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Heritage language loss among immigrant and diaspora communities represents a critical challenge to cultural identity, intergenerational communication, and cognitive diversity. Traditional heritage language maintenance approaches face significant barriers including limited resources, lack of qualified instructors, geographic dispersion, and competing demands of dominant language acquisition. This study investigates the effectiveness of bilingual digital story applications as innovative tools for heritage language maintenance among children aged 4-12 years. Through a 24-month longitudinal mixed-methods study involving 1,843 families across eight language communities (Spanish, Mandarin, Arabic, Korean, Tagalog, Vietnamese, Hindi, and Polish), we examined language proficiency development, cultural identity formation, family engagement patterns, and app usage behaviors. Quantitative analysis of pre- and post-intervention language assessments revealed statistically significant improvements in heritage language vocabulary (effect size d=0.68), listening comprehension (d=0.54), and oral production (d=0.47) among children using bilingual story apps for at least 20 minutes daily. Qualitative findings from parent interviews and child focus groups highlighted the apps' role in making heritage language learning enjoyable, facilitating parent-child interaction, connecting children to cultural narratives, and normalizing bilingualism. However, effectiveness varied substantially based on app design features, with interactive elements, culturally authentic content, parent involvement scaffolds, and adaptive difficulty showing strongest associations with outcomes. This research demonstrates that thoughtfully designed bilingual story apps can serve as accessible, scalable tools for heritage language maintenance, though they function most effectively as complements to rather than substitutes for rich home language environments and community connections. The study contributes empirical evidence to inform app development, family language planning, and policies supporting linguistic diversity in increasingly globalized societies.

: The rapid expansion of cybersecurity standards and threat intelligence frameworks has led to significant semantic fragmentation among security terminologies, hindering effective information retrieval and interoperability across systems. Traditional keyword-based search approaches are inadequate for capturing the contextual meaning of security terms, particularly within formal frameworks such as NIST, MITRE ATT&CK, and CWE. This study addresses this challenge by proposing CyberBERT, a transformer-based semantic search framework designed to align cybersecurity terminologies through deep contextual representation and ontology-driven reasoning. Research Objectives: The primary objective of this research is to develop a semantic retrieval model capable of understanding conceptual relationships between security terms beyond lexical similarity. Methodology: The proposed methodology fine-tunes a BERT-based model on the NIST Glossary corpus using a combination of masked language modeling and triplet loss objectives to generate discriminative semantic embeddings. These embeddings are further aligned with cybersecurity ontologies, including MITRE ATT&CK and CWE, to enhance semantic consistency and explainability. Semantic retrieval is performed using cosine similarity within a 768-dimensional embedding space and evaluated using Mean Reciprocal Rank (MRR) and Precision@K metrics. Results: Experimental results demonstrate that CyberBERT achieves an MRR of 0.832, outperforming domain-adapted baselines such as SecureBERT and CyBERT. The integration of ontology alignment improves semantic accuracy by over 6%, while robustness evaluations confirm resilience against adversarial linguistic perturbations. Visualization using t-SNE reveals coherent semantic clustering aligned with the five core NIST Cybersecurity Framework functions. Conclusions: In conclusion, CyberBERT effectively bridges semantic gaps across cybersecurity terminologies by combining transformer-based contextual learning with ontological reasoning. The framework offers a robust, interpretable, and scalable solution for semantic search, supporting improved interoperability and knowledge discovery in cybersecurity operations and standards harmonization.

Children living in orphanages face various psychological pressures, such as the loss of attachment figures, unstable social dynamics, and difficulty expressing emotions adaptively. These conditions require appropriate coping strategies to build emotional resilience. This community service activity utilizes a Service Learning (SL) approach, which integrates academic learning with community service. It aims to provide psychoeducation on emotions and coping strategies, and implements expressive writing as a means of emotional processing. The activity methods included interactive lectures, a pretest and posttest to measure understanding, an expressive writing therapy session, and a reflective interview at the end of the activity. Results showed an increase in understanding of emotions and coping strategies, as evidenced by improved posttest scores. Furthermore, expressive writing helped children express previously suppressed emotions, reduced psychological tension, and fostered a sense of relief, calm, and insight into personal problems. Overall, expressive writing has proven effective as a coping strategy in building emotional resilience in orphanage children, and the Service Learning approach has the potential for sustainable application in psychosocial support programs.

