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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.

Sharia-compliant investment in Indonesia has experienced rapid growth, in line with increasing public interest in instruments compliant with Islamic principles. However, market fluctuations remain a major challenge in maintaining the performance of sharia investments, particularly sharia mutual funds. This article analyzes the dynamics of sharia investment in Indonesia in the face of market volatility, focusing on the performance of sharia mutual funds. The research method used is a quantitative approach, with secondary data analysis from various scientific studies and recent statistical data. The results indicate that macroeconomic fluctuations and market conditions significantly influence the performance of sharia mutual funds. Nevertheless, sharia mutual funds continue to demonstrate resilience and certain advantages compared to conventional mutual funds, particularly in the face of market uncertainty. These findings have important implications for sharia investors, investment managers, and policymakers in designing more optimal investment strategies and strengthening the position of sharia mutual funds in an increasingly dynamic market.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

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.

Semantic interoperability remains a major challenge in large scale distributed information systems due to heterogeneous data schemas, diverse contextual interpretations, and the dynamic nature of distributed environments. Traditional metadata-based interoperability approaches are often insufficient to address these challenges, as they lack semantic expressiveness and adaptability. This study proposes a context aware knowledge graph framework to enhance semantic interoperability across heterogeneous distributed systems. The research adopts a design-oriented methodology involving requirement analysis, knowledge graph construction, ontology modeling and alignment, context aware semantic representation, and semantic reasoning. A prototype implementation is developed to evaluate the effectiveness of the proposed framework through interoperability scenarios and cross-system semantic queries. The results demonstrate that the proposed approach significantly improves semantic alignment accuracy, query precision, and recall compared to conventional metadata-based solutions. The explicit integration of contextual information and ontology-based reasoning enables adaptive semantic interpretation and reduces ambiguity across systems. Overall, the findings confirm that combining knowledge graphs with ontology modeling and context aware mechanisms provides a robust and scalable solution for improving semantic interoperability in complex distributed information systems.

In high-performance computing (HPC) environments, the need to balance memory efficiency and query performance is crucial for ensuring optimal system performance. Traditional data structures, such as B-trees and hash tables, often prioritize either memory usage or query speed, leading to suboptimal performance in memory-constrained systems. This paper proposes a hybrid data structure that combines the strengths of multiple traditional data structures to optimize both memory usage and query processing speed. The proposed hybrid structure integrates cache-conscious algorithms, dynamic memory allocation, and compression techniques for intermediate query results. The approach is evaluated through extensive benchmarking tests comparing it to standard data structures like B-trees and hash tables under various workloads. Results show that the hybrid data structure reduces memory overhead by up to 30% while maintaining query processing speeds up to 1.5 times faster than conventional methods. Furthermore, the hybrid structure demonstrates robust performance across different types of queries, including both point and range queries, ensuring versatility and efficiency. The findings indicate that this hybrid approach provides a promising solution for HPC systems, where both memory efficiency and query speed are essential. Future research can explore extending the hybrid structure to distributed systems and emerging technologies, further improving its scalability and adaptability to new computational paradigms.

The rapid proliferation of Internet of Things (IoT) devices across diverse industries has significantly increased the vulnerability of IoT edge networks to sophisticated cyber threats. Traditional intrusion detection systems (IDS), such as signature-based and anomaly-based approaches, are often insufficient in addressing the dynamic and evolving nature of these threats. This study proposes a hybrid intrusion detection system (IDS) framework that combines supervised machine learning (ML) techniques with deep reinforcement learning (DRL) to enhance detection performance in real-time, resource-constrained IoT environments. The proposed framework utilizes supervised learning for initial traffic classification and DRL for adaptive decision-making, enabling the system to continuously learn and optimize its detection policies based on new attack patterns. The hybrid approach significantly improves detection accuracy and reduces false positives when compared to conventional signature-based and single-model ML systems. In addition to improved detection capabilities, the framework's computational efficiency allows it to operate effectively within the constraints of IoT devices, ensuring that it is suitable for large-scale deployments. Benchmark evaluations using publicly available datasets, such as NSL-KDD, IoT-23, and BoT-IoT, show that the hybrid IDS framework outperforms traditional methods, providing a more robust and adaptive solution to cybersecurity challenges in IoT edge networks. The findings of this study suggest that combining machine learning with deep reinforcement learning offers a promising approach to secure IoT environments and address the limitations of existing IDS techniques. Future work will explore enhancing real-time adaptability, scalability, and the detection of zero-day attacks in evolving IoT ecosystems.

