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Excessive smartphone use among adolescents, especially students, can develop into smartphone addiction and potentially disrupt learning concentration. At SMP Negeri 11 Jambi City, most students use smartphones for long periods for entertainment and social media until late at night, which leads to drowsiness and reduced focus during lessons. This study employed a quantitative method with a cross-sectional design. The population consisted of all seventh-grade students at SMP Negeri 11 Jambi City, totaling 352 students, with a sample of 175 respondents selected through stratified proportionate random sampling. Data were collected using the Smartphone Addiction Scale–Short Version (SAS-SV) questionnaire and a learning concentration questionnaire. Data analysis was conducted using univariate and bivariate methods with Kendall’s tau-b test. The results showed that 84% of respondents experienced smartphone addiction, while 61.7% had a good level of learning concentration. Kendall’s tau-b test indicated a significant relationship between smartphone addiction and learning concentration (p-value = 0.000; r = 0.243), showing a weak association. Higher levels of smartphone addiction were linked to decreased learning concentration. Therefore, active involvement from schools and parents is needed to guide adolescents in managing stress and boredom positively.

The phenomenon of digital da'wah has significantly transformed the ways religious knowledge is accessed, interpreted, and disseminated in contemporary society. This study aims to analyze the patterns of actions and perceptions of TikTok and YouTube users toward religious content delivered by Gus Baha within the context of the digital religious space. Using Max Weber’s theory of rationalization as an analytical framework, this research explores how religious authority, knowledge transmission, and user interpretation undergo processes of rationalization on digital platforms.This study employs a qualitative approach using virtual ethnography, content analysis, and in-depth interviews with users of both platforms. Data were collected through observation of uploaded content, analysis of user interactions and comments, and examination of engagement dynamics.The findings indicate that user responses to Gus Baha’s content reflect Weber’s four types of social action. Instrumentally rational actions are evident in the use of short videos as practical and efficient learning tools. Value-oriented rational actions appear in users’ consistent efforts to deepen religious understanding. Affective actions emerge from emotional attachment to Gus Baha’s communicative style, while traditional actions are reflected in the perception of digital da'wah as a continuation of established religious learning traditions. Furthermore, digital rationalization through algorithms, short-video formats, and platform accessibility, shapes how religious knowledge is selected, interpreted, and circulated.This study concludes that digital religious spaces function not only as channels of dissemination but also as arenas for the transformation of religious authority, meaning construction, and religious practice in the digital era.

This research is motivated by the negative impacts of production activities at a manufacturing company engaged in rubber processing, specifically at PTPN VII Unit Pematang Kiwah Natar, South Lampung. The factory's operations directly impact the environment, generating noise pollution, air pollution, unpleasant odors, and liquid waste. This situation requires the company to implement Environmental Management Accounting (EMA) to balance business sustainability with social and environmental responsibility. This is in line with Law No. 40 of 2007 concerning Limited Liability Companies and PSAK 1 of 2021. The main objective of the study was to evaluate the suitability of the implementation of environmental management accounting at PTPN VII Uni ;t Pematang Kiwah Natar, South Lampung, based on the International Guidance Document IFAC 2005 and PSAK 1 of 2021. This study used a qualitative descriptive method. Primary and secondary data were collected through interviews, observation, and documentation. The research results show that the company has implemented environmental management accounting using PSAK 1 of 2021, where the company has fulfilled the identification, presentation, measurement, recognition, and disclosure stages using the 2022 sustainability report and the 2022 financial statements of PTPN VII. Furthermore, PTPN VII Unit Pematang Kiwah Natar, South Lampung, has classified environmental costs by allocating environmental costs based on the International Guidance Document IFAC 2005 and Ikhsan (2008). Therefore, PTPN VII Unit Pematang Kiwah Natar, South Lampung, has demonstrated its commitment to environmental regulatory compliance.

