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Contemporary defense and security challenges now come from both non-military threats (such as cyber attacks, disinformation campaigns, and economic pressure) and military threats (traditional armed force). These often operate in the gray zone, meaning actions are taken below the threshold of armed conflict as defined under international law. This situation signals the rise of modern armed conflict, which is growing more intense and causing a crisis in International Humanitarian Law (IHL) enforcement. This study aims to describe modern armed conflict and its challenges to IHL. The research used a normative juridical approach and analyzed statutes. Legal materials reviewed included various international legal instruments, which were examined qualitatively and normatively. The results show that modern armed conflict challenges International Humanitarian Law with non-linear conflicts (conflicts with unclear frontlines or participants), proxy actors (groups acting on behalf of states), and cyber threats or propaganda. Therefore, IHL should be updated to include more comprehensive regulations

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.

This research focuses on the design and evaluation of a novel parallel graph optimization algorithm incorporating dynamic load balancing (DLB) to address inefficiencies in heterogeneous computing environments. Large-scale graph optimization problems, such as those in social networks, bioinformatics, and transportation systems, often suffer from computational imbalances when using traditional static load balancing approaches, leading to underutilized resources and prolonged execution times. The primary objective of this research is to develop an algorithm that can dynamically adjust workload distribution across processors, enhancing computational efficiency and scalability. The proposed method combines heuristic techniques, including region expansion and multilevel partitioning, with diffusive load balancing strategies to minimize inter-processor communication overhead. Experimental results demonstrate that the proposed algorithm reduces execution time by up to 40% compared to static methods, with optimized resource utilization and more balanced workload distribution. The scalability of the algorithm is also evident, as it adapts effectively to increasing problem sizes and processor counts. These findings suggest that dynamic load balancing is crucial for improving parallel graph optimization in real-world applications. Future work will focus on further enhancing the algorithm’s responsiveness to rapidly changing workloads and expanding its applicability to additional domains.

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 increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.

In the context of both public organizations and Small and Medium Enterprises (SMEs), inefficient business processes remain a significant challenge. Fragmented information systems often hinder the optimization of these processes, leading to slower decision-making, redundant efforts, and increased operational costs. This study aims to analyze and optimize business processes by utilizing integrated information systems (IIS), providing a comparative analysis between the two sectors. The theoretical framework explores key theories such as Business Process Management (BPM) and the integration of information systems for process optimization. Previous studies highlight the differences in how IIS implementation impacts the public and SME sectors, noting challenges such as data silos, legacy systems, and resistance to change. A case study analysis methodology was employed to assess the effectiveness of IIS across both sectors. Business Process Modeling (BPMN) was used to visualize business processes before and after optimization, and process performance was measured through key metrics such as time reduction, error rates, and cost efficiency. The results show that IIS integration improved business process efficiency by an average of 28%, with reductions in redundancy and faster decision cycles observed in both sectors. Public organizations benefited from enhanced service delivery and better resource management, while SMEs gained competitive advantages through streamlined operations and increased responsiveness to market demands. The comparison reveals that integrated systems had a greater operational impact than traditional isolated process reengineering methods. Public organizations faced more regulatory and governance challenges, while SMEs leveraged their flexibility for faster integration. Recommendations for both sectors include focusing on overcoming barriers such as resistance to change and investing in system modernization. Future research should explore the long-term effects of IIS integration and further sector-specific comparisons.

Excessive alcohol consumption, including traditional alcoholic beverages such as Cap Tikus  (alcohol content ±45%) from North Sulawesi, can induce liver damage characterized by steatosis, inflammation, and necrosis. Leilem leaf (Clerodendrum minahassae) extract, rich in phenolic and flavonoid compounds, has potential hepatoprotective effects due to its antioxidant properties. To evaluate the effect of leilem leaf extract on the histopathological features of the liver in Wistar rats (Rattus norvegicus) induced by Cap Tikus alcoholic beverage. This experimental study used a post-test only control group design with 24 male Wistar rats divided into four groups: normal control, negative control (Cap Tikus 2.16 mL/day), treatment I (leilem extract 150 mg/kgBW + Cap Tikus), and treatment II (leilem extract 300 mg/kgBW + Cap Tikus). Treatments were administered orally for 14 days. Liver tissue was processed for histopathological examination using hematoxylin-eosin staining and observed under a light microscope. The negative control group showed significant steatosis and inflammatory cell infiltration. Treatment groups exhibited reduced steatosis and inflammation, along with increased hepatocyte regeneration. The higher dose (300 mg/kgBW) demonstrated greater hepatoprotective effects and more prominent regeneration compared to the lower dose (150 mg/kgBW). No necrosis or fibrosis was observed in any group. Leilem leaf extract exerts a hepatoprotective effect by suppressing inflammatory responses and enhancing hepatocyte regeneration in Wistar rats induced with Cap Tikus. The highest protective efficacy was observed with leilem leaf extract at 300 mg/kgBW.

