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  Red dragon fruit contains bioactive compounds such as vitamin C, flavonoids, and polyphenols that have antioxidant potential and possess natural pigments that can be used as cosmetic colorants. This study aims to formulate and evaluate a blush-on powder preparation from dragon fruit pulp extract as an alternative natural colorant. The evaluation includes organoleptic testing, homogeneity, pH, irritation, spreadability, fineness, moisture content, and antioxidant activity. The results showed that the formulation met most of the requirements, exhibiting a pink color, a smooth texture, no odor, homogeneity, a pH of 6 suitable for the skin, no irritation, even spreadability within 4 applications, a fineness degree of 97.6%, and a moisture content of 6.91%. Antioxidant activity showed an IC₅₀ value of 0.3036 ppm (very strong), although the relative inhibition percentage was low (18.35%). Thus, dragon fruit extract has the potential as a natural ingredient in blush-on powder that provides both color and antioxidant benefits.

This review article evaluates the physical characteristics and stability of Oil in Water (O/W) and Water in Oil (W/O) creams through a comparative literature study published between 2021 and 2026. The evaluation focuses on important parameters, including viscosity, spreadability, adhesion, pH, and accelerated stability testing. The findings reveal that O/W creams generally have lower viscosity and greater spreadability, making them more suitable for daily cosmetic and therapeutic applications due to their lighter texture and better aesthetic acceptance. In contrast, W/O creams demonstrate higher physical stability, stronger adhesion, and superior occlusive properties, which are beneficial for protecting sensitive active ingredients and maintaining skin hydration over longer periods. The differences in performance are strongly influenced by the optimization of emulsifier Hydrophilic-Lipophilic Balance (HLB) values and the ratio between oil and water phases. Both cream types show good biocompatibility with skin pH ranging from 4.5 to 6.5, indicating their safety and effectiveness for topical pharmaceutical and cosmetic formulations.

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.

The rapid development of technology in the era of the Industrial Revolution 4.0 has driven the education sector to continuously adapt to the evolving demands of digital-based industries. One of the key technological innovations supporting this transformation is the Internet of Things (IoT), which enables data acquisition, real-time monitoring, and remote control of systems through internet networks. In response to these developments, a community service program was conducted to enhance the understanding and technical skills of students at SMK Negeri 1 Sindang through the provision and utilization of an IoT Trainer Kit Simulator as a practical learning medium. This activity aimed to bridge the gap between theoretical knowledge and industry-relevant technological applications by introducing students to hands-on IoT system implementation. The program included demonstrations and guided practice on the use of sensors, microcontrollers, and web-based monitoring platforms to simulate real-world industrial scenarios. The results indicate that students showed high enthusiasm and active participation throughout the activity. Moreover, participants were able to grasp the fundamental concepts of IoT systems, understand component integration, and recognize the relevance of IoT applications in supporting automation and digital transformation. Overall, this community service activity contributed positively to strengthening students’ digital competencies and preparedness for the demands of the contemporary industrial and technological landscape.

Natural fiber based composite materials are increasingly being developed as an environmentally friendly alternative to synthetic fiber-based composites. This study aims to characterize the thermal and mechanical properties of natural fiber composite materials and evaluate their potential use as sustainable materials. Composites are made using natural fibers as reinforcement and a polymer matrix through a specific molding method. Mechanical property characterization includes tensile tests, flexural tests, and impact tests, while thermal property characterization is carried out using thermal analysis to determine the thermal stability of the material. The test results show that the addition of natural fibers has a significant effect on improving the mechanical properties of the composite, especially tensile strength and elastic modulus, compared to the unreinforced matrix. In addition, natural fiber composites show quite good thermal stability over a certain temperature range, making them suitable for non-structural applications. Based on these results, natural fiber composite materials have the potential to be developed as environmentally friendly materials that have competitive mechanical and thermal performance.

This community service activity aims to design a prototype of a web-based Intellectual Property Rights (IPR) registration information system at the South Sumatra Provincial Industry Office as an effort to support the improvement of public service quality. The main problem faced by partners is the manual registration and management process for IPR data, which has the potential to cause service delays, data recording errors, and difficulties in monitoring the status of IPR applications. This condition requires an information technology-based solution that can improve the efficiency, effectiveness, and transparency of services. The method used in this community service activity is the prototype method, which includes the stages of problem identification, user needs analysis, system design, and initial evaluation of the resulting prototype. The designed information system prototype includes features for online IPR registration, applicant and document data management, and application status monitoring. The results of the activity indicate that the IPR registration information system prototype is able to provide an overview of digital solutions that are in accordance with partner needs and have the potential to improve service efficiency and ease of data management. This activity is expected to become the basis for the development of a more comprehensive and sustainable IPR information system within the South Sumatra Provincial Industry Office.

