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Background: Thel postpartum periodl is al recovery phase involving lphysical and psychologicall changes, during which mothersl often experience fatigue due to increased cortisol levels. Excessive fatigue can interfere with the breastfeeding process and reduce motivation for exclusive breastfeeding. Lavender aromatherapyl is al non-pharmacologicall therapy containing linalool with sedative effects to provide relaxation and reduce fatigue levels. lObjective: This studyl aimed tol determine thel effect ofl lavender aromatherapy onl reducing fatigue in postpartuml mothers atl Puskesmas Ibu, West Halmahera Regency. Methods: Thisl study was al Pre-Experimentall study withl a Onel Group lPretest-Posttest lDesign. The studyl population consisted of lall postpartum mothersl at Puskesmas Ibu, with a total sampling technique of 30 respondents. The research instrument used anl observation lsheet, and ldata analysis wasl performed usingl the Paired Sample T-Testl (α = 0.05). Results: The majorityl of lrespondents' characteristics were inl the agel range of 20-35 yearsl (76.7%), had Senior High School education (60%), were unemployed/housewives (70%), and were primiparous (60%). The resultsl of thel Paired Samplel T-Testl showed al mean fatigue score before the intervention of 27.33, which decreased to 11.50 after the intervention (mean difference 15.833) with a lp-value = l0.000. lConclusion: There isl al significant effectl ofl lavender aromatherapyl administration on reducing fatigue lin postpartum lmothers at Puskesmas Ibu, Halbar Regency. Lavender aromatherapy is effective as a complementary therapy to aid postpartum maternal recovery.

This study aims to analyze the implementation of bureaucratic reform in improve the administrative performance of the Sorong City Regional Secretariat. The research uses a mixed-methods approach, combining quantitative and qualitative methods. Quantitative data were obtained by distributing questionnaires to 30 government apparatus respondents using the Likert scale and were analyzed using descriptive statistics and correlation analysis. Meanwhile, qualitative data were obtained through in-depth interviews with key informants and analyzed using thematic analysis and data triangulation. The results of the study show that the implementation of bureaucratic reform is positively related to the organization's administrative performance. The variables of policy communication, apparatus resources, and bureaucratic structure show a fairly strong correlation with administrative performance, with bureaucratic structure exhibiting the strongest relationship. Qualitative findings also show that bureaucratic reform has been carried out through simplifying procedures, updating SOPs, digitizing administration, and strengthening the performance reporting system. Theoretically, this research strengthens policy implementation theory and public organizational performance theory, which emphasize the importance of policy communication, human resource capacity, and the effectiveness of organizational structures in improving bureaucratic performance. However, this study has limitations in scope, as it is confined to a single organizational unit and a relatively small number of respondents. Therefore, further research is suggested to expand the research object in several regional apparatus organizations in order to obtain a more comprehensive picture of the implementation of bureaucratic reform at the local government level.

This research examines the innovation of community empowerment rooted in local wisdom for environmental management and green economy advancement in Ketegan Village, Taman District, Sidoarjo Regency. The study stems from the increasing environmental issues resulting from urban development and the limited public understanding of sustainable economic measures. The research examines how local values—like cooperative efforts and social responsibility—can be incorporated into innovative, economically effective environmental management frameworks. Employing a qualitative descriptive method, data collection involved in-depth interviews, participatory observation, and the documentation of environmental policies and socio-economic information. The results indicate that residents of Ketegan have effectively created an empowerment model that integrates tradition and innovation via waste bank management, the use of organic waste for compost and biogas, and community-driven green economy projects engaging women and youth. Cooperation between the government, community, and universities has been essential in maintaining these initiatives. However, institutional capability, online marketing, and program viability continue to pose considerable obstacles. The research suggests enhancing community potential by providing training in green entrepreneurship, facilitating digital transformation, and incorporating local wisdom principles into sustainable development strategies. The results confirm that innovation rooted in local wisdom can effectively create resilient, competitive, and environmentally aware communities.

This research investigates transparency and accountability in the management of state-owned assets (BMN) at the East Java Regional Police's Logistics Bureau as a step toward enhancing effective, adaptive, and integrity-focused governance in police logistics. The research context arises from the growing public call for transparency and accountability in managing state assets, in line with policies for bureaucratic reform and digitalization in the police logistics framework. This study utilized a qualitative descriptive approach involving in-depth interviews, observations in the field, and analysis of documents. The results show that adopting a unified digital system has enhanced the efficiency of asset management, sped up inventory operations, and reduced the risks of asset misuse. Nonetheless, obstacles persist, such as restricted human resource capacity, data alignment problems, and inadequate performance-based assessments. The conversation emphasizes that effective asset management relies on the implementation of adaptive governance, motivation for public service, and precise performance assessment systems. The research indicates that the integration of digital innovation, professional skills, and a robust commitment to public accountability is crucial for creating transparent and sustainable governance in police logistics

The transition toward sustainable energy systems requires the transformation of renewable energy marketing strategies, particularly for waste-based energy. This study aims to analyze the effects of green marketing, stakeholder collaboration, and technological innovation on the marketing effectiveness of waste-based energy, with customer satisfaction as an intervening variable. A quantitative approach with a survey design was used. Data were collected from 300 respondents, comprising consumers and stakeholders at PT PLN (Persero) ’s waste-based energy processing units. Structural Equation Modeling (SEM) was used to analyze the data. The results indicate that green marketing, stakeholder collaboration, and technological innovation significantly influence customer satisfaction and the effectiveness of marketing. Furthermore, customer satisfaction significantly mediates the relationship between independent variables and marketing effectiveness. These findings emphasize the importance of integrating sustainable marketing strategies, multi-stakeholder synergy, and innovative technologies to enhance the competitiveness of waste-based energy products in the market. This study contributes to the theoretical development of renewable energy marketing models and provides practical implications for policymaking and green energy marketing strategies in Indonesia.