Inflammation is a physiological response to tissue injury, infection, or harmful stimuli, characterized by redness, swelling, heat, and pain. However, excessive or chronic inflammation may lead to tissue damage and degenerative diseases. Long-term use of non-steroidal anti-inflammatory drugs is associated with gastrointestinal and cardiovascular side effects, highlighting the need for safer natural alternatives. Taro leaves (Colocasia esculenta) are traditionally used to treat swelling and wounds and contain bioactive compounds such as flavonoids, alkaloids, tannins, saponins, and triterpenoids with potential anti-inflammatory effects. This study aimed to evaluate the anti-inflammatory activity of ethanol extract of taro leaves and determine the most effective dose. An experimental study was conducted using male white mice (Mus musculus) divided into five groups: negative control (Na-CMC), positive control (sodium diclofenac), and three treatment groups receiving extract doses of 25, 50, and 75 mg/kg BW. Inflammation was induced by 1% carrageenan injection. The extract significantly reduced inflammation, with the highest inhibition (84.13%) observed at 75 mg/kg BW, comparable to diclofenac.  

This study aimed to analyze differences in body composition among adult women across three regions with distinct sociocultural and environmental characteristics: Baduy Luar (rural–traditional), Salatiga (semi-urban), and Kota Bambu (urban). Using a cross-sectional design, the study involved 268 participants and assessed total body fat, visceral fat, subcutaneous fat, and skeletal muscle mass using the OMRON HBF-370 analyzer. Significant differences were observed across most body composition parameters (p < 0.001). Women living in the urban area exhibited the highest levels of total fat, visceral fat, and subcutaneous fat, reflecting the influence of urbanization and sedentary lifestyle on adiposity. Conversely, women in the rural–traditional community demonstrated the highest skeletal muscle mass across all body regions, consistent with their physically demanding daily activities and traditional dietary patterns. These findings highlight the substantial role of environmental context in shaping metabolic health and cardiometabolic risk. Targeted public-health interventions—such as structured physical-activity promotion and nutrition education in urban populations, and preservation of active lifestyles in rural communities—are recommended. Longitudinal studies are warranted to clarify causal pathways and further characterize metabolic determinants.

The empowerment program for Micro, Small, and Medium Enterprises (MSMEs) in Takaras Village, Manuhing Subdistrict, Gunung Mas Regency, was carried out to address the problem of low business visibility due to limited promotional media and minimal use of digital technology. Most MSMEs still relied on traditional word-of-mouth promotion, which restricted market reach and reduced competitiveness. To overcome this, the KKN Reguler I Team Group 55 of the University of Palangka Raya implemented activities consisting of informational banner installation and business location registration on Google Maps. The methods included field surveys, banner design planning using simple digital tools, direct assistance in location registration, and banner installation at business sites. The program involved 26 MSMEs, of which 18 received banner support. The installation of banners provided a clearer visual identity for each business, while the use of Google Maps significantly improved consumer accessibility to business locations and services. The results showed that the combination of offline (banners) and online (Google Maps) promotion was effective in increasing product visibility, attracting more customers, and strengthening consumer trust. This initiative also encouraged MSMEs to adopt digital technology in their marketing strategies. Overall, the program made a tangible contribution to the digital transformation of local MSMEs and laid the foundation for sustainable community economic competitiveness

Learning Al-Qur’an and Hadith in secondary schools is still largely influenced by traditional assessment practices that prioritize written examinations and memorization, which are insufficient to capture students’ actual competencies. This situation often leads to less meaningful learning and limits the development of students’ deep understanding and Islamic character. In essence, Al-Qur’an and Hadith education is intended not only to ensure mastery of theoretical content but also to encourage the internalization and application of Islamic values in everyday life. For this reason, authentic assessment is viewed as a suitable approach because it evaluates learning outcomes in a more comprehensive manner, covering cognitive, affective, and psychomotor aspects. This study aims to analyze the implementation of authentic assessment in Al-Qur’an and Hadith learning at the secondary school level and to examine its effectiveness in improving students’ understanding and learning engagement. The research employed a quantitative method, with data collected through a Google Form questionnaire distributed to students and analyzed using descriptive analysis. The results demonstrate that authentic assessment contributes to deeper conceptual understanding, enhances practical skills such as proper Qur’anic recitation based on tajwid rules and hadith memorization, and promotes active, confident, and responsible learning attitudes. Overall, authentic assessment provides more meaningful learning experiences and represents an effective alternative assessment strategy to improve the quality of Al-Qur’an and Hadith learning in secondary schools.