Daily drinking water requirement for healthy individuals is a crucial factor in maintaining health and physiological balance. For individuals involved in intense physical activity or exercise, fluid requirements can increase significantly, with recommendations reaching 3 to 4 liters per day to replace fluid loss through sweat, so researchers consider it important to discuss the provision of clean drinking water installations with the application of Reverse Osmosis (RO) machines that utilize PDAM water as a source, which is in the Candrasa complex to support the fulfillment of drinking water needs for the Indonesian Navy Academy Cadets. AAL is a military educational institution that requires a supply of safe and high-quality drinking water for the Cadets so that the learning and training process can run smoothly. This study covers the daily drinking water needs of AAL Cadets, the current condition of the drinking water supply, and the concept of the RO machine itself in the Candrasa complex by utilizing PDAM water to produce healthy and suitable drinking water for AAL Cadets. The results of this study are expected to increase the availability of clean and safe drinking water for AAL cadets, facilitate drinking water distribution in the Candrasa complex, support their quality of life during their education, training, and foster care, and help maintain their health and safety. This study also underscores the importance of efficient and sustainable water management in the military environment.

Implementation Artificial Intelligence as a supporting model for e-learning still faces several challenges. This is due to several aspects, such as concerns about loss spiritual touch, low digital literacy educators, limited technological infrastructure in educational institutions, and so on. On the other hand, the interaction that occurs between students and teachers is an emotional and moral dimension that occurs. The purpose study is to analyze the use AI as supporting model for e-learning in Islamic education. In this study, the method used is descriptive qualitative research. The findings study are (1) Views Islamic education practitioners on Artificial Intelligence, one which is that the existence Artificial Intelligence is not absolute replacement between students and teachers. However, Artificial Intelligence is considered means or tool in understanding learning; (2) The results  analysis explain that several AI (Artificial Intelligence) in e-learning in Islamic education have been present in several forms, including increasing access and inclusivity, translating and understanding classical manuscripts, analyzing learning and automatic assessment, material recommendation systems and learning personalization, as well Chatbots and virtual learning assistants; (3) Concept Islamic education is not only oriented towards the transfer knowledge (ta'lim), but also the formation of morals and spirituality (tarbiyah and ta'dib).

Relational bullying is a form of bullying that often occurs in the school environment and has a significant impact on the psychological and academic aspects of students. This research aims to analyze the influence of relational bullying on students’ interest in learning at various levels of education. The research method used is literature review by reviewing 11 relevant national journals indexed by Google Scoler, DOAJ and Sinta, which were published between 2018 and 2024. The results of the study show that relational bullying generally appears in the form of exclusion, spreading gossip, manipulation of social relationships, indirect mockery, and seniority behavior. The impacts caused include a decrease in academic confidence, increased anxiety and depression, loss of study concentration, and low student motivation and participation in class. This condition implies a decrease in learning achievement and student involvement in school activities. Based on the findings, it is concluded that relational bullying has a significant negative effect on students’ interest in learning. Therefore, prevention efforts are needed through the establishment of a positive school climate, strengthening the value of empathy, and the provision of sustainable counseling services to create a safe learning environment and support the development of students.

This community service activity aims to improve the financial literacy of Micro, Small, and Medium Enterprises (MSMEs) through targeted training in calculating the Break Even Point (BEP) as a financial control tool. BEP, or break-even point, is the specific point at which total revenue equals total costs, allowing businesses to determine the minimum sales volume required to avoid losses. The activity was held in the Curug Tourism Village in Bogor Regency, with 27 MSMEs from various business sectors actively participating. The implementation method included outreach, training sessions, practical BEP calculations using participants' actual business data, and post-training mentoring to ensure long-term learning. The results of the activity demonstrated a significant improvement in participants' understanding of the BEP concept and its practical application in determining selling prices, production volumes, and cost control. Therefore, the application of BEP analysis is a crucial and effective strategy in supporting the sustainability and operational efficiency of MSME businesses.

