This study aims to examine how the integration of Islamic law (syariah) and customary law (adat) occurs within Indonesia’s legal system and to identify the opportunities and challenges arising from this integration. Employing a literature based approach and normative socio legal analysis, the research draws on constitutional, legislative, and judicial sources, supplemented by qualitative case studies from regions such as Aceh, West Sumatra, Bali, and Papua. The findings indicate that while there is significant potential for harmonization particularly through locally grounded dispute resolution mechanisms and hybrid legal practices structural and normative obstacles persist, including institutional fragmentation, normative conflicts in areas like inheritance, and human rights concerns. The study concludes that meaningful integration requires deliberate legal frameworks, inclusive policymaking, and legal education that embraces normative diversity. This research contributes to the broader understanding of legal pluralism in Indonesia and underscores the importance of recognizing diverse legal traditions to foster a more inclusive and culturally responsive justice system.
This study examines how storytelling and digital content quality shape consumer loyalty through customer engagement in the skincare industry. A quantitative explanatory design was employed using a cross-sectional survey of 120 consumers selected through purposive sampling. Eligible respondents had purchased and used the focal skincare brand and had been exposed to its official social media content. Data were collected using a 24-item questionnaire and analyzed through partial least squares structural equation modeling to evaluate the measurement model, direct relationships, and mediating effects. The results demonstrate that storytelling positively influences customer engagement and consumer loyalty. Digital content quality also has positive effects on customer engagement and consumer loyalty, while customer engagement significantly enhances consumer loyalty. The mediation analysis further shows that customer engagement partially mediates the effects of both storytelling and digital content quality on consumer loyalty. The variance accounted for values of 27.46 percent and 34.57 percent indicate complementary partial mediation for storytelling and digital content quality, respectively. Digital content quality emerges as the stronger predictor of customer engagement, suggesting that consumers respond particularly favorably to digital content that is relevant, informative, consistent, and professionally presented. The findings demonstrate that consumer loyalty is developed not only through direct exposure to compelling narratives and high-quality content, but also through the cognitive, emotional, and behavioral engagement generated by these communication strategies. This study contributes to digital marketing literature by clarifying customer engagement as a relational mechanism through which storytelling and digital content quality translate into consumer loyalty. Skincare brands should therefore integrate compelling narratives with credible and high-quality social media content to strengthen engagement and sustain long-term consumer relationships.
Electrification of logistics creates an urgent need to balance safety and limited battery capacity with operational efficiency in electric autonomous trucks. This paper presents an Energy-Aware Hierarchical Fuzzy System (EA-HFS) for collision mitigation and operational energy optimization. EA-HFS integrates an Energy Supervisor, Behavioral Planning, Overtaking Feasibility, Velocity Control, and Motion Control. The system incorporates state of charge (SOC), payload, and route grade into decision-making to balance safety and energy. Implementation uses the CARLA simulator extended with truck dynamics and a 250 kWh battery model. The EA-HFS is compared against a hierarchical fuzzy baseline, an informed Rapidly-exploring Random Tree Star (RRT*) planner with model predictive control, and a Rapidly-exploring Random Tree with Particle Swarm Optimization (RRT-PSO) energy-aware planner across urban, highway, and uphill logistics scenarios. Experimental results show that EA-HFS reduces energy consumption by 20% in kWh/km and decreases the aggregate safety cost by 25% while maintaining or improving minimum time-to-collision statistics. The EA-HFS reduces the collision frequency from 0.12 to 0.05 events per run and requires an average control cycle of 28 ms, meeting real-time constraints. Ablation studies confirm that the Energy Supervisor and SOC inclusion drive most energy gains without compromising safety. The proposed approach enables safer, more energy-efficient freight operations and offers a practical pathway to deployment in logistics fleets.
This study examines the revitalization of Pancasila values in the digital era as a strategic framework for strengthening moral and civic awareness among Indonesian youth. Amid rapid technological development and globalization, national identity, social cohesion, and ethical values face increasing challenges. Using a descriptive-analytical method with a conceptual and normative legal approach, the study explores how digital transformation influences moral reasoning, social behavior, and national consciousness. The revitalization of Pancasila is implemented through digital literacy programs, ethical education, and civic engagement reflecting the principles of humanity, unity, democracy, and social justice. Grounded in the 1945 Constitution and Law No. 20 of 2003 on the National Education System, the study highlights the importance of integrating civic, moral, and digital ethics education. It also identifies digital threats such as misinformation, cyberbullying, and online radicalization. A multi-sectoral strategy involving government, educators, and digital communities is proposed to strengthen ethical awareness and ideological resilience among youth.
The deployment of deep learning models for ocular disease screening in resource-limited environments requires a precise balance between predictive accuracy and computational efficiency. This study provides a comprehensive comparative benchmarking of two edge-optimised Convolutional Neural Network (CNN) architectures, MobileNetV2 and ShuffleNetV2, for multi-class ocular disease classification using an imbalanced four-class subset of the ODIR-2019 dataset. The primary contribution addresses the evaluation of model robustness under severe class imbalance, specifically targeting minority classes such as glaucoma and diabetic retinopathy. The quantitative results show ShuffleNetV2 achieving an inference time of 19.2 ms and a compact model size of 9.1 MB with a global accuracy of 90.8%. MobileNetV2 records a higher global accuracy of 92.1%, an inference time of 28.4 ms, and a model size of 14.2 MB. Furthermore, MobileNetV2 yields superior class-wise F1-scores for critical pathologies, registering 0.82 for glaucoma and 0.85 for diabetic retinopathy, surpassing ShuffleNetV2's respective scores of 0.79 and 0.82. This analysis delivers a definitive technical benchmark for selecting efficient neural architectures to guarantee predictive robustness in remote clinical screening applications.
