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This research traces the role of classical Islamic intellectual traditions in shaping the direction and character of the contemporary Islamic education system at the Al Asror Islamic Boarding School, Gunung Pati, Semarang. This study highlights the preservation and adaptation of the main elements of turats such as the study of the yellow book, sanad-based teacher-student relations, oral learning patterns, and the development of manners and learning ethics which are maintained as the methodological foundation of Islamic boarding schools. In the context of educational modernization, this study observes how the tradition undergoes adjustment without losing its epistemological roots. Using a qualitative approach, this study explores classroom learning practices, curriculum structure, and views of pesantren stakeholders through observation, in-depth interviews, and document analysis. Field findings show that the Al Asror Islamic Boarding School is able to combine classical scientific heritage with modern educational needs, including the implementation of more systematic learning strategies, strengthening curriculum management, and the use of digital technology as a means of supporting learning. The synergy between traditional values and the demands of this era confirms that turats are not only relevant, but also remain a source of values, ethos, and a framework of thinking that enriches Islamic boarding school educational innovations. Furthermore, the results of the study emphasize that the ability of pesantren to harmonize classical intellectual heritage with social, technological, and pedagogical developments is a strategic factor for the resilience, continuity, and competitiveness of Islamic education in the modern era.

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.

Usaha Mikro Kecil Menengah (UMKM) play a strategic role in Indonesia’s economy, serving as key drivers of inclusive economic growth and major contributors to employment creation. Despite their importance, many UMKM operators continue to face significant challenges in financial management, particularly due to the absence of structured and systematic financial record-keeping practices. This condition is also evident among UMKM in Kuning Gading Village, Unit XVIII Kuamang Kuning, where most businesses are still managed in a traditional manner with inadequate bookkeeping systems. This community service program aims to enhance financial literacy and strengthen the capacity of UMKM actors to implement simple bookkeeping as a foundation for effective business financial management. The methods employed include needs assessment through field observation, educational and practical training sessions, hands-on mentoring, and evaluation using pre-test and post-test instruments. The results indicate a significant improvement in participants’ understanding and skills related to transaction recording, separation of business and personal finances, and preparation of simple cash flow statements. Evaluation outcomes show an increase in competency scores from an initial range of 20%–40% to 75%–85% after the training. Furthermore, the program fostered a positive shift in participants’ mindset toward more professional and accountable business management practices. Therefore, the implementation of simple bookkeeping is proven to be an effective initial strategy for strengthening MSME financial governance and supporting sustainable business development at the local level.

The readiness of graduates to face the world of work is a crucial challenge amidst the increasingly competitive job market. This community service activity aims to improve student job readiness through job readiness training and strengthening soft skills. The methods used include needs analysis, CV preparation training, job interview simulations, strengthening soft skills, and pre- and post-training evaluations. The results of the activity show a significant increase in students' understanding of the world of work, self-confidence, and readiness to face the recruitment process. This activity is expected to become a model of sustainable service in increasing the competitiveness of graduates, by providing relevant and applicable provisions to face the challenges of the ever-evolving world of work. Strengthening soft skills is also expected to be a determining factor in students' success when entering the world of work. The sustainability of this program is expected to have a long-term positive impact on the development of graduates' competencies in various fields of work. In the future, the sustainability of this program is expected to have a long-term positive impact on graduates' competencies, strengthen their position in the job market, and support the development of skills needed in various fields of work that continue to evolve.

Temulawak (Curcuma xanthorrhiza Roxb.) is an indigenous medicinal plant of Indonesia that has been widely used in traditional medicine and shows considerable potential in the health and pharmaceutical fields. This potential is related to its diverse secondary metabolites with bioactive properties. This review article aims to examine the phytochemical profile of temulawak, the analytical methods used to identify bioactive compounds, and their reported biological activities based on literature studies. The method applied was a literature review of scientific articles discussing the chemical composition and biological activities of temulawak. The results indicate that temulawak contains major compounds such as curcuminoids, xanthorrhizol, flavonoids, phenolic compounds, and essential oils, which are commonly identified using chromatographic and spectroscopic techniques. Several studies report that temulawak exhibits antioxidant, anti-inflammatory, antibacterial, hepatoprotective, and potential anticancer activities. These findings indicate that temulawak has significant potential as a source of natural bioactive compounds. In conclusion, temulawak possesses a rich phytochemical profile and promising biological activities, supporting its potential application in the development of natural-based health and pharmaceutical products.

