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City branding is a strategic approach to enhancing a city's competitiveness by leveraging unique local characteristics to create a strong and distinctive identity. This study examines the role of semanggi—a traditional Surabaya dish—as a cultural icon in strengthening the city's branding. Although semanggi carries deep symbolic meaning and historical significance for Surabaya’s residents, its potential as a city branding asset remains underutilized in formal tourism and marketing programs. Adopting a qualitative exploratory case study design, data were collected through in-depth interviews, participant observation, and document analysis involving local stakeholders, small and medium-sized culinary enterprises, cultural figures, and municipal representatives. Thematic analysis identified five key dimensions of semanggi’s role in city branding: (1) visual representation in urban spaces and promotional media; (2) cultural narratives and heritage preservation; (3) community pride and participation; (4) local economic resilience; and (5) strategic promotion by municipal authorities. The findings suggest that integrating semanggi into Surabaya’s branding strategy can reinforce cultural identity, differentiate the city in competitive tourism markets, and promote sustainable local entrepreneurship. The study contributes to the theoretical discourse on culinary-based place branding and offers practical recommendations for incorporating local wisdom into destination marketing. Limitations include the single-case scope and qualitative focus, pointing to opportunities for comparative and mixed-method research in the future.  

Advances in technology and research in the field of pharmacy have driven innovation in drug formulation and administration methods to improve the effectiveness and safety of therapy. One of the most widely developed dosage forms is suspension injections, which are sterile preparations containing particles of active ingredients that are insoluble in the carrier fluid. This study aims to review the effects of various administration routes, such as intra-articular, intramuscular, and subcutaneous, on the efficacy and safety of suspension injections. The method used is a literature study analyzing various scientific journals from databases published in the last ten years. The results of the review show that the route of administration has a significant effect on the therapeutic effect, where the intra-articular route provides a strong local effect with low systemic risk, the intramuscular route produces long-term effects and is relatively easy to perform, while the subcutaneous route provides stable drug release over a long period of time. Overall, the choice of injection route for suspension injections should be tailored to the characteristics of the drug, the patient's condition, and the therapeutic goals to achieve optimal, safe, and comfortable treatment outcomes for the patient.

Proton Exchange Membrane Fuel Cells (PEMFCs) are increasingly developed due to their high efficiency, power density, durability, and environmental friendliness. However, the high cost of platinum catalysts limits their widespread application. Reducing platinum usage through the development of low-cost catalyst support materials is a promising approach. Activated carbon derived from biomass offers a sustainable and economical alternative, particularly when utilizing agricultural waste such as coffee husks, which are often discarded. This study aims to evaluate the characteristics of activated carbon produced from coffee husk waste through pyrolysis as a catalyst support for PEMFCs. Pyrolysis was conducted at 400 °C, followed by chemical activation using 1 M H₃PO₄ and 1 M KOH with a residence time of 90 minutes. The resulting activated carbon was analyzed through proximate analysis to determine moisture content, ash content, volatile matter, and fixed carbon content. The results showed that activation with 1 M H₃PO₄ produced the lowest moisture content (3.4%), the lowest ash content (3.8%), and the highest fixed carbon content (60.5%), while 1 M KOH yielded the lowest volatile matter content (27.7%). Based on these results, activated carbon produced using H₃PO₄ meets the requirements of SNI No. 01/6235/2000 and demonstrates strong potential as a catalyst support material for PEMFC applications.