This study is developed as a conceptual paper that analyzes halal marketing practices through a maqashid shariah lens across three operational domains: digital marketing, customer service and business administration. The paper is motivated by recent developments in the global halal economy, the stronger linkage between halal assurance, governance quality and digital transparency, and Indonesia's policy acceleration toward mandatory halal implementation in October 2026. Methodologically, the study uses a structured literature review and conceptual synthesis of international reputable journals and relevant Indonesian journals, complemented by policy and industry reports. The analysis maps prior findings into maqashid dimensions (protection of religion, life, intellect, wealth and dignity) and translates them into practical indicators of honesty, transparency, fairness, consumer protection and procedural reliability. The paper argues that credible halal marketing depends not only on promotional messages but also on service conduct and disciplined administrative governance. It contributes a conceptual framework and practical propositions for evaluating halal marketing practices in MSMEs and service businesses.

The results of the 2022 PISA indicate that science skills and interest in Indonesia are still low. One way to increase interest in science is by introducing inventor figure of science and techonology. However, alternative learning media that are communicative and relevant to adolescents' media consumption habits are needed. Therefore, this study aims to design a character design for an inventor in the field of science and technology for a Webtoon comic as an educational tool for adolescents aged 15–18. This study used qualitative and quantitative methods. The qualitative method was used to understand the needs and interests of the target audience and to gather information from the literature for scientific sources in designing the inventor character. The quantitative method was used as one of the character design evaluation criteria, based on voting results by adolescents aged 15–18. The character design used Marika Nieminen's principles: demographics, silhouette and shape, color, and characteristics. Several alternatives were used to generate character designs, which were then evaluated to determine the final design. The character design evaluation will be assessed using several variables within an evaluation framework.

Exclusive breastfeeding (EBF) is essential for infant growth and immunity during the first six months of life. Despite its benefits, many primiparous mothers face challenges in maintaining EBF due to lack of experience, stress, and insufficient support. Husband support has been identified as a crucial social factor that can influence a mother’s confidence and success in breastfeeding. This study aimed to examine the relationship between husband support and the success of exclusive breastfeeding among primiparous mothers in the Kalibaru Public Health Center area. A cross-sectional correlational design was employed, with 90 primiparous mothers selected through purposive sampling. Data were collected using structured questionnaires measuring husband support, including emotional, informational, and instrumental dimensions, and exclusive breastfeeding practice, following the World Health Organization (WHO) definition. Descriptive statistics summarized participants’ demographic characteristics and levels of support, while inferential analysis using the Chi-square test and Pearson correlation determined the association between husband support and EBF success. The results indicated that higher levels of husband support were significantly associated with successful exclusive breastfeeding among primiparous mothers (p < 0.05). Among the types of support, emotional and informational support were particularly influential in enhancing maternal confidence and overcoming breastfeeding challenges. These findings suggest that encouraging husband involvement through prenatal education, counseling, and family-based interventions can improve EBF rates. In conclusion, husband support plays a vital role in the successful practice of exclusive breastfeeding among first-time mothers. Health programs should actively involve fathers to provide emotional, informational, and practical support, thereby promoting optimal breastfeeding outcomes and infant health in the community.

This study evaluates the effectiveness of the Free Nutritious Meal Program (MBG) in improving students' cognitive abilities based on nutritional status in Brangsong Village, Indonesia. The research used a mixed-methods approach, combining quantitative and qualitative designs. Quantitative data were collected from 30 student beneficiaries via questionnaires and analyzed using descriptive statistics and Pearson correlation coefficients. Qualitative data were gathered through in-depth interviews with managers of the Nutrition Fulfillment Service Unit (SPPG), school authorities, students, and parents, and analyzed thematically. The results show a positive and significant relationship between nutritional status and academic achievement, but no significant relationship with students' cognitive ability. Nutrient intake, however, was positively and significantly associated with both cognitive ability and academic achievement. The strongest relationship was observed between cognitive ability and academic achievement. These findings suggest that the MBG program effectively supports academic achievement by improving nutrient intake and cognitive ability. However, its impact on students’ nutritional status has not been uniformly distributed.