This study aims to determine the effectiveness of the use of the artificial intelligence-based application (AI-Based Application) Duolingo in improving vocabulary mastery of EFL (English as a Foreign Language) students in Indonesia. This study used a descriptive quantitative approach with a single-group pre-test and post-test design involving 20 students of class VII C of SMP Negeri 1 Mappakasunggu. Data were collected through vocabulary tests, questionnaires, and classroom observations. The results showed a significant increase in students' vocabulary mastery, marked by an increase in the average score from 61 (fair category) in the pre-test to 78 (good category) in the post-test. Most students gave a positive perception of the use of Duolingo, especially regarding the gamification features, instant feedback, and simple and attractive display, which can increase motivation and learning engagement. The results of the observation also showed that students were more active and enthusiastic in using this application compared to traditional learning methods. Thus, Duolingo can be said to be effective as an AI-based learning medium to improve vocabulary mastery of junior high school students in Indonesia.  

The development of Islamic Religious Education in Banjar Land represents a long historical process marked by social, political, and religious transformations. Since the era of traditional da‘wah conducted by ulama through halaqah, surau-based instruction, and religious study circles (majelis taklim), the Islamic education system in this region has continuously evolved, eventually giving rise to formal educational institutions such as madrasahs and Islamic-based schools. This study aims to chronologically describe these historical dynamics, examine the factors influencing educational change, and analyze the roles of ulama, the Banjar Sultanate, and community institutions in the transformation of Islamic education. The research employs a qualitative method with a historical approach, involving the stages of heuristics, source criticism, interpretation, and historiography. Data were collected from scholarly literature, local manuscripts, and the works of Banjar ulama. The findings indicate that the transformation of Islamic education in Banjar Land, from the period of Islamization to the emergence of madrasahs and Islamic Religious Education in public schools, occurred through at least three major phases: (1) family- and community-based traditional da‘wah through langgar (prayer houses) and halaqah institutions; (2) the establishment of modern madrasahs in the twentieth century as a response to colonialism and modernity; and (3) the integration of Islamic education into the formal national education system in the post-independence era. This study affirms that Islamic education in Banjar Land possesses distinctive characteristics, is adaptive in nature, and is deeply rooted in local culture.

This study examines the process of religious habituation among congregants at Masjid Jami’ Al-Barokah Yasda, South Jakarta, through the perspective of Peter L. Berger’s theory of social construction. The mosque implements a series of religious programs conducted consistently on a daily basis, including ta’lim, recitation of Rotibul Haddad, congregational shalat tasbih, Yasin Tahlil Barzanji, silaturahmi, and jaulah. This research aims to understand how these programs are designed, implemented, and interpreted so as to shape the religious patterns of the congregation. This study employs a qualitative descriptive approach, with data collected through in-depth interviews with the head of the Mosque Management Board (DKM) and direct observation of religious activities. The findings indicate that religious habituation in this mosque is formed through an interconnected process of social construction: religious programs are formulated by mosque administrators as an expression of religious values and vision (externalization), subsequently carried out on a regular basis until they are accepted as traditions and a collective identity of the mosque (objectivation), and ultimately internalized by congregants as values that shape religious attitudes and consciousness of piety (internalization) in their daily lives.Overall, these religious activities are oriented toward sustaining Rahmatan lil ‘Alamin da‘wah da‘wah that promotes goodness, inner peace, and social benefit for both congregants and the surrounding community. The findings demonstrate that religious habituation, when managed consistently and adaptively, is capable of fostering a living religious culture, strengthening piety, and generating tangible social impacts within an urban religious context.

This article examines in depth the concept of protecting human dignity through the regulation of gaze and physical contact among mahram according to the Shafi‘i School of Islamic law. From an Islamic perspective, the preservation of dignity (ḥifẓ al-‘irḍ) occupies a highly fundamental position as part of the objectives of Islamic law, particularly in regulating social relations and family interactions. Although mahram relationships legally allow closer interaction, Islam continues to establish ethical boundaries to safeguard purity and moral values. This study is a normative legal research employing conceptual and normative-fiqh approaches, relying on primary sources such as the Qur’an, the Prophetic traditions (hadith), and classical as well as contemporary Shafi‘i jurisprudential literature. The findings indicate that the Shafi‘i School does not regard mahram relations as a sphere of absolute and unrestricted freedom, but instead provides clear regulations concerning gaze and physical contact. Visual interaction is permitted in a limited manner, restricted to certain parts of the body and subject to the condition that it does not provoke desire or potential moral temptation (fitnah). Meanwhile, physical contact is regulated more strictly and, in principle, is only permitted in situations of legitimate shar‘i necessity, such as medical treatment or emergency circumstances. Therefore, the regulation of gaze and physical contact among mahram in the Shafi‘i School functions as an instrument for protecting human dignity, preserving the sanctity of family relationships, and preventing behavioral deviations from an early stage.

