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This research focuses on the design and evaluation of a novel parallel graph optimization algorithm incorporating dynamic load balancing (DLB) to address inefficiencies in heterogeneous computing environments. Large-scale graph optimization problems, such as those in social networks, bioinformatics, and transportation systems, often suffer from computational imbalances when using traditional static load balancing approaches, leading to underutilized resources and prolonged execution times. The primary objective of this research is to develop an algorithm that can dynamically adjust workload distribution across processors, enhancing computational efficiency and scalability. The proposed method combines heuristic techniques, including region expansion and multilevel partitioning, with diffusive load balancing strategies to minimize inter-processor communication overhead. Experimental results demonstrate that the proposed algorithm reduces execution time by up to 40% compared to static methods, with optimized resource utilization and more balanced workload distribution. The scalability of the algorithm is also evident, as it adapts effectively to increasing problem sizes and processor counts. These findings suggest that dynamic load balancing is crucial for improving parallel graph optimization in real-world applications. Future work will focus on further enhancing the algorithm’s responsiveness to rapidly changing workloads and expanding its applicability to additional domains.

Traditional association rule analysis is effective at uncovering co-purchase patterns but fails to provide a global structural view of the market, which often results in fragmented and isolated insights. This study proposes a hybrid framework that integrates the Apriori algorithm with a Minimum Spanning Tree (MST) in order to validate and contextualize association rules within a single structural backbone. Transaction data from a retail store are transformed into a weighted, undirected product graph using an inverse-support function, and an MST is then extracted to represent the market backbone, while frequent itemsets and strong rules are obtained using Apriori. Experimental results on 236 multi-item transactions show that the MST backbone comprises 10 products and 9 fundamental links, with 66.67% of these links being confirmed by strong association rules, indicating a substantial coherence between statistical and structural evidence. The proposed model identifies 41 Apriori patterns that can be embedded in the MST and ranks them using a new metric, Structural Distance, which enables the categorization of Core Patterns, Bridge Patterns, and Complex Patterns according to their structural tightness. This hybrid perspective distinguishes dense, strategically meaningful bundles from anomalous but frequent combinations that are structurally peripheral, thereby offering a more holistic and actionable alternative to conventional Market Basket Analysis. The validated framework can support various applications, including store layout optimization, cross-selling strategies, and the design of path-based recommender systems, and it opens avenues for future extensions based on dynamic graphs and Graph Neural Networks.

The gap between academic curriculum content and modern industrial needs is often an obstacle for fresh graduates in the Information Technology field, particularly in the rapidly evolving Artificial Intelligence (AI) sector. This study aims to identify the relationship patterns among technical competencies (hard skills) most demanded by the global industry. The method employed is Association Rule Mining with the Apriori algorithm to discover association rules between skills, and Network Graph Analysis to visualize the topological map of these competencies. The research dataset covers 15,000 AI job vacancies from the 2024-2025 period, analyzed in depth using Support, Confidence, and Lift Ratio evaluation parameters to validate the strength of relationships between items. The results show that Python is the central competency with the highest frequency of occurrence. Strong association rules were found indicating that proficiency in TensorFlow has a high probability of requiring Python proficiency. The Network Graph visualization reveals three main competency clusters: Data Engineering Ecosystem, Deep Learning, and Infrastructure. These findings offer a strategic foundation for aligning curricula with the job market. Focusing on strengthening the identified competency clusters is expected to directly enhance the relevance and work readiness of graduates.

This study aims to develop an educational application based on a Graphical User Interface (GUI) using MATLAB App Designer that functions as an interactive simulation for evaluating blood pressure. The application allows users to input systolic and diastolic blood pressure values along with supporting information such as name and age. The input data are then analyzed and classified into several blood pressure categories according to the standards of the American Heart Association (AHA), including normal, hypotension, stage 1 hypertension, stage 2 hypertension, and hypertensive crisis. The classification results are presented visually through an interactive pie chart with dynamic percentages and legends to enhance user understanding. In addition, all data are automatically stored in a Microsoft Excel file containing a summary of blood pressure categories and session timestamps. The system is designed with a simple interface and intuitive interaction, making it suitable for early health education purposes. Although the application still relies on manual data input, it has the potential to serve as an effective learning tool for increasing public awareness of the importance of regular blood pressure monitoring. 