Background: Diabetes Mellitus (DM) is a disease that cannot be completely cured or cannot even be completely cured. The vile shard plant is empirically used by the community to treat diabetes (DM). This study aims to conduct phytochemical screening and test the activity of 96% ethanol extract of kejibeling leaves (Strobilanthes crispus (L.) Bl.) as a herbal antidiabetic in male white mice (Mus musculus) with alloxan induction. Method: This research is an experimental laboratory research with a true experimental posttest control design using a completely randomized design (CRD) with 5 treatments and 5 replications. Treatment P1 (without treatment) as normal control (N), P2 as positive control (+), P3 as negative control (-), P4 kejibeling leaf extract 250 mg/kg BW, P5 kejibeling leaf extract 500 mg/kg BW. Result:The results of phytochemical screening showed the presence of alkaloids, flavonoids, tannins, saponins, terpenoids and steroids. SPSS results show that the data is normally distributed (p>0.05) and homogeneous (p>0.05). The results of the ANOVA on the treatment of giving keji beling leaf extract 250 mg/Kg BW showed a sig. 0.393 (p>0.05) and treatment of 500 mg/Kg BW obtained a sig value. 0.517 (p>0.05). Conclusion:The conclusion from the research results shows that administering doses of 250 mg/kg BW and 500 mg/kg BW of keji beling leaf extract can reduce blood sugar levels in mice. It is hoped that the results of this research will be useful for the community as an antidiabetic therapy using kejibeling leaves (Strobilanthes crispus (L.) Bl.).

This study aims to analyze the role of Islamic Religious Counselors as street-level bureaucrats in implementing Islamic Community Guidance policies in Batang Regency. The study used a mixed-methods approach with a sequential explanatory design, beginning with the collection and analysis of quantitative data and then deepening with qualitative data. Quantitative data were obtained from 53 respondents using a questionnaire that was tested for validity and reliability. In contrast, qualitative data were collected through in-depth interviews, observations, and documentation with religious counselors, Ministry of Religious Affairs officials, the KUA (Office of Religious Affairs), and the community. Data analysis was conducted descriptively and analytically using Michael Lipsky's street-level bureaucracy theory as an analytical framework. The study's results indicate that the role of Islamic Religious Extension Workers as street-level bureaucrats is positively and strongly associated with the successful implementation of the Islamic Community Guidance policy (r = 0.826; p < 0.01). Qualitative findings revealed that extension workers exercise discretion in the form of community assistance, adjustments to extension methods, strategic flexibility, and responsiveness to socio-religious issues at the local level. This study confirms that the effectiveness of Islamic Community Guidance policies is not determined solely by formal policy design but is highly dependent on the capacity, flexibility, and discretion of Islamic Religious Counselors, the implementing actors at the field level. These findings provide theoretical contributions to the development of street-level bureaucracy studies in the context of religious policy, as well as practical implications for formulating more contextual and responsive policies.

This study examines the implementation of Papua Special Autonomy in the context of the relocation policy affecting indigenous betel nut vendor women in Sorong City, using an interpretative perspective. The research focuses on how betel nut women vendors understand and experience the relocation policy as indigenous actors directly affected by public policy. The objective of this study is to explore the meanings, experiences, and interpretations constructed by these women in relation to relocation policies under the framework of Papua Special Autonomy.  This research employs a qualitative method with an interpretative approach, utilising in-depth interviews, field observations, and document analysis. The findings reveal that the relocation policy has not fully reflected the core principles of Special Autonomy, particularly in protecting, empowering, and recognising the rights of indigenous Papuans. The women vendors interpret relocation not merely as an urban spatial arrangement, but as a restriction on their economic space and socio-cultural identity.  This study contributes theoretically by enriching the discourse on Special Autonomy from the perspective of local actors, and practically by offering policy recommendations for more inclusive, participatory, and culturally sensitive relocation policies in Papua.