One of the most common occupational health risk factors found in industrial areas is noise, and long-term exposure ≥85 dB(A) for more than 8 hours has the potential to cause progressive and permanent Noise-Induced Hearing Loss (NIHL). The risk of NIHL increases if workers do not use Ear Protection Equipment (APT) optimally and have certain individual factors such as age, working period, and length of exposure. This study aims to determine the relationship between the use of APT and worker factors with hearing loss due to noise in PT HMK II Palembang City in 2025. This study uses an analytical quantitative design with a cross sectional approach. A sample of 55 respondents was selected through purposive sampling technique. Data was collected using a questionnaire and analyzed by chi-square test. The results showed that there was a significant relationship between the use of APT (p=0.000) and there was no significant relationship between, the age of workers (p=0.838), working time (p=0.290), and length of work (p=0.172) and noise-induced hearing loss. The use of APT has been shown to be associated with a reduced risk of noise-induced hearing loss, although age, length of employment, and length of work did not show a significant relationship.

One effort to enhance students’ engagement, learning interest, and conceptual understanding in Natural Science learning at the elementary school level is through the use of interactive learning media that are aligned with learners’ characteristics. Interactive learning media are considered capable of creating a more active and meaningful learning environment. This study aims to evaluate the use of ClassPoint as an interactive learning medium based on live quizzes on the topic of Energy and Its Transformations for fifth-grade elementary school students. The research method employed is a Systematic Literature Review (SLR) by examining various relevant and credible scientific articles obtained from the Google Scholar, Garuda, and Directory of Open Access Journals (DOAJ) databases. The findings indicate that the use of ClassPoint in science learning can significantly increase students’ participation, activeness during the learning process, and learning motivation. In addition, students’ responses to the use of ClassPoint generally show positive attitudes, as learning is perceived to be more engaging, interactive, enjoyable, and easier to understand. However, the successful implementation of ClassPoint in elementary school science learning requires adequate facilities and infrastructure as well as teacher training. The challenges encountered include limitations in technological infrastructure, the readiness of supporting devices, and teachers’ ability to effectively optimize the features of learning media.

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 consumption of packaged food products in Indonesia reflects modern lifestyle changes but simultaneously raises public health concerns related to high calorie, sugar, and fat intake. Nutritional information presented on food labels consists of multiple interrelated variables, making it difficult to identify dominant nutritional factors that characterize packaged food products. This study aims to apply Principal Component Analysis (PCA) to reduce the dimensionality of nutritional data and to map the nutritional characteristics of packaged food products in Indonesia. The research employs a quantitative exploratory approach using secondary data obtained from nutrition facts labels of 1,651 packaged food products. Seven nutritional variables were initially analyzed, namely total energy, protein, total fat, total carbohydrates, sugar, sodium, and dietary fiber. Data preprocessing included data cleaning, Z-score standardization, and iterative variable selection based on the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity to ensure sampling adequacy and sufficient correlation among variables. Variables with low sampling adequacy and perfect multicollinearity were eliminated, resulting in five variables retained for the final PCA model. Principal components were extracted using the eigenvalue greater than one criterion and confirmed through a scree plot, followed by Varimax rotation to enhance interpretability. The results indicate the formation of two principal components explaining approximately 69.7% of the total variance. The first component represents energy density and macronutrient richness, while the second component reflects carbohydrate-related characteristics, particularly the contrasting pattern between sugar and dietary fiber. Biplot visualization further illustrates product distribution based on these components. The findings demonstrate that PCA effectively simplifies complex nutritional information and provides a clear nutritional mapping of packaged food products, offering practical insights for consumers, producers, and policymakers in supporting healthier food choices in Indonesia.

This study examines the relationship between Corporate Social Responsibility (CSR) disclosure and tax avoidance, with CEO Overconfidence considered as a moderating factor. The research focuses on non-cyclical consumer goods companies listed on the Indonesia Stock Exchange during the 2022-2024 period. Using annual and sustainability reports, the analysis employs multiple linear regression and moderated regression analysis (MRA). Robust standard errors based on the Newey-West method are applied to ensure reliable estimation. The results indicate that CSR disclosure does not have a significant direct effect on tax avoidance. However, CEO Overconfidence significantly moderates the relationship between CSR disclosure and tax avoidance, highlighting the role of executive behavioral characteristics in corporate tax decisions. These findings suggest that CSR disclosure alone is insufficient to explain firms’ tax avoidance behavior without considering managerial traits. The study contributes to the literature by integrating behavioral perspectives into tax avoidance research and emphasizing the importance of executive oversight in aligning CSR practices with responsible tax behavior.