In the 21st century learning era that demands the integration of 4C skills and digital literacy, the textbook Akidah Akhlak Class IV MI based on the 2019 KMA 183 Curriculum needs to be evaluated for its suitability to be relevant in shaping students' Islamic character. This study aims to analyze the suitability of the book's content, presentation, language, and graphics according to BSNP standards, with a focus on the strengths of active learning and the weaknesses of technology integration such as QR codes and science-technology interconnections. The descriptive qualitative method through library research uses the 2020 Kemenag textbook by Subkhiaton Noor as primary data, supported by BSNP instruments and comparisons with other books. The results show that the book is very suitable overall: very good content suitability (accurate material, relevant KI-KD, supports 4C through discussion and reflection), good presentation (systematic structure, HOTS-LOTS, glossary), good language (communicative, appropriate to students' cognition), graphics pass with minor improvements in visual consistency, so it is effective for contextual learning even though it needs additional digital features.

This study investigates the impact of GeoGebra-assisted collaborative learning on students' understanding of function graphs. Function graphs are fundamental in mathematics education, yet many students struggle to grasp the relationships between variables, primarily due to traditional teaching methods that focus on procedural skills rather than conceptual understanding. To address this challenge, the study incorporates GeoGebra, a dynamic mathematics software, alongside collaborative learning strategies. The research utilizes a quasi-experimental design involving high school students who had previously struggled with function graphs. The results demonstrate that the experimental group, which engaged in GeoGebra-assisted collaborative learning, showed a significant improvement of 27% in their post-test scores, compared to just a 6% improvement in the control group using traditional methods. The study highlights the effectiveness of GeoGebra in fostering a deeper conceptual understanding of mathematical functions by enabling students to visualize and manipulate graphs interactively. Additionally, collaborative learning encouraged peer interaction, reinforcing the learning process and promoting better problem-solving skills. The findings suggest that combining interactive tools like GeoGebra with collaborative learning techniques can enhance students’ mathematical comprehension, leading to improved engagement and performance in mathematics education.

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

Children with emotional and behavioral difficulties are children who have trouble adjusting to their social environment. This can affect their academic and functional abilities in daily life. Children with emotional dysregulation and aggressive behavior are particularly vulnerable to experiencing a series of social problems due to their emotional and behavioral difficulties. Therefore, emotional and behavioral intervention is needed to help them develop new adaptive behaviors and reduce maladaptive behaviors. One intervention method that is often used to reduce children's abberant behavior is CBT, especially the CBT-AAC model, which focuses on three dimensions, namely anger management skills, problem-solving skills, and social skills. This study used the SSR (Single Subject Research) research model on a 15-year-old boy with behavioral problems (Conduct Disorder) with symptoms of aggressive dominance. The CBT program was implemented with a total of 5 meetings using various approaches that focused on the child's aggressive problems. The behavior frequency graph then showed a significant decrease in aggressive behavior and positive changes in the cognitive process.

In the book Bina Belajar Al-Qur'an dan Hadith for grade V MI published by Erlangga Publisher in accordance with the madrasah curriculum (KMA) No. 183 of 2019, this book is analyzed by researchers in meeting the needs of 21st century learning, especially in developing critical thinking skills, creativity, communication, and collaboration. This study aims to analyze the feasibility of textbooks based on four aspects of the National Education Standards Agency (BSNP) standards, namely the feasibility of content, feasibility of presentation, language and graphics. This study uses a literature study approach (library study) which is used to collect various data sources from the book Bina Belajar Al-Qur'an dan Hadith, journals and others. The results of the study show that this book is in the very feasible category, with the suitability of the material to KMA No. 183 of 2019, the use of communicative language, and consistent graphic displays. However, deficiencies were found in the presentation aspect, such as the absence of concept maps, learning objectives, and the lack of contextual illustrations. Overall, this book remains relevant and can support the learning of the Qur'an and Hadith in MI.