Motorcycles are one of the main modes of transportation widely used by the Indonesian people due to their practicality, efficiency, and ability to support daily mobility. However, many motorcycle users complain about the suboptimal engine power, especially when the vehicle is used on extreme terrains outside urban areas, such as uphill roads, rocky paths, or long distances. This situation has driven the development of various innovations in the automotive field to improve engine performance. One widely applied method is the oversize technique on the piston and piston block, which aims to increase the combustion chamber capacity, thereby maximizing the power output. This study aims to analyze the significant comparison of engine performance after the application of the oversize method on the KVY engine type, as well as to examine the factors influencing the optimization of engine performance based on the philosophy and needs of the vehicle owner. The testing method used a dynamometer to measure the increase in torque and engine power. The test results showed that the daily oversize application resulted in a torque increase of 1.19 Nm and a power increase of 0.85 Hp compared to the standard condition. Meanwhile, the oversize for competition needs showed more significant improvements, with a torque increase of 7.97 Nm and a power increase of 8.12 Hp. These findings demonstrate that the oversize method can significantly enhance engine performance according to its intended use.

This study explores the integration of adaptive streaming models with edge computing to optimize multimedia delivery, particularly in real-time applications such as video conferencing, live streaming, and virtual reality. The proposed model leverages adaptive compression techniques, including scalable video coding (SVC) and hybrid adaptive compression (HAC), which adjust video quality based on real-time network conditions. The use of edge computing further enhances the model by processing and delivering content closer to the user, reducing latency and optimizing bandwidth usage. The research demonstrates that the edge computing-based adaptive streaming model significantly improves latency by up to 30%, reduces bandwidth consumption, and ensures higher visual quality during video playback, even under fluctuating network conditions. This model addresses key challenges in multimedia streaming, such as maintaining video quality in bandwidth-constrained environments and minimizing buffering times. Furthermore, it enhances the overall Quality of Experience (QoE) for users by providing smoother interactions and real-time responsiveness. The study highlights the potential impact of this model on various fields, including remote education, entertainment, and interactive applications, where low-latency content delivery and high-quality streaming are critical. The findings suggest that integrating AI algorithms for even more efficient compression and expanding edge computing infrastructures will further optimize multimedia streaming in the future, ensuring reliable and high-quality user experiences in increasingly connected environments.

The development of information technology encourages organizations to adopt a more efficient, flexible, and secure data management system, especially in the field of financial management that requires high accuracy and reliability. One of the technologies that is widely used is cloud computing, which offers easy access to data and an integrated security system. This article aims to analyze the utilization of cloud technology in improving the security and accessibility of financial management data. The method used in this study is a literature study by examining various scientific sources, books, and online news relevant to the topic of cloud computing and financial data management. The results of the study show that cloud technology is able to improve data security through the implementation of encryption, multi-layered access control, user authentication, and a reliable data backup system. In addition, cloud technology also improves the accessibility of financial data because it allows users to access information in real-time, flexibly, and without location or device restrictions. Thus, the application of cloud technology can be a strategic solution for organizations in improving operational efficiency, data security, and the quality of decision-making in financial management.

This article aims to describe the teaching methods and practices of the Philosophy of Islamic Education course at STAI Darussalam. This research uses a qualitative descriptive approach with a field study method that focuses on the learning process, the competencies to be achieved, teaching methods, assignment formats, evaluation systems, and obstacles encountered during the course. Data were obtained through observation and information gathering related to the implementation of the Philosophy of Islamic Education course. The results show that the teaching of the Philosophy of Islamic Education at STAI Darussalam focuses on understanding the nature of Islamic education, developing students' critical thinking patterns, improving educational literacy, and linking philosophical studies to social and political realities. The lecture method remains the dominant method used by lecturers, with an emphasis on students' ability to define and explain basic philosophical concepts accurately as an indicator of understanding. Learning success is measured by changes in students' thinking, which becomes increasingly critical, reflective, and contextual in responding to educational issues and social justice issues.

This research explores the impact of Cache Aware optimizations on signal processing pipelines in High Throughput computing systems. The growing demand for efficient memory management in modern computing systems, especially for data-intensive applications such as artificial intelligence (AI) and multimedia processing, necessitates the development of optimized memory hierarchies. Traditional memory systems often suffer from memory bottlenecks, significantly reducing the performance of these systems. This study investigates how memory hierarchy optimizations, particularly cache line aware optimization, dependency-aware caching, and adaptive cache replacement algorithms, can mitigate these challenges and improve system performance. Through analytical modeling and experimental benchmarking, this work evaluates various memory hierarchy configurations, including processing-in-memory (PIM) and three-dimensional integrated circuits (3D ICs), comparing them to conventional systems. The results demonstrate that Cache Aware optimizations lead to a reduction in memory access latency by up to 30%, while throughput improved by up to 40%. Additionally, cache hit rates increased by 25%, and energy consumption was reduced by up to 20%, highlighting the effectiveness of optimized memory management. The research contributes to the field by providing valuable insights into the design and implementation of efficient signal processing pipelines. It also identifies key challenges, including the need for dynamic occupancy mechanisms and DAG-aware scheduling algorithms, and suggests potential areas for future research, such as the exploration of collaborative caching approaches and further optimization of cache-adaptive algorithms. This work lays the foundation for more efficient, high-performance computing systems that can handle large datasets and complex tasks in real-time applications.