Bullying is a psychosocial hazard that can occur in the school environment and may negatively affect students’ mental health, social interactions, and learning processes. Bullying can take various forms, including physical, verbal, and psychological actions that occur repeatedly and cause victims to experience emotional distress, decreased learning motivation, and feelings of insecurity at school. This Community Service Program (PKM) aimed to increase high school students’ knowledge and awareness regarding the dangers of bullying and its prevention through the principles of Occupational Safety and Health (OSH). The methods used in this activity included delivering educational materials through lectures, conducting question-and-answer sessions to assess students’ initial understanding, and facilitating interactive discussions to encourage active participation. Monitoring and evaluation were also conducted to assess the effectiveness of the activity. The results of the program indicated a significant improvement in students’ understanding and awareness of the forms, impacts, and prevention strategies related to bullying in the school environment. Students also demonstrated a more critical attitude toward bullying behavior and a greater awareness of the importance of creating a safe, healthy, and respectful school environment. The application of an OSH-based approach in bullying prevention is expected to strengthen the safety culture in schools and support the development of a conducive learning environment for all students.  

The rapid development of social media, particularly TikTok, has transformed digital marketing strategies by emphasizing influencer endorsement and content marketing to reach Generation Z consumers. This generation is characterized by high social media usage and strong responsiveness to visual content and influencer recommendations. This study aims to analyze the effect of influencer endorsement and content marketing on the purchase intention of Generation Z TikTok users toward Antarestar products in Makassar City, both partially and simultaneously. This study employed a quantitative approach with an associative research method. The population consisted of Antarestar consumers residing in Makassar City, and the sample was determined using purposive sampling. A total of 140 respondents who met the research criteria were involved in this study. Data were collected through a questionnaire using a five-point Likert scale and analyzed using multiple linear regression analysis with the assistance of IBM SPSS Statistics software. The results of this study indicate that (1) influencer endorsement has a positive and significant effect on the purchase intention of Generation Z TikTok users toward Antarestar products, (2) content marketing has a positive and significant effect on purchase intention, and (3) influencer endorsement and content marketing simultaneously have a positive and significant effect on the purchase intention of Generation Z TikTok users toward Antarestar products in Makassar City. The findings conclude that the effectiveness of Antarestar’s digital marketing on TikTok is strongly influenced by credible and attractive influencers as well as relevant, informative, and consistent marketing content. Therefore, optimizing influencer endorsement and content marketing strategies is essential to enhance purchase intention among Generation Z consumers.

Inconsistent terminology across cybersecurity frameworks undermines global governance and interoperability. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0) and ISO/IEC 27001:2022 share similar objectives but diverge semantically in defining risk, control, and resilience. This semantic gap causes difficulties in compliance mapping and automated policy translation. Research Objectives: This study aims to analyze the semantic similarity and divergence between NIST and ISO/IEC 27000 terminologies, identify conceptual structures influencing interoperability, and propose an AI-assisted foundation for harmonizing cybersecurity language globally. Methodology: A mixed-method semantic comparative design integrates Natural Language Processing (NLP) and ontology mapping. Using the nist_glossary.csv dataset and ISO vocabularies, terms were normalized and analyzed via cosine similarity using sentence-transformer embeddings. Ontological alignment was visualized through the Semantic Threat Graph (STG) and validated by certified experts using Cohen’s Kappa reliability tests. Results: From 672 term pairs, results show 40.9% high semantic equivalence, 38.8% partial overlap, and 20.3% semantic divergence. Strongest alignment appears in “Protect” and “Identify” domains, while divergences occur in governance and recovery-related terms. Ontology mapping revealed three conceptual clusters—Risk Governance, Technical Safeguards, and Organizational Readiness. Conclusions: Findings confirm a 79.7% total semantic alignment, indicating strong potential for harmonizing global cybersecurity standards. The study contributes an empirical model combining computational linguistics and AI-based ontology mapping to establish semantic interoperability, enabling unified cybersecurity governance and AI-driven compliance automation. Keywords: Semantic Interoperability; Ontology Mapping; Cybersecurity Frameworks; Terminology Alignment; AI Harmonization

