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Public health is an important indicator of the success of regional development and the quality of public services. Local governments play a strategic role in the provision of healthcare services through adaptive, effective, and community-oriented policies. This study aims to analyze local government policies in addressing public health issues through a literature review approach. The research method employed a literature study by examining scientific journals, laws and regulations, books, and policy documents related to regional health policies in Indonesia. The findings indicate that regional health policies are influenced by decentralization, fiscal capacity, the quality of governance, and inter-institutional synergy. Policy implementation continues to face several challenges, including limited budgets, low quality of healthcare human resources, unequal access to healthcare services, and weak intersectoral coordination. However, public service innovation and the strengthening of good governance have proven effective in improving healthcare service delivery. This study emphasizes that the success of regional health policies is determined by government commitment, community participation, integration between central and local policies, institutional strengthening, budget optimization, and service innovations based on community needs.

In the digital era, e-commerce has developed not only as a platform for buying and selling activities but also as a strategic medium for marketing and business development. University students theoretically possess strong potential to become edupreneurs because they are closely connected to technology and supported by higher education environments. Nevertheless, the reality at Al-Amien Prenduan University indicates that students tend to act more as consumers in e-commerce rather than as entrepreneurs. This condition creates a gap between theoretical expectations and practical implementation, raising questions regarding the effectiveness of e-commerce in encouraging students’ entrepreneurial spirit. This study examines strategies for strengthening Edupreneurship graduates through the optimization of e-commerce. The research specifically explores ways to maximize e-commerce in supporting the development of Edupreneurship graduate profiles and improving graduates’ competitiveness in entrepreneurship and employment sectors. A qualitative approach with a case study method was employed at Al-Amien Prenduan University. Data were collected through observation, interviews, and documentation involving female students of the Islamic Education Edupreneurship program who actively engage in entrepreneurial activities through e-commerce platforms. The findings reveal that optimizing e-commerce can be achieved through identifying students’ digital competencies and needs, as well as utilizing flexible technologies that support broader market access and global transactions. In addition, strategies such as enhancing digital marketing creativity, strengthening market research capabilities, and understanding consumer behavior significantly contribute to entrepreneurial development. The study concludes that e-commerce effectively fosters entrepreneurial attitudes, adaptability, and competitiveness among students in facing the challenges of the digital economy.

The ICU is one of the largest clinical cost centers in hospitals due to its high demand for human resources, medical equipment, and operational costs. This situation emphasizes that intensive care unit efficiency is a multidimensional issue that cannot be assessed solely from a single aspect. This study aims to analyze the efficiency of the intensive care unit at Ciremai Hospital, Cirebon City, using the Data Envelopment Analysis (DEA) model to determine resource optimization. Analytical quantitative study with a cross-sectional design. The population in this study consisted of personnel in the ICU of Ciremai Hospital from 2023 to 2025, with total sampling applied. The input variables included the number of physicians, nurses, beds, monitors, and ventilators, while the output variables consisted of BOR, LOS, and recovered patients. The ICU input variables remained stable during the study period, while output variables fluctuated with a declining trend in BOR and LOS. The results of the Data Envelopment Analysis (DEA) indicated that all study periods achieved an efficiency score of 1.00 (efficient), with a slack value of 0.00 across all input variables.

This review article evaluates the physical characteristics and stability of Oil in Water (O/W) and Water in Oil (W/O) creams through a comparative literature study published between 2021 and 2026. The evaluation focuses on important parameters, including viscosity, spreadability, adhesion, pH, and accelerated stability testing. The findings reveal that O/W creams generally have lower viscosity and greater spreadability, making them more suitable for daily cosmetic and therapeutic applications due to their lighter texture and better aesthetic acceptance. In contrast, W/O creams demonstrate higher physical stability, stronger adhesion, and superior occlusive properties, which are beneficial for protecting sensitive active ingredients and maintaining skin hydration over longer periods. The differences in performance are strongly influenced by the optimization of emulsifier Hydrophilic-Lipophilic Balance (HLB) values and the ratio between oil and water phases. Both cream types show good biocompatibility with skin pH ranging from 4.5 to 6.5, indicating their safety and effectiveness for topical pharmaceutical and cosmetic formulations.