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

Continuous chemical control of rat pests (Rattus spp.) can sometimes lead to resistance and resurgence problems, and even some cases of inaccurate targeting ultimately killing livestock. The idea of ​​​​repelling rats in rice fields using vibrations of crickets and other natural animals transmitted via radio has been done and the results are quite optimal for rat control, but it has not been done in corn cultivation. The purpose of this study was to determine the effect of cat sound audio on the intensity of rat pest attacks (Rattus spp.) in corn (Zea mays L.) plantations. The research location was in a farmer's land owned by a farmer in Dagan Village, Solokuro District, Lamongan Regency. Determination of sampling points using the diagonal method, each into 5 sub-observation plots. In each sub-plot, markers were placed as sampling points. Audio with cat and bird sounds was played continuously from 05.00 pm - 07.00 am. Observations were conducted at 7:00 a.m. at all sampling points at 30 and 60 days after planting. Observations were conducted for three days using audio and three days without audio. Damage symptoms were observed directly at each sampling point. Corn plants showing symptoms were then recorded and analyzed using the Damage Intensity formula. Observations showed that the use of cat sound audio was able to reduce the level of rat infestation. At 30 days after planting, the damage percentage was reduced by 8.33%. Meanwhile, at 60 days after planting, the damage percentage was reduced by 18.33%.

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.

The development of Islamic political thought cannot be separated from the historical context and social dynamics surrounding it. One significant period that influenced the direction of this thought is the era of Western colonialism in the 19th to early 20th centuries. During this period, much of the Islamic world was under Western colonial rule, which had a profound impact on the social, political, and intellectual conditions of Muslims. Within the Muslim community itself, various internal issues related to religious understanding arose, causing difficulties in facing the dominance and hegemony of the West. Islamic political thought became one of the fields of study in political science, which is not only relevant today but also gained attention during the classical Islamic period. This study is highly significant, both in the development of knowledge and as a framework for political practice. Therefore, it is important to study Islamic political thought, as it provides deep insights into political dynamics and Islam’s contribution to shaping existing political systems, as well as offering perspectives that are useful in facing contemporary global challenges.

Explainable Artificial Intelligence (XAI) has become a critical area of research within artificial intelligence, focusing on improving the transparency and interpretability of machine learning (ML) models, often referred to as "black-box" models. The need for XAI techniques arises from the inherent complexity of ML models, which can make their decision-making processes difficult for users to understand. This study investigates various XAI techniques, including LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), to assess their impact on model interpretability without significantly compromising predictive performance. A comparative experimental design was used, applying these XAI methods to different ML models, including deep neural networks and ensemble methods, within large-scale enterprise data analytics systems. The results indicate that XAI methods significantly enhance model transparency and decision traceability, allowing users to understand the influence of individual features on predictions. While a slight reduction in predictive accuracy was observed, especially with simpler models, the trade-off between interpretability and performance was deemed acceptable, particularly in fields requiring transparency, such as healthcare, finance, and autonomous systems. The use of XAI in enterprise data systems has practical implications for fostering trust and enabling informed decision-making among stakeholders. Furthermore, the study discusses the challenges and limitations of applying XAI techniques, such as complexity, scalability, and model-specific limitations. Future research is suggested to focus on developing more scalable and efficient XAI methods, enhancing their applicability across various model types, and addressing the challenges of real-time applications. This will be crucial in ensuring the widespread adoption of XAI in critical domains, promoting the ethical use of AI while maintaining predictive accuracy.

The increasing adoption of cloud native microservices has brought about significant improvements in scalability, flexibility, and resilience. However, these advancements also introduce substantial security challenges, particularly in distributed environments where traditional perimeter-based security models prove inadequate. This paper proposes a secure architecture for cloud native microservices that integrates Zero trust Network Access (ZTNA) and multi layered encryption techniques to address these security concerns. The architecture operates on the principle of "never trust, always verify," ensuring that access to resources is strictly controlled and continuously monitored. By incorporating multi layered encryption methods such as RSA and AES, the architecture ensures data protection both in transit and at rest, significantly reducing the risk of data breaches and unauthorized access. Through experimental evaluations, the proposed architecture demonstrated its effectiveness in preventing lateral movement, mitigating data leakage, and resisting common attack vectors such as man-in-the-middle (MITM) attacks and privilege escalation. Additionally, the performance of the system remained optimal, with minimal overhead despite the additional security layers. The architecture's scalability and robust security mechanisms make it a viable solution for real-world microservices environments, where both security and performance are crucial. This paper discusses the potential impact of this secure architecture on the broader field of distributed system security and offers recommendations for future work, including the integration of advanced machine learning techniques for real-time threat detection and automated responses, as well as the adaptation of the architecture for emerging technologies like edge computing and 6G networks.