Myalgia is a muscle pain condition that can occur in a specific area of the body or spread to several regions. Its main symptoms include pain, soreness, or discomfort in the muscles, whether during movement or at rest. The pain may range from mild to severe and is sometimes accompanied by stiffness, muscle tension, or even cramps that interfere with daily activities. This condition is commonly experienced by the elderly due to aging, loss of muscle mass, improper physical activity, stress, lack of stretching, or an unhealthy lifestyle. This community service activity aimed to provide education and raise awareness about myalgia among 10 elderly participants in the Elderly Community of Kalirejo, Malang, to help them better understand prevention, pain management, and self-care strategies. The method used was service learning (SL) with achievement parameters measured through pre-test and post-test evaluations. The results showed an increase in participants’ knowledge regarding the definition, causes, and rehabilitation of myalgia from 10% to 90%, demonstrating that this program effectively improved the elderly’s understanding and awareness of myalgia.

This study examines the revitalization of the Qur’anic Education Center (TPA) in Mekar Jaya Village, which had ceased operating due to the loss of its main teacher and limited volunteer support. Using a Participatory Action Research (PAR) approach, the program engaged community members, volunteers, religious leaders, and parents in collaborative efforts to restore regular Qur’an learning activities and strengthen the TPA’s function as a vital center of Islamic education. The intervention led to increased student participation, including the return of former learners and the enrollment of new participants, as well as improved learning motivation through active engagement and parental support. This collaborative revitalization not only revived essential Islamic learning for children but also fostered sustained community involvement, highlighting the critical role of volunteers and local leadership in sustaining nonformal religious education. The model developed through this initiative provides practical implications for other rural communities seeking to maintain and strengthen Islamic educational institutions.

This study aims to determine the level of problematic smartphone use (PSU) and revenge sleep procrastination (RBP) among MTs/SMP students in District X, while also examining the relationship between the two variables. The problem of excessive smartphone use among adolescents is increasingly important to study because it can impact sleep quality, learning concentration, and mental health. This research approach uses a quantitative correlational method with data collection through a bold questionnaire (Google Form). The research sample consisted of 103 MTs/SMP students in District X who were selected using an incidental sampling technique. The RBP variable measurement scale was developed by the researcher with reference to aspects proposed by Kroese et al. (2014). Meanwhile, the PSU Scale was developed based on dimensions formulated by Foerster et al. (2015), including withdrawal, desire, loss of control, dependence on peers, and negative life consequences. The results of the correlation analysis showed that PSU had a very strong positive relationship with RBP (r = 0.833) and was statistically significant (p < 0.000). This means that the higher a person's tendency to experience problematic smartphone use, the greater the likelihood of revenge bedtime delay. Among the dimensions of PSU, withdrawal showed the highest correlation with RBP (r = 0.831). This was followed by negative life consequences (r = 0.778), craving (r = 0.577), loss of control (r = 0.489), and dependence on peers (r = 0.333). Overall, this study concludes that PSU plays a significant role in increasing RBP trends among students. These findings highlight the urgent need for awareness and intervention programs to reduce excessive smartphone use among adolescents. Therefore, the results of this study are expected to serve as a reference for schools and parents in designing strategies to prevent the negative impacts of smartphone use on adolescents.

This study presents a machine learning approach for predicting the dimensions of microstrip antenna slots based on antenna performance parameters such as frequency, gain, directivity, return loss (S11), radiation efficiency, and VSWR. A two-phase methodology was employed. In the first phase, ten regression algorithms were evaluated, and Random Forest was identified as the most effective model based on Mean Absolute Error (MAE) and R-squared (R²) scores. In the second phase, hyperparameter tuning was conducted using Grid Search to further improve the model’s performance. The optimized Random Forest model demonstrated consistent improvements in predictive accuracy, with R² values increasing across all output variables. These results indicate that the combination of regression-based modeling and systematic hyperparameter tuning is effective for capturing complex relationships in antenna design tasks. The proposed approach offers a promising data-driven alternative for geometric prediction in microstrip antenna development, particularly when analytical models are insufficient.

