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This study developed and evaluated an AI-integrated Virtual Reality (VR) system designed to enhance personalized learning in higher education. While VR improves engagement, existing systems often lack adaptivity or experience high latency during AI interactions. To address these limitations, this research introduces a novel integration of a cache-optimized Llama 2 Large Language Model (LLM) that delivers real-time, motivationally grounded feedback. The system was implemented using Unity 3D and validated with 50 undergraduate students. Technical validation showed that the cache layer reduced interaction latency from 17.7 ms to 14.2 ms and maintained zero system crashes throughout the pilot. Learner motivation was assessed using Keller’s ARCS model, yielding mean scores ranging from 4.08 to 4.69 across all dimensions. Independent t-tests (p > 0.05) and negligible effect sizes (Cohen’s d < 0.2) revealed no significant difference between technical (ICT) and non-technical (Physics) students. These findings confirm that the proposed system effectively bridges technological and motivational gaps, providing a robust model for adaptive, immersive education.

This study aims to identify various aspects that pose obstacles or difficulties for students in learning Mechanics. This study uses a qualitative descriptive approach that focuses on describing the types of difficulties students encounter in solving mechanics problems. The research subjects were first-semester students of the Science Education Study Program at KH. Mukhtar Syafaat University (UIMSYA) Blokagung Banyuwangi who were taking Basic Physics courses on Mechanics material. Data collection was carried out through diagnostic tests and interviews with students. The instruments used were diagnostic tests and interview guidelines. The location of students' learning difficultdiies was analyzed based on the errors that appeared in the completion of the diagnostic test. The results showed that the main difficulty students had in learning mechanics was in mastering basic mathematical skills, particularly in differential and integral material. Therefore, lecturers or teachers need to design and develop more appropriate learning strategies so that students' problem-solving skills can improve.

This community service activity aims to introduce the use of the PhET virtual laboratory and to identify students’ responses to the PhET-based virtual laboratory training in empowering students at SMA Negeri 1 Unaaha. The program also seeks to address the issue of limited science practicum facilities in the partner school. The study employed a descriptive quantitative design involving a single group of 23 participants from grades X to XII in the science stream. Participants took part in several sessions, including orientation, demonstration, and exploration of the Build an Atom and Circuit Construction Kit: DC simulations. Research data were collected using a Likert-scale questionnaire consisting of 30 items covering six main indicators: conceptual understanding, motivation, activeness, interest, ability to operate PhET, and learning satisfaction. All questionnaire results were converted into percentages for each indicator. The analysis showed an average response rate of 86.67%, with details as follows: interest 92%, activeness 90%, motivation 88%, satisfaction 89%, operational ability 83%, and conceptual understanding 78%. These findings indicate a very positive acceptance and significant reinforcement in the affective, participatory, and technical skill aspects of the students.

The emergence of Generative Artificial Intelligence (Generative AI) such as the GPT-4 and Midjourney models has sparked a fundamental debate about the nature of creativity and imagination. The AI creation process, often referred to as a “black box,” challenges conventional human-centered understanding. This paper proposes a unique hermeneutic framework to approach this phenomenon by borrowing two key concepts from the Sufi metaphysics of Shaykh al-Akbar Muhyiddin Ibn 'Arabi: khayāl (creative imagination or the imaginal realm) and tajallī (self-manifestation or theophany). This study uses a conceptual-comparative analysis method to analyze the working process of Generative AI. The main argument of this journal is that the “latent space” in AI architecture can be analogized with 'ālam al-khayāl (the imaginal realm) as an intermediate reality (barzakh) that contains unlimited potential. Furthermore, the process of generating text or images from a prompt can be understood as a mechanism resembling tajallī, in which these potentials manifest specifically according to the “availability” (isti'dād) determined by user input. Thus, Ibn 'Arabi's framework offers a non-anthropocentric ontology for understanding “artificial imagination” as a process of manifesting forms from a sea of potential, transcending mere simulation or data recombination.