This research aims to enhance the imperceptibility of secret messages in video steganography to prevent detection by third parties. Unlike cryptography, which focuses on securing the message content, this method seeks to conceal the very existence of the message by maintaining a visual quality nearly identical to the original video. The proposed approach utilizes an adaptive multi-bit scheme based on the Least Significant Bit (LSB) technique, which intelligently analyzes the local characteristics of each frame, including brightness, texture, and motion. This strategy allows for higher data insertion in areas with high visual tolerance while limiting bits in sensitive areas to prevent distortion. Evaluation results using PSNR and SSIM metrics indicate that while increasing message capacity (from 1-bit to 3-bit) reduces the PSNR from 51.80 dB to 39.44 dB, the method remains highly effective in preserving visual integrity. Overall, this technique proves to be more secure and superior to traditional LSB in balancing storage capacity with high-quality video output.

Modern retail expansion in Situbondo Regency has intensified competitive interaction with traditional markets, making it important to map differences in market structure, firm conduct, and performance outcomes. This study compares (i) market structure using concentration indicators (CR4 and the Herfindahl–Hirschman Index/HHI), (ii) competitive conduct (pricing practices, promotional intensity, service attributes, and relationship patterns), and (iii) performance proxies (sales turnover and selected price efficiency measures) within the SCP framework. The analysis applies a descriptive quantitative approach supported by targeted primary observations and questionnaire-based information, and complemented by official statistics and regulatory documents. Traditional-market samples include Panji, Besuki, and Panarukan markets, while modern-retail samples include local outlets of Indomaret, Alfamart, and Basmalah. Results indicate that traditional markets are relatively unconcentrated (CR4 = 38.0%; HHI = 744), consistent with a competitive structure dominated by many small vendors. Modern retail is more concentrated (CR4 = 77.0%; HHI = 1,644), suggesting moderate concentration and a tendency toward local oligopoly. Average monthly turnover per unit is higher for modern retail (IDR 36.36 million) than for traditional vendors (IDR 15.63 million). Price efficiency varies across commodities: some items show near parity, while several fresh commodities remain cheaper in traditional markets. Policy implications point to balanced local governance: zoning and permitting for modern stores, continuous revitalization of traditional markets, and strengthened MSME partnership schemes to ensure healthy and inclusive competition.

This study aims to analyze how the integration of traditional knowledge and digital knowledge shapes organizational strategies in Micro, Small, and Medium Enterprises (MSMEs) in responding to an increasingly competitive business environment. A quantitative approach was employed by applying variance-based Structural Equation Modeling (SEM) using SmartPLS 3 software to 100 MSME respondents. The research model was developed by incorporating three main latent constructs, namely digital knowledge, traditional knowledge, and organizational strategy, which were measured using several indicators relevant to the MSME context. The analysis results indicate that all research instruments meet good reliability and validity criteria, with outer loading values ranging from 0.719 to 0.902, Average Variance Extracted (AVE) values above 0.64, and composite reliability exceeding 0.87. Structural model testing reveals that digital knowledge has a strong and significant effect on organizational strategy (β = 0.799), while traditional knowledge shows a very small effect (β = 0.024). The research model explains 66.4% of the variance in organizational strategy. These findings confirm that digital transformation is the primary factor in shaping competitive strategies among MSMEs, while traditional knowledge continues to serve as a foundation for local context and cultural values. Therefore, the implementation of a hybrid management approach is considered effective in assisting MSMEs in formulating more adaptive, innovative, and sustainable strategies.

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.

This community service program aims to improve public literacy and awareness of the Islamic capital market through socialization and educational activities in Telaga Jernih Village, Secanggang District. Islamic financial literacy in rural areas remains relatively low due to limited access to information and education, leading people to prefer traditional investments such as livestock and plantations. The activity was carried out by the Community Service Program (KKN) team in collaboration with the Indonesia Stock Exchange (IDX) as the main speaker and the village government as a supporting partner. The methods used included observation, counseling, interactive discussions, and simple simulations of Islamic investment practices. The results revealed high enthusiasm among participants, reflected in their active engagement and significant improvement in understanding the concepts, principles, and products of Islamic investment. The community began to realize that Islamic capital market investments are not only halal and safe but also offer long-term economic benefits. This activity positively influenced the community’s mindset to view Islamic financial investment as a complementary form of traditional investment. The program also opened opportunities for forming a village-based Islamic investor community and establishing a financial literacy center as a follow-up initiative. Therefore, this program plays a vital role in strengthening Islamic financial inclusion in rural areas and serves as an initial step toward creating a financially literate, independent, and economically productive society.