This community service activity aims to strengthen the marketing strategy of FUNN Maumere as a culinary business operating in an increasingly competitive market environment. The program was conducted using a qualitative descriptive approach through field observations, interviews with business owners and employees, and direct mentoring in implementing more effective marketing strategies. The results indicate that FUNN Maumere has applied a combination of traditional and digital marketing strategies, including brochure distribution and active promotion through social media platforms such as Instagram, TikTok, and Facebook. The mentoring activities focused on optimizing digital marketing, improving service quality, and strengthening brand consistency. These efforts contributed positively to increased brand awareness, customer interest, and wider promotional reach. However, sales performance remains fluctuative due to external factors, such as weather conditions and differences in outlet location characteristics. Therefore, this community service emphasizes the importance of continuous product innovation, enhancement of customer experience, and optimization of digital marketing strategies to reduce dependency on external conditions. This activity is expected to serve as a practical reference and mentoring model for other culinary MSMEs in developing adaptive, innovative, and sustainable marketing strategies to improve competitiveness and business continuity.

Electronics learning in higher education continues to face various challenges, particularly in the provision of interactive learning media capable of concretely and engagingly visualizing the form, characteristics, and working principles of electronic components. The limitations of conventional learning media often result in abstract learning processes, which in turn lead to a low level of student understanding of basic electronics concepts. This study aims to develop the AMPERE as an innovative and technologically relevant interactive learning medium. The research employed R&D approach using the Borg & Gall model, which includes the stages of needs analysis, design, product development, validity testing, and limited implementation. The AMPERE application was developed using marker based AR technology, in which a smartphone camera detects markers to display and interact with 3D electronic component objects in real time. The results indicate that the AMPERE application achieved a high level of validity, with a score of 0.88 from subject-matter experts and 0.84 from media experts, and was therefore deemed suitable for use as a learning medium. The small-group trial results showed a practicality level of 82.07%, while the practicality test during the implementation stage reached 85.67%. These findings demonstrate that AMPERE is effective in enhancing learning interactivity and assisting students in understanding the form, function, and working principles of electronic components through smartphone-based digital visualization. Theoretically, these results are consistent with constructivist learning theory, which emphasizes active knowledge construction through direct experience and interaction with learning objects.

Sleep is a fundamental biological need for adolescents and plays a crucial role in supporting cognitive function, emotional regulation, and overall mental health. Despite its importance, sleep quality among adolescents has increasingly declined due to the interaction of individual characteristics, social environments, and daily sleep behaviors. This article aims to analyze the factors influencing adolescent sleep quality using the Social Cognitive Theory framework through a literature review of national and international journals published between 2021 and 2025. The findings show that adolescent sleep quality is shaped by personal factors, including attitudes toward sleep, self-efficacy, and psychological conditions. Environmental factors, such as parental support, peer influence, and academic demands, also significantly affect sleep patterns. In addition, behavioral factors, particularly sleep hygiene practices, play a key role in determining sleep quality. Consistently, unhealthy sleep behaviors are associated with poor sleep quality. Therefore, comprehensive interventions are needed, involving the improvement of sleep habits, strengthening adolescents’ self-regulation abilities, and enhancing family and school support to promote better sleep quality.