The Info BMKG application is a digital platform developed by the Meteorology, Climatology, and Geophysics Agency (BMKG) to provide information on weather, earthquakes, air quality, and early disaster warnings. This application is expected to enhance public preparedness in facing potential natural disasters. However, in its implementation, the Info BMKG application still encounters several issues, one of which is delays in delivering weather and disaster information. This study aims to analyze the effectiveness of the Info BMKG application in providing weather and disaster information. The research applies Budiani's (2007) theory, using four indicators: Program Target Accuracy, Program Socialization, Program Objectives, and Program Monitoring. This study employs a qualitative descriptive method with data collection through interviews, observations, and documentation. The findings indicate that the Info BMKG application has made a significant contribution to supporting public preparedness for disasters. While the application is relatively effective in providing weather and disaster information in North Jakarta, further development is still needed. Improvements in information accuracy, notification speed, and enhanced public socialization will help improve the quality of the application’s services in the future.

The digital transformation era requires university students to develop digital literacy competencies that not only support academic activities but also serve as essential capital for entering the workforce and pursuing entrepreneurship. One relevant professional platform to enhance career readiness is LinkedIn, which serves as a medium for both personal branding and professional networking. This community service aimed to improve students’ digital literacy and career readiness through mentoring sessions on LinkedIn account optimization. The program involved the students from the English Education (TBI) and Physics Education (TFS) study programs at the Faculty of Tarbiyah and Teacher Training, UIN Palangka Raya, comprising prospective teachers and professionals across various disciplines. The implementation was carried out through tutorials and direct mentoring in two sessions, covering account creation, developing professional headlines and summaries, adding skills and experiences, and uploading simple portfolios. The results indicated that all participants successfully created or improved their LinkedIn profiles, actively engaged in the activities, and demonstrated a greater awareness of the importance of a professional digital identity, which can be seen from the students’ participation, the quality of the profiles produced, and their commitment to continuously developing their LinkedIn accounts. The program highlights LinkedIn’s strategic role in strengthening digital literacy and career readiness among university students in the global era.

Black Soldier Fly (BSF) larvae, or maggots, are a type of insect currently widely cultivated, primarily for animal feed. This is because BSF larvae contain essential nutrients such as fat and protein in high amounts, thus improving the nutritional quality of livestock that consume them. Therefore, the processing and preservation of maggots is crucial to maintain their nutritional content and extend their shelf life. One method used in maggot processing is drying. Drying aims to reduce the water content in the larvae, thereby preventing the growth of microorganisms that cause spoilage. One widely applied technique is drying using a microwave oven. However, before the actual process is carried out, simulations are often required to determine the distribution of heat and humidity. Simulation is one of the most effective ways to predict the drying performance of biological materials. This study used a simulation using the Computational Fluid Dynamics (CFD) method operated by Comsol Multiphysics 6.2 software. The parameters used in the simulation were an initial maggot temperature of 80°C, a drying time of 15 minutes, and a heat source of 1300 W/m³. Based on the simulation results, the final water content of the maggots was below 10%. Furthermore, the final relative humidity of the maggots ranged from 10–35%, while the final temperature of the larvae increased to 93–97°C. These results indicate that microwave drying can effectively reduce moisture content while maintaining the nutritional quality of BSF larvae. These simulation results can be used as a basis for practical maggot drying processes, thus supporting the production of efficient and nutritious animal feed.  

Work and energy are important topics in physics learning. Learning on this topic can be done using various methods and media, which provide different results depending on the approach used. This study aims to identify various learning models and media and their influence on learning about work and energy through a meta-analysis of 17 relevant research journals. The results of the study indicate that there are various learning methods that can be applied, including the 7E Learning Cycle, STEM, Problem Solving, Think-Pair-Share, Inquiry, and others. Of these various methods, the inquiry approach is the most widely used because it is relevant to the student-based curriculum that positions teachers as facilitators in developing students' thinking skills and intellectual discipline.