Textbooks are the heart of the learning process, therefore an in-depth analysis of the suitability, relevance, and pedagogical potential of textbooks is crucial to ensure that learning outcomes can be achieved optimally. This study aims to analyze and describe based on aspects of content, presentation, language, graphics in the Indonesian language textbook for grade II elementary school revised independent curriculum published by Erlangga by Dr.A. Indradi, M.Pd & Rahmah Purwahida, S.Pd., M.Hum. The method used is qualitative descriptive analysis by referring to textbook feasibility guidelines. The results of the analysis show that this textbook is generally suitable for use. The suitability of the material with learning outcomes includes the development of four language skills, namely listening, speaking, reading, and writing, which are presented in an integrated and contextual manner. The presentation of the material is considered effective through the use of simple language, logical flow, and varied assignments. Overall, this textbook is a relevant and supportive learning resource in implementing the independent curriculum at the grade II elementary school level.

This research aims to develop GeoQuest, an interactive learning media integrated with Artificial Intelligence (AI), to enhance the understanding of trigonometry concepts among Grade XI students at SMAN 1 Percut Sei Tuan. The development follows the Research and Development (R&D) approach using the ADDIE model, which consists of Analysis, Design, Development, Implementation, and Evaluation. The product integrates AI-based features such as adaptive practice questions, automated feedback, and dynamic visualizations of trigonometric graphs. Data were collected through expert validation, student response questionnaires, and learning outcome tests. The results show that the media meets the criteria of validity, practicality, and effectiveness. Material experts and media experts rated GeoQuest as "very valid," while students responded positively to its ease of use and engaging interface. Learning outcome tests indicate a significant improvement in students’ understanding of trigonometry after using the AI-based GeoQuest media. Thus, GeoQuest is proven to be a reliable and effective digital learning tool to support trigonometry learning.

Recommender systems play a critical role in shaping user decisions across digital platforms; however, the increasing complexity of recommendation algorithms has raised serious concerns regarding transparency, trust, and accountability. This study focuses on enhancing the transparency of recommender systems by integrating Explainable Artificial Intelligence (XAI) techniques within a MovieLens-based recommendation framework. The primary problem addressed is the opacity of conventional recommendation models, which limits user understanding of why certain items are recommended and may reduce trust, perceived fairness, and system acceptance. Accordingly, the main objective of this research is to design and evaluate a hybrid explainable recommender system that balances predictive accuracy with human-understandable explanations. The proposed approach combines Matrix Factorization, feature-importance-aware neural networks, and knowledge graph embeddings to construct a robust recommendation model. To enhance explainability, multiple XAI strategies are integrated, including model-agnostic methods (LIME, SHAP, and CLIME), argumentation-based explanations, and context-aware personalized explanations. A comprehensive evaluation framework is employed, incorporating algorithmic metrics (accuracy, fidelity, robustness, counterfactual consistency, and fairness) alongside human-centered evaluations measuring trust, transparency, cognitive load, and perceived usefulness. Experimental results demonstrate that the knowledge graph–enhanced hybrid model achieves superior recommendation accuracy compared to baseline approaches. Moreover, context-aware explanations consistently outperform other methods in terms of fidelity, robustness, and user-perceived transparency, while argumentation-based explanations are found to be the most persuasive. CLIME offers a strong balance between technical stability and interpretability. The findings indicate that no single explainability technique is universally optimal; instead, hybrid and adaptive explanation strategies are most effective. In conclusion, this study confirms that human-centered, context-adaptive XAI significantly improves transparency and user trust in recommender systems, highlighting explainability as a fundamental component rather than an optional enhancement.

The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.

The concept of supremum is fundamental in real analysis and plays a crucial role in the optimization of single-variable real functions. In practice, not all functions attain their supremum explicitly, which necessitates numerical approaches to evaluate their behavior computationally. This study aims to analyze the supremum of several one-dimensional real functions with different characteristics using a grid-search method implemented in Python. Four functions were examined: a parabolic function, a rational function with a sharp peak, a discontinuous piecewise function, and a function with a vertical asymptote. The analysis involved modeling the functions, discretizing the domain, performing numerical approximation of the supremum, verifying the results against analytical values, and using graphical visualization to observe the function behavior near the supremum. The findings indicate that the supremum of the parabolic, rational, and piecewise functions can be accurately identified, with results consistent with analytical expectations despite minor deviations caused by grid resolution limitations in the rational function. Meanwhile, the function with a vertical asymptote yields an unbounded supremum, which cannot be attained within the domain. These results demonstrate that Python provides stable and reliable numerical estimates of the supremum across various types of one-dimensional real functions, validating the effectiveness of computational methods in supporting conceptual understanding of supremum.