Oral literature is a form of storytelling from one individual to another in a concise manner as a representation of a society's culture. One type that is interesting to study in this field is legends. This study examines the Keong Mas legend using Burhan Nurgiyantoro's structuralism approach theory, which focuses on analyzing the relationship between intrinsic and extrinsic elements in literary works. This study uses a qualitative descriptive method that aims to reveal the narrative structure and social and cultural meanings contained in the legend as a reflection of the moral values and local wisdom of the people of East Java. The information collection method used observation and note-taking. The results of the analysis show that the Keong Mas legend has a cohesive story structure, including theme, characters, plot, setting, and point of view, which simultaneously reflect the values of loyalty, justice, and family conflict in a socio-cultural context. These findings confirm the relevance of oral literature as a medium for the inheritance of living and evolving cultural values, and contribute to the understanding of cultural identity and literary aesthetics in Indonesia, especially in the East Java region.

This Community Service (PkM) activity was implemented in the form of an entrepreneurship seminar. This seminar strategically raised the theme "Changing the Mindset Shift from Follower to Owner", with the crucial aim of facilitating a paradigm shift in participants from mere passive consumers or followers to active and empowered business owners (owners). The implementation of PkM is based on the reality of the high unemployment rate among educated people and the urgency of global economic challenges that demand mental transformation and strengthening of practical skills. The main target group of this activity included students of entrepreneurship study programs from various universities in East Java as well as representatives of Micro, Small, and Medium Enterprises (MSMEs) operating in the Gresik area, ensuring the relevance of the material to academic and practical needs. This seminar featured two credible expert speakers in the field of entrepreneurship. The implementation method focused on interactive delivery of material, followed by a focused discussion session to maximize knowledge absorption. The results achieved showed a significant increase in understanding among participants, not only regarding the importance of instilling an entrepreneurial mindset but also mastery of practical aspects of business, including digital marketing implementation strategies and concrete steps in starting or developing a business unit. Through this intervention, PkM is expected to be able to provide a real contribution to accelerating the creation of new, innovative entrepreneurs and strengthening the sustainable entrepreneurial ecosystem in East Java

Intellectual Property Rights (IPR) are the result of human thought that have economic and social value and play a significant role in encouraging creativity, technological innovation, and sustainable national development. IPR protection not only aims to provide legal certainty for creators and rights holders, but also serves as a strategic instrument in strengthening the creative economy. However, low public awareness of the importance of IPR protection and the gap between legal norms and practices in the field result in frequent violations of intellectual works. This study uses a normative-juridical approach combined with a participatory-educational approach, in accordance with the characteristics of community service activities in the legal field. This approach aims to integrate normative legal analysis with legal education activities for the community, thereby producing both conceptual understanding and practical benefits. The results of the activities show a significant increase in public understanding of the relationship between constitutional principles and IPR protection in Indonesia. Through educational activities and legal dialogues, it was revealed that the integration between constitutional principles and the IPR legal system has not been optimal, not only due to limited regulations, but also weak public legal awareness and the application of substantive justice as mandated by the 1945 Constitution. These findings emphasize the importance of integrating constitutional values ​​in the IPR protection system.

The rapid development of artificial intelligence (AI) in the medical field has become an important part of the learning process and health services. Preparing medical students as future healthcare professionals to understand, use, and implement AI responsibly is a crucial aspect. This level of readiness can vary depending on their knowledge, abilities, perceptions, and ethics in using AI. This study aims to determine the readiness of young medical students in the Surabaya area in using artificial intelligence based on these four domains, and to compare scores between first-year professional students and undergraduate students. This study used a quantitative descriptive design with a cross-sectional approach. The instrument used was the Medical Artificial Intelligence Scale for Medical Students questionnaire, which consists of four domains: knowledge, abilities, perceptions, and ethics. The study sample was first-year and second-year professional students of the Faculty of Medicine, Ciputra University. Data analysis was performed using descriptive statistics including mean values, standard deviations, and frequency distributions for each domain. The results showed that the total readiness scores for DM1 (89.95 ± 11.84) and DM2 (88.38 ± 8.85) showed a positive picture, with minimal mean differences. The knowledge and skills domain showed almost uniform values ​​between the two groups, while the ethics domain had the highest stability with a very small mean difference. These findings indicate that the readiness of professional students at the Faculty of Medicine, Ciputra University, towards the use of AI shows a positive and relatively even picture at all levels.