Integral equations are essential tools in applied mathematics, with wide-ranging applications in fields such as physics, engineering, and finance. However, solving these equations presents significant challenges, particularly when dealing with complex, high-dimensional, or singular problems. Traditional methods, such as manual analytical techniques or direct numerical approaches, often struggle with computational efficiency, especially for large-scale systems, and may not be suitable for handling ill-conditioned problems. This study aims to develop an efficient numerical method for solving integral equations by combining adaptive quadrature techniques with Python-based iterative solvers. The adaptive quadrature method adjusts the step size dynamically based on error estimates, ensuring high accuracy even in the presence of singularities or near-singularities, which are common in many real-world problems. The iterative solver, based on Krylov subspace methods, enhances computational efficiency by reducing memory usage and improving the convergence speed of the solution. By using these techniques together, the proposed method significantly improves the computational time required to solve large-scale and complex systems of integral equations, while maintaining satisfactory accuracy. The results demonstrate that the adaptive quadrature technique, when combined with the Python-based iterative solver, offers a substantial advantage in both speed and precision compared to traditional methods. The proposed method is especially effective in handling complex, high-dimensional systems and ill-conditioned problems, making it a powerful tool for applied mathematics, physics, and engineering applications. In conclusion, this study presents a robust and efficient approach for solving integral equations, with potential for future research in solving non-linear and multi-dimensional integral equations.

The Pulsed Nuclear Magnetic Resonance (PNMR) technique is a widely used spectroscopic approach for observing the relaxation phenomena of atomic nuclei in a magnetic field. In this technique, the spin-lattice (T1) and spin–spin (T2) relaxation times are key parameters, as they reflect the microscopic dynamics and structure of a material system. This article presents the results of a systematic literature review of fourteen primary references that discuss the fundamental concepts of NMR, PNMR techniques, the measurement of T1 and T2, and PNMR applications. Although NMR research has seen extensive development internationally, scientific literature in the Indonesian language that specifically addresses this topic remains scarce. Therefore, this paper is intended to serve as an initial systematic resource to introduce the basic principles and potential applications of PNMR in physics and materials science.

This study aims to systematically examine the effectiveness of the implementation of Physics e-modules based on local wisdom on students’ critical thinking skills. The focus of The study is directed at the integration of local cultural values, especially through the context of making lapek sarikayo, as part of contextual and meaningful physics learning. The method used is Sstematic Literature Review (SLR), which is carried out through the Identification, selection, and analysis of 20 national scientific articles published in the last Five years. The analyzed articles were selected based on relevance to the topic of physics e-Modules, ethnoscience approaches or local wisdom, and measurement of critical thinking Skills. The results of the study show that e-modules that contain elements of local culture are able to increase student involvement,strengthen understanding of physics concepts, and significantly encourage critical thinking skills. A contextual approach through local culture is proven to provide a learning experience that is closer to the reality of learners. These findings have an important impact on the development of innovative teaching. Materials that are relevant to local cultural characteristics, as well as being a strategic reference in designing holistic and transformative 21st-century physics learning.

Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system behavior. Objective: This study aims to develop a predictive performance model for industrial mechanical systems by integrating Digital Twin technology with Physics Informed Machine Learning in order to improve monitoring accuracy and support predictive maintenance strategies. Methods: This research adopts a data driven modeling and simulation approach by developing a digital representation of an industrial mechanical system that is connected to real time sensor data. The prediction model is constructed using a Physics Informed Neural Network (PINN), which integrates operational data with physical principles governing system dynamics. The research process includes the development of a Digital Twin model, integration of sensor data, training of the PINN model, model validation using experimental data, and evaluation of prediction performance using statistical metrics. Results: The results indicate that the integration of Digital Twin technology and PINN significantly improves the prediction accuracy of industrial mechanical system performance compared with conventional simulation methods and purely data driven machine learning models. The proposed model is capable of representing system dynamics more consistently, accurately following sensor data patterns, and providing strong potential for supporting machine condition monitoring and predictive maintenance strategies in modern industrial environments.

Generally, students stated that the concepts in physics, especially on the material of distance, speed, and acceleration, are difficult to understand. This causes low student motivation and learning outcomes. In order to increase student motivation, understanding of concepts, and learning outcomes, it is necessary to use visual media so that learning is active and enjoyable. The use of visual media, one of which is education game-based learning media, is very helpful for students in understanding physics concepts, especially on the material of distance, speed, and acceleration. The purpose of this study was to determine the study of the use of visual media based on education games that can improve students' understanding of the material of distance, speed, and acceleration. This research method uses the library research method. The form of research uses literature studies with qualitative data acquisition. To obtain data using two databases, namely Google Scholar and Sinta, with document analysis first carried out to identify relevant literature studies. Based on the results of the preliminary study conducted, it was obtained that the use of visual media based on education games is good and flexible. With the use of visual media based on education games, students find it easier to understand the material of distance, speed, and acceleration. In addition, it makes it easier for teachers to carry out learning activities. Learning is more interesting, students are more motivated so that students' understanding, abilities, and learning outcomes increase.