This study aims to analyze the effect of information technology (IT)-based learning media on improving students' academic performance. With the advancement of digital technology, the use of IT-based media such as interactive presentations, educational videos, Learning Management Systems (LMS), and online quiz applications has become part of modern teaching strategies. This study uses a quantitative approach with a quasi-experimental method. The subjects of the study were secondary school students divided into experimental and control groups. The instruments used include learning achievement tests to measure academic performance and observation sheets to assess the implementation of IT media usage. Data were analyzed using t-tests and simple regression analysis. The results show a significant difference in academic performance between students who used IT-based learning media and those who used conventional methods. The experimental group showed a higher average score compared to the control group. These findings indicate that the use of IT-based learning media, when planned and implemented systematically, can improve students' motivation, engagement, and understanding of learning materials. Therefore, the integration of information technology into the learning process is recommended as an innovative strategy to enhance the quality of education.

This study aims to provide a comprehensive analysis of Predictive Policing and its implications for law enforcement transformation in Indonesia, based on an extensive review of its global applications, benefits, and challenges. The study uses qualitative literature and international case study review methods to assess the impact and complexity of implementing digital technologies such as artificial intelligence (AI), machine learning, and big data analytics within a Predictive Policing framework. The results of this review highlight that while Predictive Policing offers significant potential for proactive crime prevention and increased operational efficiency, its implementation is consistently fraught with critical legal, ethical, and technical challenges, including regulatory gaps, risks of algorithmic bias, and data privacy concerns, which are particularly relevant to Indonesia. The findings underscore that public trust and police legitimacy in the context of adopting such technologies are strongly influenced by transparency, strong accountability mechanisms, and community involvement in shaping their use. This study contributes to the growing discourse on digital policing in developing countries and culminates in practical policy recommendations designed to guide the Indonesian police towards the development and implementation of Predictive Policing models that are effective, efficient, and fundamentally respectful of legal and human rights principles.

This research investigates thermal material properties and performance characteristics for high-speed vessel components subjected to extreme thermal stress during sustained high-speed operations. High-speed vessels including patrol boats, fast ferries, and naval craft experience elevated thermal loads from high-power density propulsion systems, aerodynamic heating, and sustained operational intensities creating demanding conditions for structural and mechanical components. Through qualitative analysis involving naval architects, materials engineers, high-speed vessel operators, and component manufacturers, this study examines how material thermal properties affect component durability, performance, and safety while identifying optimal material selections for critical applications. Results demonstrate that advanced thermal materials including high-temperature aluminum alloys, titanium alloys, ceramic composites, and thermal barrier coatings can extend component service life by 40-70%, improve thermal management effectiveness by 25-45%, and enhance operational reliability compared to conventional materials. Key implementation challenges include material cost premiums of 150-300%, manufacturing complexity, limited operating experience, qualification testing requirements, and supply chain constraints. Findings reveal that strategic thermal material selection for critical components represents essential enabling technology for high-speed vessel performance, reliability, and operational availability supporting defense, commercial, and emergency response applications requiring sustained high-speed capabilities. This research contributes to marine materials engineering literature by providing evidence-based frameworks for thermal material selection applicable to diverse high-speed vessel applications.

The development of automation and robotics technology has driven innovation in various industrial fields, particularly in automatic sorting systems. Manual sorting processes often lead to inefficiencies and human errors, creating the need for an automatic, fast, and accurate system. This research employs a qualitative method which includes experimentation, testing, and system documentation. The system is designed as a robotic arm for sorting objects based on color, utilizing a TCS3200 color sensor and an ESP32 microcontroller. An ultrasonic sensor detects the presence of objects, while the sorting results are displayed through a real-time web monitoring system. The test results show that the prototype successfully sorts four primary colors (red, green, blue, and yellow) with a high level of accuracy. This research is expected to serve as a reference for the development of automation systems and robotics learning tools in both educational and industrial applications. In addition, this research also contributes to the development of technology that can increase efficiency and accuracy in industrial production processes and provide more environmentally friendly solutions by reducing the need for manual labor.