Explainable artificial intelligence (XAI) has become a critical requirement in cybersecurity due to the high-stakes nature of security decision-making and the limitations of black-box learning models. This study investigates the construction of an explainable cybersecurity knowledge representation by leveraging standardized terminology from the NIST cybersecurity glossary. The primary problem addressed is the lack of transparent and semantically grounded reasoning mechanisms in existing AI-driven cybersecurity systems, which limits trust, accountability, and analyst adoption. To address this challenge, we propose a NIST-based semantic knowledge graph that embeds explainability directly into its ontology structure and reasoning process. The proposed framework systematically extracts definitional entities and relations from NIST glossary entries to construct a domain ontology and a multi-relational knowledge graph. A rule-based semantic relation extraction method is employed to ensure faithful, interpretable, and reproducible reasoning paths. The resulting knowledge graph contains over 3,000 cybersecurity concepts and approximately 27,000 semantic relations, covering hierarchical, associative, dependency, and mitigation semantics. Experimental evaluation demonstrates that the proposed approach achieves a high level of explainability, with 92.4% of reasoning outcomes being fully traceable and only 1.4% classified as non-traceable. Most explainable reasoning paths are limited to two or three hops, indicating an effective balance between inferential depth and human interpretability. Structural analysis further confirms the presence of meaningful hub concepts that support multi-hop semantic inference. These results confirm that ontology-driven, standard-based knowledge graphs provide a robust foundation for explainable cybersecurity intelligence. The study concludes that explainability-by-design, grounded in authoritative standards, offers a viable and trustworthy alternative to opaque AI models for cybersecurity applications.

This study examines the application of Transparent Artificial Intelligence (AI) for fraud detection in public welfare programs using publicly available administrative data. Persistent challenges in welfare governance such as misallocation, fraud, and data inaccuracy necessitate analytical frameworks that are both effective and explainable. The research aims to design and evaluate an interpretable anomaly detection system capable of identifying irregularities in welfare distribution while maintaining transparency and accountability. Methodologically, the study employs two unsupervised models Isolation Forest and Local Outlier Factor (LOF) to detect anomalies in sub-district-level welfare data, incorporating features such as population size, number of beneficiaries, and coverage ratio. An Explainable AI (XAI) framework integrating surrogate Random Forests, Permutation Feature Importance (PFI), and local linear surrogates (LIME-like) is applied to ensure interpretability of both global and local model behaviors. Findings reveal that receivers per 1000 population and percentage coverage are dominant determinants of anomaly scores. Fifteen administrative units were flagged for potential inconsistencies suggesting over- or under-reporting of beneficiaries. Cross-validation between IF and LOF models confirmed consistency in identifying anomalous regions. The integrated XAI explanations enhance transparency, enabling policymakers and auditors to trace the rationale behind detected anomalies. In conclusion, the proposed Transparent AI framework demonstrates that combining anomaly detection with interpretability tools can strengthen accountability and fairness in welfare administration. It offers a reproducible, ethical, and data-driven approach to social program monitoring, reinforcing public trust and supporting responsible AI governance.