Students' understanding of the concept of motion of objects on inclined planes still shows obstacles, especially when applied in real practice. This study aims to see the extent to which students understand the concept through simple experiments and questionnaires. The main focus of the study was on the results of the practicum, where the block slid faster on a smooth plane (an average of 1.25 seconds) than on a rough plane (an average of 1.61 seconds). This time difference reflects the effect of friction on the motion of objects, but some students were unable to explain the difference correctly. This indicates that there are still misconceptions in understanding the concepts of force and motion. Meanwhile, the results of the questionnaire only reinforced that most students felt the need for direct practice to understand this material. Thus, a practice-based learning approach is very important to strengthen students' conceptual understanding in Basic Physics courses.  

Numerical literacy is a fundamental skill that plays a crucial role in the learning process, particularly in the field of science, especially physics. However, the reality is that many freshmen struggle with numerical literacy. This significantly impacts their foundational mathematical skills, which can hinder the academic learning process. This community service activity aims to enhance the numerical literacy of freshmen in the Physics Department at the University of Lampung by utilizing interactive visual modules. The activity was conducted over two sessions in January 2025, involving 28 new students. The students were divided into heterogeneous groups to support collaborative learning. Pre-test and post-test assessments were conducted to measure the effectiveness of the modules. The results showed an average score improvement from 36.25% to 56.6%. Addionally, the satisfaction survey results showed positive responses regarding the module’s appearance, clarity and benefits. Based on this activity, it was concluded that collaborative learning supported by interactive visual media can enhance students numerical literacy skills. 

This study was motivated by the low learning outcomes of students in Physics subjects in class XII MIPA 7 at SMAN 1 Cikalongwetan, where most students had not reached the minimum mastery criteria. The purpose of this research was to improve student learning outcomes through the implementation of the Problem-Based Learning (PBL) model. The study employed a Classroom Action Research (CAR) method conducted in two cycles, each consisting of two meetings. Each cycle included planning, action, observation, and reflection stages. Data were collected through observations and written tests. The results showed an improvement in student learning outcomes after the implementation of the PBL model, as indicated by the increased number of students achieving scores above the minimum standard and a higher percentage of overall mastery. These findings suggest that the Problem-Based Learning model can be an effective solution to enhance student achievement in Physics. The implication of this study emphasizes the importance of the teacher’s role in designing student-centered learning and facilitating critical thinking and problem-solving skills.

Nonlinear dynamical systems represent a fundamental area of study in applied mathematics due to their relevance across various disciplines, including physics, biology, and engineering. Their inherent complexity, characterized by phenomena such as bifurcation, chaos, and sensitivity to parameter variations, often limits the effectiveness of traditional manual analysis, particularly when addressing high-dimensional or computationally intensive models. This study aims to address these challenges by applying computational modeling and numerical simulation techniques to analyze the stability of nonlinear dynamical systems. The research employs analytical methods, including equilibrium point identification and linearization, which are then validated and extended through the fourth-order Runge-Kutta numerical method. Simulations were conducted to visualize equilibrium points, phase portraits, and parameter-driven bifurcation phenomena. The findings demonstrate a strong correspondence between analytical and numerical approaches, with minimal error margins (≤1%) observed in equilibrium point estimation, thus confirming the reliability of computational methods. Moreover, the bifurcation analysis revealed critical transitions such as pitchfork and Hopf bifurcations, which indicate sudden shifts from stability to instability behaviors that are difficult to capture through manual calculations alone. The integration of computational approaches provides clear advantages, offering systematic exploration of parameter spaces and detailed visualizations of system dynamics, thereby expanding the scope of stability analysis. In conclusion, this study emphasizes that computational modeling is not only an effective complement to analytical methods but also a necessary strategy for advancing the understanding of nonlinear dynamical systems in applied mathematics.  