This study aims to analyze the performance of Android-based attendance applications used at PT PLN ULP West Sumba by integrating two evaluation methods, namely WebQual 4.0 and Importance Performance Analysis (IPA). This attendance application functions to monitor employee attendance digitally so that it is expected to be able to improve the efficiency and accuracy of data recording. Evaluations are conducted to assess the extent to which the application meets the needs of users as well as the expected performance. WebQual 4.0 is used to measure the quality of user experience in terms of ease of access, interactivity, trust, and satisfaction, while IPA is used to compare the level of user interest with application performance based on four main attributes: system quality, information quality, service quality, and usage quality. The research data was collected through a survey with questionnaires compiled according to WebQual 4.0 and IPA indicators, involving application users at PT PLN ULP West Sumba. The results show that the majority of users are satisfied with the ease of use and performance of the application, but there are aspects that need to be improved, especially the speed of the system and a more user-friendly interface design. The science analysis emphasizes that the quality of systems and information is a crucial factor that must be a priority for development. This research provides strategic recommendations for PT PLN ULP West Sumba to improve the performance of the attendance application and support the company's operational needs in a sustainable manner.

Objective: Autoimmune diseases are complex disorders that arise when the immune system loses tolerance to self-antigens, leading to chronic inflammation and tissue damage. To understand disease pathogenesis and to evaluate therapeutic efficacy, animal models are widely used in autoimmune research. This review aims to analyze various types of animal models employed in studies of autoimmune diseases such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), type 1 diabetes mellitus (T1DM), and multiple sclerosis (MS), with a particular focus on reproducibility and clinical applicability. Methods: This study was conducted through the selection and analysis of scientific literature published over the last ten years (2015–2025), using specific keywords including “clinical application,” “autoimmunity,” “animal models,” “humanized mice,” “lupus,” “rheumatoid arthritis,” “reproducibility,” and “translational research.” Literature searches were performed in major databases such as Google Scholar, PubMed, ScienceDirect, and Scopus. Results: Spontaneous models, such as NOD and MRL/lpr mice, exhibit close resemblance to human disease pathogenesis but are influenced by strain variability and environmental factors. Induced models, including collagen-induced arthritis (CIA) and experimental autoimmune encephalomyelitis (EAE), allow greater control over disease onset but do not fully capture the clinical complexity observed in humans. Humanized models demonstrate high translational relevance; however, their use is constrained by high costs and technical limitations. Conclusion: No single animal model is universally ideal for studying autoimmune diseases. Model selection should be based on biological relevance, reproducibility of outcomes, and the potential for clinical translation in autoimmune disease research.

This study investigates biomass-derived surface engineering of AISI 1020 steel for electromedical applications using galam wood charcoal and chicken bone waste as carburizing media. Surface modification is required to improve the mechanical performance of low-carbon steel, particularly in applications that demand high wear resistance and long-term durability. A pack carburizing approach was applied using various ratios of biomass-derived media at a treatment temperature of 800 °C for 2 hours. Chemical composition was analyzed using Optical Emission Spectroscopy (OES), surface hardness was evaluated using Micro Vickers hardness testing, and microstructural characteristics were observed using optical microscopy. The results show a significant increase in surface carbon content with increasing fractions of chicken bone powder, indicating its effectiveness as a carbon donor and diffusion promoter. The surface hardness increased from approximately 150 HV in the untreated condition to a maximum of about 860 HV in the treated specimen. Microstructural observations revealed the formation of a distinct carburized layer with increasing thickness and uniformity, consistent with enhanced carbon diffusion and surface strengthening. These findings demonstrate that biomass-derived surface engineering provides an effective and sustainable approach for improving the surface properties of low-carbon steel. The proposed method offers strong potential for environmentally friendly manufacturing of durable and reliable electromedical components.

The aim of the research is to find out the process of developing knowledge learning media based on Android applications.Learning media is very much needed as a tool used in practical learning.Changes in educational progress require innovation in learning media to keep them up to dat and able to overcome new problems that arise. The research method used is the R & D research and development method with the 4D model. The stages of the 4D development model are 1) Definition; 2) Design; 3) Development; 4) Distribution. This research produces android-based textile knowledge learning media. The software used is iSpring Suite Toolkit Authoring based on Power Point; and Website 2 APK Builder.The iSpring Suit software is used in the design and development of learning media, while the Website 2 APK Builder software is used to change Android application programs. Based on the results of the learning media feasibility questionnaire, it can be seen that the textile knowledge learning media is very feasible to use, reaching 90%.