High-dimensional clinical data exhibit complex and non-linear relationships among patient attributes, where outcomes are often influenced by feature interactions rather than isolated variables. However, many existing machine learning models prioritize predictive performance while providing limited interpretability and insufficient insight into interaction structures. This study aims to address this limitation by developing an interpretable and robust framework for feature interaction mining in clinical data. We propose a hybrid tree–neural modeling framework that explicitly captures and ranks feature interactions while maintaining stable predictive performance. Tree-based ensemble models are employed to identify non-linear interaction patterns, while neural representations enhance learning flexibility and generalization. The framework integrates interaction importance analysis, cross-validation–based stability assessment, and evaluation across multiple data splits to ensure robustness and interpretability. Experiments conducted on a real-world high-dimensional clinical dataset demonstrate that the proposed approach achieves consistent predictive performance, with AUC values ranging from 0.628 to 0.641 across five cross-validation folds (mean AUC ≈ 0.633). Performance remains stable under varying train–test splits, indicating strong generalizability. Interaction analysis reveals that a small number of dominant feature interactions—such as age combined with length of hospital stay and medication count combined with diagnostic information—consistently contribute to model predictions, appearing in over 80% of validation folds. Ablation studies further confirm that removing interaction-aware components leads to noticeable performance degradation, highlighting their importance.  In conclusion, this study demonstrates that explicit feature interaction modeling enhances interpretability, stability, and generalization in clinical prediction tasks. The proposed hybrid framework provides a reliable foundation for developing trustworthy and transparent clinical decision-support systems

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.

This study analyzes the juridical issues related to the implementation of criminal sanctions below the statutory minimum, as reflected in Cassation Decision Number 7853/K/Pid.Sus/2024. The focus of this research centers on the tension between the rigid provisions of Article 111 paragraph (1) of Law No. 35 of 2009 concerning narcotics and the reality of judicial practice, which often deviates from these provisions. This phenomenon raises debates regarding the extent to which the principle of legality can be compromised in pursuit of justice without undermining the pillar of legal certainty within Indonesia’s criminal justice system. Using a normative legal research method with a statutory and case study approach, this study qualitatively analyzes judges’ considerations. The findings indicate that, although the policy of imposing sentences below the minimum carries the risk of creating legal uncertainty, the Supreme Court in this case reinterpreted the principle of legality. Judges tend to prioritize proportionality and substantive justice to avoid purely mechanical punishment. The study concludes that, while judicial discretion represents a concrete expression of judicial independence, such practice requires clearer normative parameters. Without explicit regulation, deviations from the statutory minimum risk widening disparities in judicial decisions. Therefore, standardized sentencing guidelines are necessary to preserve legal integrity while maintaining a sense of justice for defendants.

This study aims to analyze the fundamental role of Islamic Religious Education (PAI) teachers in shaping the noble character of students using a qualitative approach through literature review techniques. Data were collected from various scientific sources published between 2020 and 2026 and analyzed using content analysis methods, which included stages of reduction, categorization, and conceptual synthesis. The main focus of this study is to examine how PAI teachers integrate the values of tawhid and worship into the daily behavior of students, so that religious education does not merely remain at the cognitive level but manifests in noble character. The findings indicate that PAI teachers play a strategic role as murabbi, mu’allim, and muaddib, whose success is determined by the integration of pedagogical, professional, social, personality, and spiritual competencies. Amid the dynamic challenges of the digital era, teachers are required to revitalize their roles not only as educators but also as moral exemplars (uswatun hasanah) and agents of character transformation. The synergy between teachers' competencies and consistent exemplification is the key to safeguarding the students' morality and fostering a resilient personality in the school environment.

The low interest in reading and learning outcomes of students in Indonesia poses a significant challenge in the education sector. The Kampus Mengajar program has emerged as one of the solutions to enhance literacy and the quality of learning in schools. This article aims to analyze the impact of literacy activities initiated through the Kampus Mengajar program on student learning outcomes using a literature review approach. The research uses a qualitative method in the form of a literature review that examines various relevant sources published between 2020 and 2026. The study's findings indicate that structured literacy activities, such as reading for 15 minutes before lessons, have a significant impact on the improvement of students' cognitive, affective, and psychomotor skills. Literacy is not limited to reading and writing skills but also serves as a crucial foundation for developing critical thinking abilities, which are directly correlated with students' academic performance. Therefore, the Kampus Mengajar program can be an important driver to enhance reading interest and the quality of learning, as well as shape a more critical and high-achieving generation in the future.