The banking sector plays a strategic role in supporting financial system stability and capital market development. Market performance, reflected through stock returns, represents investor confidence in a firm’s prospects and sustainability. In recent years, investors have increasingly considered non-financial factors such as intellectual capital and corporate social responsibility in evaluating firm value. However, empirical findings regarding the effect of these factors on market performance remain inconsistent, particularly in the Indonesian banking sector. This study aims to examine the effect of intellectual capital and corporate social responsibility on market performance of conventional commercial banks listed on the Indonesia Stock Exchange during the 2021–2024 period. This research employs a quantitative approach using secondary data obtained from annual reports and sustainability reports. Intellectual capital is measured using the Value Added Intellectual Coefficient method, while corporate social responsibility is measured using a disclosure index based on the Global Reporting Initiative. Market performance is proxied by stock returns. Data analysis is conducted using multiple linear regression with the Ordinary Least Squares approach. The results indicate that intellectual capital and corporate social responsibility have a positive and significant effect on market performance. These findings suggest that effective management of intangible assets and social responsibility disclosure can enhance investor perception and firm value. The results provide important implications for bank management in formulating value-enhancing strategies and for investors in making investment decisions.  

Learning the Qur'an in Islamic Elementary Schools requires an approach that emphasizes not only reading skills but also understanding the meaning and internalizing the Qur'anic values ​​contextually. However, learning practices that are still dominated by conventional methods have the potential to reduce student engagement and interest in learning, especially in narrative materials such as animal stories in the Qur'an. This study aims to examine the effectiveness of the Qur'anic Magic Cards media as a learning medium for animals in the Qur'an in increasing the understanding and interest in learning of students in Islamic Elementary Schools. This study uses a qualitative approach with data collection techniques through observation, interviews, and documentation. The results show that the use of the Qur'anic Magic Cards media can help students understand the meaning of verses more concretely, increase active involvement during learning, and foster interest in learning the Qur'anic material. This media also contributes to creating a fun, contextual, and meaningful learning atmosphere, so that the messages of the Qur'an are not only understood textually but also internalized in the learning process. Thus, the Magic Quran Cards can be used as an innovative and relevant alternative Quranic learning medium for implementation in Islamic Elementary Schools (Madrasah Ibtidaiyah).

This study aims to examine the effect of contextual learning on students’ learning motivation in Indonesian language learning for second-grade students of Madrasah Ibtidaiyah (MI) Salafiyah 1 Kauman. Contextual learning is an instructional approach that connects learning materials with students’ real-life experiences, which is expected to increase their engagement and learning motivation. This study employed a quantitative approach with an experimental research design. The subjects of the study were second-grade MI students divided into an experimental class and a control class. Data were collected through learning motivation questionnaires and classroom observations during the learning process. The collected data were analyzed using statistical techniques to determine differences in learning motivation between the two groups. The results indicate that contextual learning has a positive and significant effect on students’ learning motivation in Indonesian language learning. Students who were taught using contextual learning showed higher learning motivation than those who were taught using conventional learning methods. Therefore, contextual learning can be considered an alternative instructional strategy to enhance students’ learning motivation at the elementary or Madrasah Ibtidaiyah level.

The Muhammadiyah Orphanage in Pamekasan faces major challenges in developing life skills and digital education for its children due to limited facilities, teaching staff, and conventional learning methods. To address these issues, an edutainment-based approach and digital pedagogy intervention were implemented to enhance learning quality, motivation, and preparedness for future social and technological challenges. The activities included workshops and training on Digital Pedagogy and Edutainment learning materials, as well as simulations and role-plays using a Game-Based Learning approach, followed by evaluations and participant plan presentations. The program significantly improved the wards’ digital literacy, particularly in personal security (online safety), digital ethics (cyber ethics), gadget usage, and information management, with the average score rising from 2.84 to 4.10 on a 5-point scale, surpassing the target of 75% of participants in the “good” category. Beyond cognitive aspects, the program also boosted motivation, engagement, communication, problem-solving, and independence. Caregiver training was also provided to ensure program sustainability. It is recommended that the orphanage integrate the Game-Based Learning Digital Safety module into its non-formal curriculum, enhance caregiver capacity through advanced training, and improve IT infrastructure.