This study aims to analyze teachers' teaching strategies in improving the quality of science learning for upper grade students (IV, V, and VI) at the SD Inpres Perumnas II Makassar. The results show that teachers implement various strategies including selecting approaches that suit students' needs, using a variety of learning methods, actively involving students in learning activities, utilizing media that support conceptual understanding, and implementing continuous evaluation to assess learning success. All of these strategies complement each other and help create more engaging, understandable, and relevant science learning for students. Research findings also revealed that the choice of teaching strategy is influenced by several important factors: student characteristics, the characteristics of the science material being taught, the availability of learning media and facilities, the learning environment, and the desired learning objectives. These five factors serve as the basis for teachers to determine the most appropriate strategy, ensuring a more effective, focused learning process that enhances students' understanding of the material. Furthermore, this study identified several challenges teachers face in implementing science teaching strategies, such as diverse student abilities, time constraints, a lack of supporting media and facilities, sub-conducive classroom conditions, and uneven student motivation. These challenges require teachers to be creative and adapt strategies to ensure learning objectives are achieved and the quality of science instruction continues to improve.

This study aims to improve the quality of gallon water products and strengthen the competitiveness of CV. Anugerah Gemilang through the implementation of Quality Function Deployment (QFD) using the House of Quality (HoQ). The QFD approach is employed to translate the Voice of Customer (VoC) into technical product characteristics as well as service quality attributes that influence customer satisfaction. The research method applied is quantitative-descriptive with the support of qualitative data. Quantitative data were collected through questionnaires distributed to customers to measure the importance and satisfaction levels regarding product and service attributes, while qualitative data were obtained through in-depth interviews to reinforce and validate the questionnaire results. Subsequently, the VoC data were analyzed using the HoQ matrix to determine quality improvement priorities based on customer importance weights. This study is expected to produce strategic recommendations in the form of prioritized product and service quality enhancements focused on customer satisfaction, thereby supporting the sustainable improvement of the company’s competitiveness.

This study develops a computational-based system to identify individual potential through the analysis of signature patterns using Artificial Neural Networks (ANN) and the Backpropagation algorithm. The research aims to explore and examine the effectiveness of applying ANN in recognizing and identifying signature patterns that are assumed to be related to an individual’s potential. In the data processing stage, Principal Component Analysis (PCA) is employed as a dimensionality reduction and feature extraction technique to optimally obtain the main characteristics of signature images. The system performance evaluation is conducted using a total of 80 signature images, consisting of 60 training data and 20 testing data. This study analyzes two network architecture configurations, namely a model with one hidden layer and a model with two hidden layers. The experimental results show that both network configurations achieve the same accuracy level of 92.5%. These findings indicate that the use of Artificial Neural Networks with the Backpropagation algorithm is effective in producing high accuracy in the signature pattern recognition process. Furthermore, the developed system has broad potential applications in the field of personal identification, such as employee evaluation, selection systems, and other applications across various organizational and industrial sectors.

This study aims to improve the quality of gallon water products and strengthen the competitiveness of CV. Anugerah Gemilang through the implementation of Quality Function Deployment (QFD) using the House of Quality (HoQ). The QFD approach is employed to translate the Voice of Customer (VoC) into technical product characteristics as well as service quality attributes that influence customer satisfaction. The research method applied is quantitative-descriptive with the support of qualitative data. Quantitative data were collected through questionnaires distributed to customers to measure the importance and satisfaction levels regarding product and service attributes, while qualitative data were obtained through in-depth interviews to reinforce and validate the questionnaire results. Subsequently, the VoC data were analyzed using the HoQ matrix to determine quality improvement priorities based on customer importance weights. This study is expected to produce strategic recommendations in the form of prioritized product and service quality enhancements focused on customer satisfaction, thereby supporting the sustainable improvement of the company’s competitiveness.

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.