This research aims to design and build a web-based interactive photo booth application with layout, filter, and frame features using object-oriented programming concepts. The application was developed as an alternative solution to digital photo booths that can be accessed directly through a web browser without the need for additional installation. The system development method used is the Waterfall method which includes the stages of needs analysis, system design, implementation, and testing. The technology used in application development includes HTML, CSS, JavaScript, and the use of Web APIs to access the device's camera and process images in real-time. The results of the study show that this web-based photo booth application is able to run well on modern browsers, providing photo capture features, layout settings, filter application, and interactive frame addition. The implementation of object-oriented programming results in a more modular code structure, is easy to understand, and facilitates the development and maintenance of future systems. Thus, the developed application can become a practical, flexible, and easy-to-use digital photography medium.

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.

The growing pressure on urban drainage systems caused by blockages and environmental pollution highlights the importance of developing sustainable filtration materials. This study explores the use of coffee waste as an environmentally friendly filler integrated into polyester fabric (PE 24S) to create a green filtration medium for drainage applications. The research focuses on developing and characterizing the composite material through macro-photographic morphology analysis, tensile strength testing, and antibacterial evaluation. The findings demonstrate that polyester fabric modified with coffee waste shows a tensile strength of 54.024 ± 5.498 MPa, elongation of 111.128 ± 6.915%, and a Young’s modulus of 0.486 ± 0.543 MPa, indicating improved flexibility and sufficient mechanical durability for drainage system use. Additionally, antibacterial testing reveals that the composite material can inhibit microbial growth due to the presence of natural bioactive compounds found in coffee waste. Overall, the results suggest that coffee waste–enhanced polyester fabric offers a sustainable and mechanically reliable alternative for environmentally friendly drainage infrastructure while supporting circular economy practices.

This study is motivated by the increasing need for accurate modeling and classification of one-dimensional signal data in intelligent systems. The rapid development of deep learning has led to the adoption of more adaptive and complex neural network architectures capable of capturing both temporal dependencies and local patterns in sequential data. This research aims to analyze and compare the performance of several deep learning models, namely Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid Convolutional Neural Network–GRU (CNN–GRU) model for signal data classification. The research method employs a quantitative experimental approach involving data preprocessing, windowing, model training, and performance evaluation. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the hybrid CNN–GRU model outperforms the other models, particularly in capturing local features and long-term temporal dependencies within signal data. These findings suggest that the integration of convolutional layers and recurrent mechanisms enhances feature representation and learning stability. This study is expected to contribute both theoretically and practically to the development of deep learning models for signal processing and time-series-based intelligent applications.

Rainfall is a crucial factor in the stability of the Earth's ecosystem and has a significant impact on agriculture, forestry, energy, and water management. However, increasingly unstable climate change makes rainfall patterns difficult to predict accurately using traditional methods. The city of Medan, the capital of North Sumatra Province, has a tropical rainforest climate with an average annual rainfall of approximately ±2200 mm and an average temperature of 27°C. Significant weather fluctuations in this area can trigger flooding when rainfall increases and cause water shortages when rainfall decreases (BMKG, 2021). Therefore, a prediction approach that can manage non-linear and dynamic data is needed. Artificial Neural Networks (ANN) are one of the reliable machine learning methods for detecting data patterns. By using the backpropagation algorithm, the model can gradually reduce prediction errors, making it widely used in weather forecasting applications. In this regard, this study uses ANN with the backpropagation method to forecast monthly rainfall in Medan City by utilizing data from 2022–2024 as training and testing data.

Guidance on the use of digital technology is needed to produce relevant, effective, and efficient financial reports. The mentoring method is carried out in several stages. The first stage is the initial stage through observation, compiling mentoring materials, and preparing facilities and infrastructure. Second, the implementation stage involves socialization and direct practice in the form of training on the use of website-based applications, starting from recording daily transactions to creating more structured financial reports. This activity is motivated by the importance of transparent, accountable, and efficient financial governance in religious institutions, particularly Rumah Tahfidz. Through this training, managers and administrative staff will be trained to create a digital financial reporting system using a web-based platform that is easy to access and use. The expected results of this activity are the realization of a transparent and efficient web-based financial reporting system, increased digital competence of Rumah Tahfidz managers, and growing awareness of the importance of digitalization in the management of religious institutions. The transaction recording process becomes more structured, efficient, and can be done in real time, thus enabling more accurate and up-to-date financial monitoring.