Rapid technological change in the digital era has reshaped the nature of organizational innovation, requiring firms not only to innovate but also to align their innovation activities with technological developments and market dynamics, a capability referred to as Techno-Resonance Innovation Capability (TRIC). Although Knowledge Management (KM) has been widely recognized as a driver of innovation, studies that explicitly link KM to the development of techno-resonant innovation remain limited. This study aims to address this gap by systematically reviewing the literature to examine how KM contributes to innovation capability and how this relationship evolves toward TRIC through absorptive capacity. Using the systematic literature review methodology proposed by Tranfield et al., this study follows three stages planning, conducting, and reporting the review to identify, evaluate, and synthesize relevant studies on KM, absorptive capacity, innovation capability, and techno-resonance. The findings indicate that innovation capability emerges from interconnected KM processes, including knowledge acquisition, sharing, storage, and application, which form the organizational infrastructure for innovation. Absorptive capacity is identified as a key bridging mechanism that enables organizations to transform managed knowledge into innovative outcomes by enhancing their ability to acquire, assimilate, transform, and exploit technological knowledge. This study concludes that integrating KM and TRIC through absorptive capacity extends conventional innovation capability models and provides a stronger theoretical explanation of innovation in technology-driven and digitally dynamic environments.

This study aims to conceptually examine the role of visual media in Arabic vocabulary learning and its relevance in the context of education in the digital era. Vocabulary mastery is a fundamental component in learning Arabic; however, in practice, vocabulary learning often encounters various challenges, such as the abstract nature of vocabulary, low student motivation, and the dominance of conventional teaching methods. This study employed a qualitative approach using library research. Data were obtained through a review of relevant scholarly sources, including books, journal articles, and previous research related to visual media and Arabic language learning. Data were collected through documentation techniques and analyzed using content analysis to examine the concepts, roles, and effectiveness of visual media in Arabic vocabulary instruction. The findings indicate that visual media, both conventional and digital, play a significant role in improving vocabulary comprehension, strengthening memory retention, and increasing students’ motivation and engagement in the learning process. Visual media also help transform abstract vocabulary into more concrete and contextual representations, making learning more meaningful and effective. Nevertheless, the implementation of visual media still faces several challenges, including limited facilities, teachers’ digital competence, and the suboptimal use of technology in learning activities. Therefore, it is necessary to develop innovative instructional strategies and enhance teachers’ competencies to ensure that visual media can be utilized effectively and sustainably in Arabic language learning.

Digital transformation in the land sector is part of the state’s efforts to enhance efficiency, transparency, and legal certainty within the land registration system. One of the strategic policies implemented is the issuance of electronic land certificates as a substitute for conventional certificates. However, the implementation of electronic land certificates raises several legal issues, particularly concerning their evidentiary value and the guarantee of legal certainty for holders of land rights. This article aims to analyze the legal regulation of electronic land certificates within Indonesia’s land registration system, examine their evidentiary strength in civil disputes, and identify the legal and technical challenges in their implementation. The research employs a normative juridical method using statutory and conceptual approaches. The findings indicate that electronic land certificates have a valid legal basis and possess evidentiary strength equivalent to that of conventional certificates, provided that the principles of validity and security of electronic systems are fulfilled. Nevertheless, their implementation still faces challenges related to technological infrastructure readiness, public legal literacy, and potential vulnerability to cybercrime. Therefore, strengthening technical regulations, enhancing system security, and conducting continuous public dissemination are necessary to ensure legal certainty for holders of electronic land certificates.

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

Video steganography faces fundamental challenges in balancing embedding capacity, imperceptibility, and robustness, where conventional Least Significant Bit (LSB) methods often produce visual artifacts such as flickering. To address this, this research proposes an advanced method named Adaptive Multi-layer LSB, which dynamically adjusts the number of embedded bits in each pixel based on a multi-factor analysis of the video's spatial and temporal characteristics. This adaptation mechanism is evaluated through three primary criteria: brightness level, local texture complexity, and inter-frame motion stability. Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Frame Difference Stability Index (FDSI) metrics demonstrates that the proposed method achieves high visual quality, with an average PSNR of 42.15 dB and SSIM of 0.985. These results significantly outperform the non-adaptive approach, which only recorded a PSNR of 38.5 dB. More importantly, the FDSI value of this method (1.25) is much lower compared to the non-adaptive approach (3.40), demonstrating its superiority in maintaining temporal stability. Thus, this approach provides a significant contribution to enhancing security and quality in video steganography practices. Abstract: Video steganography faces fundamental challenges in balancing embedding capacity, imperceptibility, and robustness, where conventional Least Significant Bit (LSB) methods often produce visual artifacts such as flickering. To address this, this research proposes an advanced method named Adaptive Multi-layer LSB, which dynamically adjusts the number of embedded bits in each pixel based on a multi-factor analysis of the video's spatial and temporal characteristics. This adaptation mechanism is evaluated through three primary criteria: brightness level, local texture complexity, and inter-frame motion stability. Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Frame Difference Stability Index (FDSI) metrics demonstrates that the proposed method achieves high visual quality, with an average PSNR of 42.15 dB and SSIM of 0.985. These results significantly outperform the non-adaptive approach, which only recorded a PSNR of 38.5 dB. More importantly, the FDSI value of this method (1.25) is much lower compared to the non-adaptive approach (3.40), demonstrating its superiority in maintaining temporal stability. Thus, this approach provides a significant contribution to enhancing security and quality in video steganography practices.