The objective of this study is to comprehensively examine and analyze the influence of microstructure on the mechanical properties of metallic materials. Microstructure is known to play a crucial role in determining the mechanical behavior of metals; therefore, a thorough understanding of this relationship is essential for the development of engineering materials. This study adopts a systematic literature review approach, employing descriptive analysis and meta-analysis of recent scientific publications obtained from various reputable academic databases. The analysis results indicate that microstructure is a significant determinant of the mechanical characteristics of metallic materials, including strength, ductility, and resistance to deformation. The most influential microstructural parameters include grain size, phase distribution, crystallographic orientation, dislocation density, and the presence and characteristics of precipitates. Among these parameters, grain size has been shown to be the most dominant factor. The Hall–Petch strengthening mechanism demonstrates that grain refinement can increase the tensile strength of materials by approximately 200 to 300 percent. In addition, materials with multi-phase microstructures, such as dual-phase steels and TRIP steels, exhibit an excellent combination of strength and ductility compared to single-phase materials. Based on the meta-analysis results, each metallic material system exhibits a trade-off between strength and ductility, whereby an increase in strength is generally accompanied by a reduction in ductility. These findings indicate that achieving an optimal combination of mechanical properties for specific application requirements necessitates a comprehensive and integrated microstructural engineering approach.

The rapid development of globalization and the acceleration of digital transformation have encouraged organizations to adopt more adaptive and collaborative work practices. In this context, collaborative culture has become a strategic element that plays a crucial role in enhancing organizational effectiveness and competitiveness. This study aims to comprehensively examine the concept, characteristics, forming factors, and theoretical foundations of collaborative culture in modern organizations. The research employs a qualitative approach through a literature review, analyzing reputable national and international journal articles, textbooks, and relevant institutional reports. Data analysis is conducted using a descriptive-analytical technique by synthesizing findings from previous studies. The results indicate that collaborative culture significantly contributes to improved communication quality, work coordination, adaptability, and both individual and organizational performance. Collaborative culture is shaped through the integration of shared vision, open communication, trust, willingness to share resources, collaborative leadership, flexible organizational structures, and the support of collaborative technologies. This study also highlights that the success of digital transformation largely depends on the strength of an effectively internalized collaborative culture. The findings are expected to provide a theoretical reference for organizations and researchers in developing sustainable collaborative culture strategies.

Highways play a strategic role in supporting economic development, so the quality of pavement materials (flexible pavement) must meet previously established quality standards. The Asphalt Concrete Wearing Course (AC-WC) layer, as the topmost layer of flexible pavement, is susceptible to damage caused by aging triggered by environmental conditions, particularly ambient temperature. Temperatures above 30°C are known to accelerate plastic deformation (rutting) and reduce pavement bearing capacity. The Meteorology, Climatology, and Geophysics Agency indicates that temperatures in Lhokseumawe City, North Aceh, range from 33.7–35.3°C with day-night fluctuations that could potentially accelerate the degradation of mechanical properties. This study aims to analyze the Marshall characteristics of AC-WC asphalt mixtures under ambient temperature conditions. The testing was conducted with varying exposure times of 0, 15, 30, 45, 60, 75, and 90 days at a minimum temperature of 23.90°C and a maximum temperature of 31.80°C, with an average humidity of 87.93%. The test results showed that the AC-WC asphalt mixture experienced a decrease in stability value of 21.37% and an increase in flow value of 36.36%, resulting in a decrease in the Marshall Quotient (MQ) value of 42.52%.

This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets and detecting emerging malware variants, the study employed SMOTE to ensure balanced training data and fair model evaluation. The dataset was divided into 80% training and 20% testing subsets, and models were assessed using key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. The findings revealed that the proposed Random Forest model outperformed the other classifiers, achieving an accuracy of 0.993, precision of 0.992, recall of 1.000, F1-score of 0.996, and a near-perfect ROC AUC of 0.9998 surpassing state-of-the-art approaches. These results affirm the superior predictive capability, consistency, and robustness of the Random Forest algorithm in Android malware detection. The study concludes that base models, when integrated with class-balancing techniques, provide reliable and efficient malware detection across imbalanced datasets. For future research, the study recommends exploring advanced hybrid or ensemble frameworks that integrate Random Forest with deep learning architectures or other meta-heuristic optimization techniques to further enhance detection accuracy, adaptability, and resilience against rapidly evolving Android malware threats.