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.

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.).

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.

The Role digital marketing nowadays become part important in today's business strategies implemented by MSMEs, which is enable MSMEs to​​ reach the target market with more broad , measurable , and efficient in global value chain (GVC) context . Digital marketing strategies do not  can denied  involving various elements, including optimization machine search engine optimization (SEO), marketing through social media, digital advertising, marketing content ,and also email marketing. In buniess process of PT Bungas food Nusatra abalikpapan tahy have product a weadang dayang.  In facing such a market dynamic, marketing strategy must capable adapt with current digital trends including Fear of Missing Out (FOMO) approach to create greater urgency and involvement​ tall in a way contextual relation with the value chain that creates consumer new abroad or​ marketing across countries by them. In this case, on of the challege ib export product process. The methods of research here, is survey and looking some literature that related in this subject discussion and research, the conclusion is  the With utilise trend this company​ can increase visibility brand , strengthen interaction with customers  and encourage conversion sale in a way more effective and This strategy expected can give impact positive to growth business and power competition companies in the digital era in more fluently in global value chain (GVC).

This study aims to analyse the implementation of the Village Operational Funds (VOF) distribution policy as an instrument for realising good governance principles in the Government of Sorong City. The research focuses on the implementation of fund distribution and the application of transparency, accountability, participation, and effectiveness in managing Village Operational Funds. A quantitative approach with a descriptive design was employed. Data were collected through questionnaires administered to 20 respondents and in-depth interviews with five key informants, including village officials and local government representatives. Additional data were obtained through observations of planning and fund utilisation processes, as well as through analysis of regulatory documentation and accountability reports. Data analysis was conducted through data reduction, data presentation, and conclusion drawing. The findings indicate that the implementation of the Village Operational Funds distribution policy in Sorong City has not been carried out in accordance with existing regulations, particularly regarding administrative procedures and fund disbursement mechanisms. This condition is primarily caused by the absence or non-disbursement of operational funds at the village level. Furthermore, the application of good governance principles has not been fully optimised due to limited human resources, weak supervision, and low community participation in planning and evaluation. The study concludes that Village Operational Funds have strategic potential as an instrument for promoting good governance if managed transparently and accountably. Therefore, strengthening institutional capacity, supervision systems, and community participation is essential to support effective village governance in Sorong City.

This study aims to analyse the role of digital literacy of the State Civil Apparatus (ASN) in supporting the implementation of smart cities and improving the quality of public services in the Sorong City Regional Secretariat. The research method used is qualitative with a descriptive approach, through in-depth interviews with structural officials within the Sorong City Secretariat. The results of the study show that the level of digital literacy of civil servants is still varied and greatly influenced by the duties and functions of the position, generational background, and the intensity of the use of digital technology in daily work. The implementation of the smart city concept in Sorong City has not been fully optimal due to limited infrastructure, readiness of human resources, budget support, and coordination between regional apparatus organisations. Nevertheless, digitalisation has had a positive impact on the efficiency of public services, especially in the aspects of administration, transparency, and ease of access to information for the public. This study also found that the success of digital governance is highly determined by the commitment of regional leaders, internal policies that support the digital capacity building of civil servants, and continuous training and mentoring. Therefore, strengthening the digital literacy of civil servants is a strategic and sustainable priority policy to support the development of smart cities and digital public services that are effective, inclusive, and accountable in Sorong City.

This study examines how the transition from manual to digital services influences user satisfaction through digital service quality and user perception at the Marine and Fisheries Office of Pekalongan Regency. The research employs a mixed-methods approach, combining a quantitative survey (N = 40) using Pearson correlation analysis with qualitative interviews involving service leaders and operators. The results reveal very strong correlations between service quality, user perception, and user satisfaction, as well as the critical role of system stability and operator assistance in shaping user experience. Qualitative findings confirm that changes in work culture, leadership, and human resource adaptation are key factors in the success of digitalisation. The study implies that the success of digital public services is determined not only by technology but also by strengthening human resource capacity, simplifying service design, and providing operator support for users with low digital literacy. The limitations of this study include the small sample size, the single organisational context, and reliance on perception-based data. Future research is recommended to conduct comparative cross-agency studies, longitudinal approaches, and structural model testing to examine the mediating roles of digital literacy and operator assistance.

This study aims to analyze the implementation of good governance principles in inclusive public services in Candisari District, Semarang City. The study used a descriptive qualitative approach with data collection techniques through questionnaires, interviews, and documentation. Research informants consisted of sub-district officials and community service users, including vulnerable groups. Data analysis was conducted through the stages of data reduction, data presentation, and conclusion drawing. The results of the study indicate that the implementation of good governance principles in Candisari District has begun, but has not been running optimally. The principle of transparency has been attempted through the provision of service information and the use of digital media, but accessibility of information for vulnerable groups is still limited. The principle of accountability has not been fully realized, as indicated by the less than optimal performance of the Women and Children Protection Task Force (Satgas PPA) in carrying out its duties and the less than optimal inclusive public complaint mechanism. From the aspect of participation, community involvement, especially vulnerable groups, in the planning and decision-making process is still limited and unstructured. In addition, legal certainty and regional security stability are relatively conducive, but still require institutional strengthening and synergy between stakeholders. This study concludes that the implementation of good governance in Candisari District still faces institutional, participation, and service accessibility constraints, so a strategy is needed to strengthen governance to realize inclusive and sustainable public services.