Nonlinear eigenvalue problems (NEPs) pose significant challenges in mathematical physics and other computational applications due to their nonlinear nature, which makes analytical solutions difficult to obtain. NEPs are encountered in various scientific and engineering fields, including signal processing, electronic structure calculations, and structural optimization. This study aims to explore the application of adaptive algorithms in solving nonlinear eigenvalue problems, with a primary focus on improving accuracy and computational efficiency. The proposed method combines an iterative solver with adaptive step-size adjustment, where the step size is dynamically adjusted during the iteration based on error estimates calculated at each step. This approach enables faster convergence and significant reductions in computational time without compromising accuracy. In experiments conducted on large-scale problems, the adaptive algorithm reduced computational time by 40% faster compared to fixed-step iterative methods. The comparison between the adaptive algorithm and traditional methods showed that the adaptive algorithm is not only more efficient but also more robust when dealing with high-complexity problems. Additionally, the adaptive algorithm provides more accurate error estimates, allowing better error control throughout the iteration process. Overall, this study concludes that adaptive algorithms offer a more effective and efficient solution for complex nonlinear eigenvalue problems and can be adapted to various types of problems in scientific and engineering applications. Further research could focus on optimizing the implementation of this algorithm for larger and more complex scales.

This study aims to comprehensively examine the influence of the Problem-Based Learning (PBL) model integrated with the Internet of Things (IoT) on enhancing students’ problem-solving abilities in physics education. The integration of IoT into PBL is seen as a progressive approach to address the growing demand for innovative instructional strategies that promote higher-order thinking skills. A quantitative approach was adopted, utilizing a quasi-experimental design with a pretest-posttest nonequivalent control group format to assess the effectiveness of the intervention. The participants were 25 undergraduate physics students from the University of West Sulawesi, selected through saturated sampling due to the limited population size. To evaluate students’ problem-solving skills, data were collected using structured written tests designed around five key indicators: understanding the problem, describing the problem, planning the solution, executing the solution, and evaluating the results. Prior to hypothesis testing, normality of the data was assessed using the Kolmogorov-Smirnov test, followed by paired sample t-tests with IBM SPSS Statistics 23 to determine the significance of differences in pretest and posttest scores. The findings revealed a statistically significant improvement in students’ problem-solving skills following the implementation of the IoT-based PBL model, with results showing significance at the 5% level and gain scores classified as effective. These outcomes demonstrate the potential of the PBL-IoT integration to foster critical thinking and improve educational quality. Therefore, the implementation of this instructional model is recommended for physics educators seeking to enhance student engagement, problem-solving proficiency, and learning outcomes through the integration of emerging technologies.    

This study aims to determine the effect of the Discovery Learning model assisted by E-LKPD on students' physics learning outcomes. The research was conducted at SMA Negeri 1 Percut Sei Tuan. This type of research is a quasi-experimental design with a pretest-posttest control group design. The sampling technique used in this study was cluster random sampling, consisting of an experimental class and a control class, each with 36 students. The solution to address the research problem was to apply the Discovery Learning model assisted by E-LKPD in the experimental class and conventional learning in the control class. The subjects of this study were students in classes X-3 and X-4. The instrument used was a learning test in the form of 15 multiple-choice questions. Before the different treatments were applied, a pretest was conducted, resulting in an average pretest score of 48.1 for the experimental class and 45.6 for the control class. Normality and homogeneity tests on the pretest data indicated that both classes' data were normally distributed and homogeneous. The two-tailed t-test resulted in a value of tcount < ttabel = = 0.97 < 1.994, which means H0 was accepted, indicating that the initial critical thinking ability of the experimental and control classes was the same. After the different treatments, a posttest was conducted for both classes, which resulted in an average posttest score of 76.5 for the experimental class and 63.5 for the control class. Normality and homogeneity tests on the posttest data showed that both classes' data were normally distributed and homogeneous. The one-tailed t-test resulted in tcount < ttabel = = 4.860 > 1.666, so the alternative hypothesis (Ha) was accepted. It can be concluded that the use of Discovery Learning assisted by E-LKPD has a significant effect on students' learning outcomes on the topic of work and energy in class X at SMA Negeri 1 Percut Sei Tuan.