The digital design of cultural museums with an interactive and educational approach in Indramayu Regency aims to present a modern, innovative, and interesting cultural preservation space. This concept was born from the urgent need for a means that are able to preserve, document, and promote local cultural wealth in the midst of the rapid flow of globalization. The lack of cultural literacy among the public, especially the younger generation, as well as the loss of historical objects due to inadequate storage facilities, are the main reasons for the importance of developing digital-based museums. Through an interactive approach, visitors can actively engage with cultural collections using advanced technologies such as augmented reality (AR), virtual reality (VR), and interactive multimedia. This technology provides an immersive, engaging, and personalized learning experience, so that visitors not only see, but also virtually experience the richness of the culture on display. Meanwhile, the educational approach is designed to create an informative, collaborative, and fun learning environment, which is highly relevant for learners and the younger generation as the successors of cultural preservation. Indramayu Regency, known for its long history, maritime traditions, and distinctive coastal culture, has great potential to become a center for cultural education. This digital museum will utilize digital technology to document cultural narratives in depth, so that it can be accessed not only by local people, but also national and international tourists. With its strategic location in the city center, the presence of this museum is expected to encourage the participation of the public and tourists in understanding, appreciating, and preserving the local culture of Indramayu. Apart from being an educational facility, this museum is expected to be able to become a driver of local tourism that makes a positive contribution to the economy of the surrounding community. Digital cultural museums are not only a place for preservation, but also a symbol of a strong regional identity, reflecting the synergy between technological innovation and commitment to cultural sustainability in the modern era.

Pneumonia detection from chest X-ray images is widely used in computer-aided diagnostic systems. However, effective clinical decision support requires not only accurate classification performance but also consideration of unequal error costs, since false negative predictions may lead to more severe consequences than false positives. In addition, prediction probabilities must be well calibrated to support threshold-based medical decisions such as triage and patient escalation. This research investigates asymmetric misclassification costs and probability calibration for binary classification (PNEUMONIA vs. NORMAL) using the Hugging Face dataset hf-vision/chest-xray-pneumonia. The proposed framework utilizes a ResNet-18 architecture integrated with cost-sensitive learning through weighted cross-entropy loss (FN:FP = 5:1), threshold optimization based on validation data to reduce expected cost, and post-hoc temperature scaling for improving probability calibration. Experimental results on the independent test set indicate that the cost-sensitive approach enhances specificity and decreases expected cost compared to the conventional cross-entropy baseline. Furthermore, temperature scaling improves the reliability of probabilistic predictions, as demonstrated by better negative log-likelihood and Brier score values. The study also explores selective prediction strategies to balance prediction coverage and risk reduction, complemented by Grad-CAM visualizations and structured failure-case analysis for qualitative assessment. Overall, the findings demonstrate that incorporating cost-aware decision thresholds and calibrated probability estimates can serve as lightweight yet effective enhancements for chest X-ray classification systems in clinical decision-support applications.

The obligation to seek knowledge holds a central position in Islamic law, grounded in strong normative and theological principles. Islam classifies the pursuit of knowledge as fardhu ‘ain (an individual duty) for essential religious and worldly knowledge, and fardhu kifayah (a collective duty) for broader societal needs. This article explores the legal foundations of this obligation in Islamic jurisprudence and examines its relevance in the context of contemporary education. Using a normative-juridical method, this study analyzes primary Islamic sources such as the Qur’an, Hadith, and the views of classical and modern scholars. The findings show that Islam does not treat knowledge merely as a tool for personal development, but as a moral and spiritual responsibility. In modern educational systems, these values remain highly relevant, particularly in addressing ethical decline and the loss of purpose in learning. This article recommends the integration of Islamic legal perspectives on knowledge into educational curricula to help restore the spiritual and moral aims

This study explores the challenges and expectations faced by teachers in educating deaf students at SLB Negeri Keleyan. Using a qualitative descriptive approach, data were collected through interviews and observations to understand the real experiences of teachers in the learning process. The findings reveal that one of the main challenges is teaching students with total hearing loss from birth, which demands more time, creativity, and adaptation in instructional strategies. Teachers often adjust the curriculum based on the students’ individual abilities. Despite these difficulties, students show positive development when they receive consistent encouragement, emotional support, and recognition. Teachers express hope for improved training opportunities, especially in visual and adaptive teaching techniques, as well as better support from families and the government. Additionally, they highlight the importance of assistive learning tools and inclusive job opportunities after graduation. This study affirms that with collaborative support, deaf students have the potential to grow, learn, and contribute meaningfully to society.

