 I2C LCD as the output display. The testing results show that the system can respond quickly to user input and display character movement and score updates dynamically on the LCD screen. Although the graphical capability of the LCD is limited, the game runs well and successfully demonstrates the basic concepts of microcontroller programming as well as interactive input-output processing. Further development is recommended to enhance the graphical interface and add features such as sound effects and progressive difficulty levels.

The development of digital content on the YouTube platform encourages creators to produce more engaging and informative podcast videos through the editing process. This study discusses the video podcast editing workflow of the Tikitalk program on the Ampu Studio YouTube channel using Adobe Premiere Pro. This research also aims to understand how the editing stages are carried out in improving the quality of video podcast presentation. The research method used is descriptive qualitative through direct observation during internship activities, so that the data is obtained based on real field experience. The results of the study show that the editing process, such as video cutting, transition settings, audio adjustment, audio censoring, and the addition of motion graphics, can significantly improve the quality of video podcast presentation. In addition, this process also helps make the content more organized, informative, and engaging for viewers. Thus, the role of the editor in the production process is very important in supporting the quality of digital content on the YouTube platform. Therefore, it can be concluded that editing is not only a technical process, but also an important part in increasing the attractiveness and professionalism of a video podcast.

Occupational Health and Safety (OHS) is a crucial aspect in manufacturing industries due to the high risk of workplace accidents caused by heavy machinery, chemical substances, and intensive production activities. Companies usually provide Standard Operating Procedures (SOP) as safety guidelines; however, SOPs are often delivered in long textual formats that are less engaging, making workers reluctant to read or difficult to understand quickly. This study aims to design an infographic-based SOP media as an effective visual communication tool to improve workers’ understanding of safety procedures. The research applies a qualitative method with a design approach through workplace observation, interviews with HSE personnel, literature review, and design validation using questionnaires. The results produce infographic media in the form of posters and signage presenting PPE usage procedures, hazard warnings, and evacuation steps using safety color codes, icons, and readable typography. The conclusion indicates that infographic SOP media is more effective than text-based SOP because it improves readability, comprehension, and workers’ memory of safety procedures.

Explainable artificial intelligence (XAI) has become a critical requirement in cybersecurity due to the high-stakes nature of security decision-making and the limitations of black-box learning models. This study investigates the construction of an explainable cybersecurity knowledge representation by leveraging standardized terminology from the NIST cybersecurity glossary. The primary problem addressed is the lack of transparent and semantically grounded reasoning mechanisms in existing AI-driven cybersecurity systems, which limits trust, accountability, and analyst adoption. To address this challenge, we propose a NIST-based semantic knowledge graph that embeds explainability directly into its ontology structure and reasoning process. The proposed framework systematically extracts definitional entities and relations from NIST glossary entries to construct a domain ontology and a multi-relational knowledge graph. A rule-based semantic relation extraction method is employed to ensure faithful, interpretable, and reproducible reasoning paths. The resulting knowledge graph contains over 3,000 cybersecurity concepts and approximately 27,000 semantic relations, covering hierarchical, associative, dependency, and mitigation semantics. Experimental evaluation demonstrates that the proposed approach achieves a high level of explainability, with 92.4% of reasoning outcomes being fully traceable and only 1.4% classified as non-traceable. Most explainable reasoning paths are limited to two or three hops, indicating an effective balance between inferential depth and human interpretability. Structural analysis further confirms the presence of meaningful hub concepts that support multi-hop semantic inference. These results confirm that ontology-driven, standard-based knowledge graphs provide a robust foundation for explainable cybersecurity intelligence. The study concludes that explainability-by-design, grounded in authoritative standards, offers a viable and trustworthy alternative to opaque AI models for cybersecurity applications.