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 rapid advancement of technology has made it easier for the public to access online loans (pinjol), but it has also increased the risk of misuse by illegal entities. The Financial Services Authority (OJK) recorded that from 2018 to 2022, it shut down 4,265 illegal online lending platforms. However, many cases continue to emerge due to the ease of creating applications and the use of overseas servers. Teachers are among the most affected victims due to low salaries and high living costs. The impacts of illegal online loans are highly detrimental, including the leakage of personal data, threats, intimidation, and excessively high interest rates. Education is crucial to prevent new victims. Isometric motion graphics are chosen as a medium because of their advantages in delivering information through engaging, clear, and easily understood visual and audio elements that are sustainable for audiences.

This Community Service (PKM) activity aims to improve artificial intelligence (AI) literacy in students majoring in Visual Communication Design (DKV) at SMK Muhammadiyah 2 Palembang through Canva-based training. The development of AI technology has brought about a significant change in the world of graphic design, where features such as Magic Design, Text-to-Image, and Background Remover are now widely used to support the creative process. However, most vocational school students are still passive users, lacking an understanding of the AI principles behind the technology. Through a Project-Based Learning (PjBL) and Active Learning approach with a demo–practice–reflection model, this activity is designed so that students are not only able to use the AI features in Canva, but also understand the work concepts, benefits, and ethics of using them. The activity was conducted over two days, involving 30 DKV class XI students. The evaluation of the results showed a significant increase in the aspects of understanding AI concepts (48%), motivation to learn design (35%), and creativity of works (32%). Students also show an increase in learning independence and digital ethical awareness. These results confirm that AI-based learning through Canva is effective in building AI literacy, creativity, and student readiness to face the technology-based creative industry. This activity is recommended to be expanded to teachers and other departments to strengthen digital literacy in the vocational education environment.  

This study aims to analyze the effect of asset growth, capital structure, and asset structure on the profitability of PT Astra Graphia Tbk during the period 2016–2023. The research employs a quantitative approach with a causal research design using secondary data derived from the company’s quarterly financial statements. A total of 32 quarterly observations were selected through purposive sampling. Profitability is measured using Return on Equity (ROE), while data analysis is conducted using multiple linear regression. Prior to hypothesis testing, classical assumption tests including normality, multicollinearity, heteroskedasticity, and autocorrelation tests were performed to ensure the robustness of the regression model. The results indicate that asset growth, capital structure, and asset structure simultaneously have a significant effect on firm profitability. However, partially, only asset structure has a significant effect on profitability, while asset growth and capital structure show no significant influence. These findings suggest that efficient asset composition plays a more critical role in improving profitability than mere asset expansion or increased leverage. The managerial implication of this study highlights the importance of optimizing asset structure to enhance the firm’s ability to generate sustainable profits.

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in the economy of Ketapang Regency but still face challenges in financial recording and management. Many MSME actors have not yet utilized digital technology optimally, leading to manual bookkeeping processes that are prone to errors. This study aims to develop a web-based financial bookkeeping application using the User-Centered Design (UCD) approach, focusing on user needs. The UCD method was applied through four stages: understanding the context of use, specifying user requirements, designing solutions, and evaluating the results. The developed application includes key features such as product management, supplier management, sales recording, receipt printing, and financial reporting. Based on usability testing involving 25 respondents, the application achieved an average satisfaction level of over 85% across aspects of learnability, efficiency, memorability, error handling, and satisfaction. The findings indicate that the application effectively supports MSME actors in recording financial transactions more efficiently, accurately, and reliably. Future improvements may include the integration of digital payment systems, enhanced data security, and interactive graphical financial analysis features.

Let G=(V(G),E(G)) be a connected graph and c be a coloring of the graph G. Let ∏={S_1,S_2,...,S_k }, where S_i is the class of colors in G which is colored i with 1≤i≤k. The representation of v with respect to Π is called a color code, denoted c_Π (v) is a k-element ordered pair, that is, c_∏ (v)=(d(v,S_1 ),d(v,S_2 ),...,d(v,S_k )), where d(v,S_i )=min{d(v,x)∣x ϵ S_i } for 1≤i≤k. If each vertex in G has a different color code then c is called a location coloring. The minimum number of colors used in the location coloring of a graph G is called the Location chromatic number with