The development of Artificial Intelligence (AI) technology in the education sector has undergone major changes. These changes bring great opportunities to change the learning process to be more personal, effective, and inclusive. The purpose of this journal is to analyze the potential and challenges that need to be faced in the application of AI in education so that it can run optimally. The method used in this study is the literature review method of various recent studies that discuss the use of AI in education, The results of the study show that AI has great potential to improve the personalization of learning and educational efficiency. However, there are number of significant challenges, such as limited infrastructure, privacy and data security issues, resistance from educators and students, and the digital divide that still occurs in several regions. Therefore, a strategy is needed that includes a academic laziness that reduces learning motivation, constraints on AI contextual knowledge, Inability to process complex information, loss of literacy skills, amd the risk of addiction to AI technology. With a planned and sustainable strategy, AI has great potential to improve the quality of national education and also reduce the dangers that may arise from the modern technological era.

Madrasas and Islamic boarding schools are Islamic educational institutions that have a strategic role in shaping the character, morals, and competencies of students. In addition to carrying out the formal learning process, these two institutions are also centers for moral, spiritual, and social development. In their operations, madrasas and pesantren manage various administrative activities, such as student data collection, attendance recording, grade processing, teacher data management, lesson schedule preparation, and delivery of academic information to related parties. However, many institutions still use manual systems that rely on notebooks or separate applications that are not integrated. This condition poses a number of obstacles, including the high risk of recording errors, duplicate data that is difficult to track, the slow process of recapitulation of grades and attendance, and delays in the delivery of academic information. In addition, manual processes cause administrative management to be less efficient and prone to data loss. As the need for fast, accurate, and accurate information increases, technological solutions are needed that are able to integrate all administrative and academic processes in one centralized system. Web-based information systems are an effective alternative to improve work efficiency, minimize errors, and facilitate access to information for teachers, students, and guardians. With the implementation of a well-managed system, madrasas and Islamic boarding schools are expected to improve the quality of services and support a more modern, structured, and sustainable learning process.

With the times, cash transactions that used to use cash are now turning to credit cards. However, the increasing use of credit cards presents challenges, especially in maintaining customer loyalty. Customer churn is the loss of customers within a certain period for various reasons. Logistic Regression is a machine learning algorithm that studies the relationship between a dependent variable and several independent variables and Extreme Gradient Boosting (XGBoost) is a Gradient tree-boosting algorithm that offers out-of-core learning and sparsity awareness.The purpose of this study is to compare the performance between Logistic Regression and Extreme Gradient Boosting (XGBoost) algorithms in predicting customer churn in credit card services using evaluation metrics such as accuracy, precision, recall, and F1-score. Based on the research results, it can be concluded that XGBoost has better performance in all evaluation metrics, both in terms of precision, recall, F1-score, and accuracy. Based on the research, XGBoost shows superior performance compared to Logistic Regression in all evaluation metrics.

Advances in information and internet technologies have significantly transformed the business environment, including the financial sector. The COVID-19 pandemic has further accelerated this digital adoption, expanding the e-commerce industry and highlighting the necessity for secure online transactions. Credit Card Fraud Detection (CCFD) stands critical as the prevalence of fraudulent activities continues to rise with the increasing volume of online transactions. Traditional methods for detecting fraud, such as rule-based systems and basic machine learning models, tend to fail to keep pace with fraudsters' evolving tactics. This study proposes a novel ensemble deep learning-based approach that combines Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Multilayer Perceptron (MLP) with the Synthetic Minority Oversampling Technique and Edited Nearest Neighbors (SMOTE-ENN) to address class imbalance and improve detection accuracy. The methodology integrates CNN for feature extraction, GRU for sequential transaction analysis, and Multilayer Perceptron (MLP) as a meta-learner in a stacking framework. By leveraging SMOTE-ENN, the proposed approach enhances data balance and prevents overfitting. With synthetic data, the robustness and accuracy of the model have been improved, particularly in scenarios where fraudulent examples are scarce. The experiments conducted on real-world credit card transaction datasets have established that our approach outperforms existing methods, achieving higher metrics performance.

TK Aisyiyah Bustanul Athfal 1 Bengkulu is an early childhood education institution that still manages administrative and academic processes manually, such as recording student data, admitting new learners, and assessing learning outcomes. This has the potential to cause recording errors, data loss, and delays in information processing. This research aims to design a web-based school information system that can help manage school data and information in a computerized manner. The system includes student management, payment, assessment, and school profile modules. The methods used in designing this system include observation, interview, and literature study. System requirements analysis was conducted to identify the needs of users, both school managers and the community. The system was developed using Codeigniter 3 framework and MySQL database. It is expected that this system can improve the efficiency and accuracy of data management and accelerate the delivery of real-time information.