Inconsistent terminology across cybersecurity frameworks undermines global governance and interoperability. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0) and ISO/IEC 27001:2022 share similar objectives but diverge semantically in defining risk, control, and resilience. This semantic gap causes difficulties in compliance mapping and automated policy translation. Research Objectives: This study aims to analyze the semantic similarity and divergence between NIST and ISO/IEC 27000 terminologies, identify conceptual structures influencing interoperability, and propose an AI-assisted foundation for harmonizing cybersecurity language globally. Methodology: A mixed-method semantic comparative design integrates Natural Language Processing (NLP) and ontology mapping. Using the nist_glossary.csv dataset and ISO vocabularies, terms were normalized and analyzed via cosine similarity using sentence-transformer embeddings. Ontological alignment was visualized through the Semantic Threat Graph (STG) and validated by certified experts using Cohen’s Kappa reliability tests. Results: From 672 term pairs, results show 40.9% high semantic equivalence, 38.8% partial overlap, and 20.3% semantic divergence. Strongest alignment appears in “Protect” and “Identify” domains, while divergences occur in governance and recovery-related terms. Ontology mapping revealed three conceptual clusters—Risk Governance, Technical Safeguards, and Organizational Readiness. Conclusions: Findings confirm a 79.7% total semantic alignment, indicating strong potential for harmonizing global cybersecurity standards. The study contributes an empirical model combining computational linguistics and AI-based ontology mapping to establish semantic interoperability, enabling unified cybersecurity governance and AI-driven compliance automation. Keywords: Semantic Interoperability; Ontology Mapping; Cybersecurity Frameworks; Terminology Alignment; AI Harmonization

The idea of functions in mathematics and how they are used to build different mathematical models are methodically examined in this publication. Functions are basic mathematical constructs that show relationships between two or more variables in explicit equations, tables, or graphs. The fundamental building blocks of mathematics are functions, which enable the representation of variable interdependencies in a variety of formats, including formal mathematical expressions, data tables, and graphs. The classification of function types, such as linear, quadratic, and exponential, and their corresponding uses in the domains of physics, economics, and epidemiology are the main topics of this study, which takes a descriptive and exploratory approach.This article illustrates how knowledge of functions greatly aids processes through a review of the literature and an examination of secondary sources from current textbooks and academic publications. of judgment, forecasting, and analysis. In both academic and professional contexts, mathematical modeling based on functions has demonstrated efficacy in accurately and efficiently representing real-world occurrences. Thus, the significance of incorporating functional thinking into STEM education and multidisciplinary practice is emphasized in this essay.

This study presents a Graph-Augmented Isolation Forest (GAIF), an unsupervised anomaly-detection framework for analyzing mobile user behavior. The proposed framework represents users and behavioral attributes as a user–feature bipartite graph, enabling the capture of relational dependencies that are not explicitly modeled in conventional vector-based approaches. Low-dimensional user representations are learned through Node2Vec and Graph Sample and Aggregate (GraphSAGE), and the resulting embeddings are subsequently processed by an Isolation Forest to produce anomaly scores. Experiments are conducted on a Mobile Device Usage and User Behavior dataset comprising 700 user profiles derived from application-level behavioral indicators. The dataset is treated as a behavioral abstraction rather than as a malware classification benchmark. A consistent 80:20 stratified train–test split is employed, with all learning-capable operations restricted to the training data to mitigate information leakage. Detection performance is evaluated post hoc using precision, recall, F1-score, and area under the curve (AUC) metrics. Under the evaluated setting, GAIF achieves an F1-score of 0.94 and an AUC of 0.97, demonstrating improved anomaly detection effectiveness relative to representative unsupervised baseline methods. These results are obtained on a static, proxy dataset and should not be interpreted as evidence of real-time deployment capability. Model interpretability is supported through post-hoc Uniform Manifold Approximation and Projection (UMAP) visualizations of the learned embeddings, providing structural insights into anomalous user behavior. Overall, the findings indicate that integrating graph-based representation learning with isolation-based anomaly scoring constitutes a computationally efficient approach for unsupervised mobile user behavior anomaly detection within the scope of this study.

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.