Steganography is a method to hide confidential messages in digital media so that they are not detected by unauthorized parties. Unlike cryptography which protects the content of messages through encryption, steganography hides the message itself. One popular technique is the Least Significant Bit (LSB), which replaces the least important bit on the pixel with a secret message bit. However, conventional LSB methods such as 1-bit or 3-bit have limitations due to the compromise between insertion capacity and visual quality of the media. This study proposes an LSB-based video steganography method with an adaptive multi-bit embedding approach. This technique determines the number and position of bits that are dynamically inserted based on the local brightness and texture levels of each video frame, with Laplacian operators used to analyze both high and low textured areas. The process includes frame and audio extraction, frame-by-frame embedding, inserted video reconstruction, and decoding using video cover references. The evaluation was carried out quantitatively using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, as well as qualitatively through visual comparison. The results showed that the adaptive multi-bit method was able to maintain visual quality with a PSNR of 45.23 dB and SSIM of 0.9424, and increased the insertion capacity by up to 2–3 times compared to the 1-bit adaptive method. Thus, this approach effectively balances imperceptibility and insertion capacity on dynamic video steganography systems.  

Despite its cultural potential, Wetabar Village has not been supported by effective digital promotional media. Information access remains limited, and the lack of interactive promotional platforms has contributed to the low interest of tourists. Current promotional efforts rely heavily on conventional methods, resulting in decreased visitor numbers. The implementation of digital technologies such as Augmented Reality (AR) and Web-based 360° Virtual Reality Tours is considered an effective approach to increasing tourist engagement by offering immersive exploration experiences and interactive cultural content. This research aims to design and implement a web-based tourism promotion media that integrates local cultural content with AR and 360° virtual tour technologies. The development process follows the MDLC research method, utilizing the Lumi H5P platform and PHP for system implementation. The resulting system enables users to digitally explore Wetabar Village through panoramic views and interactive multimedia features. The findings show that the developed application runs smoothly on both Android devices and laptops and is capable of providing hotspot access to operate the virtual reality features. This innovation is expected to enhance tourist interest while supporting cultural preservation and promoting digital transformation within the tourism sector of West Sumba.

Steganography is a technique for hiding secret data within digital media such as images, audio, and video without causing noticeable visual changes. In video media, this technique offers advantages because each frame can be utilized dynamically, resulting in a larger data embedding capacity. However, conventional methods such as fixed-number Least Significant Bit (LSB) embedding still face limitations in balancing visual quality, embedding capacity, and resistance to compression or noise. To address these challenges, this study proposes an Adaptive Video Steganography Method based on Multi-Bit LSB that employs brightness, texture, and motion analysis for each frame to determine the number of embedding bits adaptively. The system adjusts the embedding capacity according to the local characteristics of the video: areas with high texture or rapid motion are assigned more bits, while static or low-texture areas use fewer bits to preserve visual quality. After the embedding process, the video quality is evaluated using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Measurement) to assess its similarity to the original video. Experimental results show a PSNR value of 45.86 dB and an SSIM value of 0.9441, Thus, the proposed adaptive method proves to be efficient, robust against disturbances, and capable of maintaining data security without compromising visual quality, making it highly suitable for implementation in multimedia-based information security systems.

Abstract: Character education has become a critical challenge in secondary education, particularly in learning contexts that emphasize cognitive achievement over affective and behavioral development. Although Project-Based Learning (PjBL) is widely recognized for its potential to foster collaboration and responsibility, character outcomes are often treated implicitly and rarely evaluated systematically. This study aims to develop and evaluate a character-oriented Project-Based Learning framework using a Design-Based Research (DBR) methodology in Islamic secondary education. The research was conducted through iterative design cycles involving problem analysis, instructional design, implementation, evaluation, and refinement. Explicit character indicators were embedded into each phase of the PjBL process and assessed using quantitative and qualitative instruments. Statistical analysis of pretest and posttest data revealed significant improvements in students’ character-related outcomes following the implementation of the proposed framework. Qualitative findings further indicated enhanced student engagement, collaboration, and social responsibility during project activities. The results demonstrate that integrating explicit character indicators into PjBL through a DBR approach enhances the effectiveness of character education beyond conventional instructional practices. This study contributes to the literature by providing a structured and empirically validated framework that bridges Project-Based Learning, Design-Based Research, and character education. The proposed framework offers practical implications for educators seeking to integrate affective learning objectives into active learning environments.