Effervescent granules are a pharmaceutical dosage form that offer advantages in terms of ease of use, improved palatability, and rapid solubility in water. This study aimed to formulate and evaluate the physical properties of vitamin C Effervescent granules using the dry granulation method. The main materials used included ascorbic acid as the active substance, citric acid and tartaric acid as acid sources, and sodium bicarbonate as the base source. Evaluation was carried out on organoleptic properties, flow properties, pH, angle of repose, moisture content, density, compressibility index, and dispersion time. The results showed that the granules met the requirements for organoleptic properties, pH (4–5), angle of repose (38.41°), compressibility index (12.3%), and dissolution time (49.62 seconds). However, the flow property (29.11 seconds) and moisture content (6.47%) did not meet the established standards. These findings indicate that the formulated granules met most of the evaluation parameters but still require optimization, particularly in the drying process and flow properties, to obtain a more stable and high-quality dosage form.

Sweet orange peel (Citrus x aurantium L.) is an agricultural by-product rich in bioactive compounds including flavonoids, phenolics, terpenoids, and vitamin C with antioxidant and moisturizing potential. This study aimed to formulate and evaluate a body lotion using 15% ethanol extract of sweet orange peel obtained by maceration with 96% ethanol. Evaluations included organoleptic, homogeneity, pH, adhesion, spreadability, viscosity, irritation, cycling test, cream type, and DPPH antioxidant activity assessments. The preparation was semisolid, yellow, with a characteristic herbal aroma, homogeneous, pH 8, adhesion time of 4.10 seconds, spreadability of 9.9–11.1 cm, and acceptable viscosity. The preparation caused no skin irritation, remained stable through six cycling test cycles, and formed an oil-in-water (O/W) emulsion. Antioxidant activity showed an IC₅₀ of 284.6 ppm (weak category) compared to vitamin C as positive control (IC₅₀ 4.2 ppm). It was concluded that ethanol extract of sweet orange peel can be formulated into a stable and safe body lotion, though further optimization is needed to enhance its antioxidant activity.

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 community engagement program addresses the limited digital marketing utilization of Be Bloomy Bouquets, a creative knitting business, within the MBKM Entrepreneurship framework. The program aims to design and implement adaptive digital marketing strategies to enhance brand visibility, competitiveness, and business sustainability. Using a Participatory Action Research (PAR) approach, the program was conducted from October to November 2025 through preparation, implementation, and evaluation stages. The results indicate that digital marketing assistance via Instagram and TikTok significantly increased audience reach, customer interaction, and brand awareness, while marketplace utilization still requires further optimization. The discussion highlights that integrating handmade product creativity, digital branding strategies, and participatory reflection fosters entrepreneurial behavior change and strengthens local leadership capacity. In conclusion, the program generates not only technical improvements in marketing performance but also social transformation through enhanced digital literacy, independence, and local leadership. Future recommendations emphasize content consistency, production management, and sustainable digital marketing strategies.

This study aims to evaluate the Quran Tahfidz program at Bahrul Hikmah Islamic Junior High School using the Context, Input, Process, and Product (CIPP) evaluation model introduced by Stufflebeam. The findings of the research are as follows: First, the evaluation of the context aspect indicates that the program objectives have been comprehensively formulated. Data analysis confirms that the program implementation is well-aligned with the established goals, placing it in the 'good' category. Second, the evaluation of the input component reveals that the managerial competence of the administrators and the pedagogical capabilities of the educators have met the expected standards. Nevertheless, the infrastructure aspects require further optimization to foster a more conducive learning environment for students. Third, regarding the process aspect, the program implementation has generally achieved the targeted outcomes. However, observations identified obstacles predominantly stemming from students' internal factors; this implies the crucial role of teachers in integrating motivational stimuli as a preventive measure against declining learning enthusiasm. Fourth, the product evaluation shows that the daily memorization targets are generally achieved, although some students require remedial sessions. Ultimately, students are capable of completing the memorization of three juz within the three-year period.

This study aims to analyze the effect of competency synergy and training on performance with job satisfaction both at the Department of Women's Empowerment and Child Protection and Population Control and Family Planning of Pekalongan Regency directly and indirectly. The population used is the Department of Women's Empowerment and Child Protection and Population Control and Family Planning of Pekalongan Regency 160 people, with a sampling technique using the Slovin formula totaling 114 respondents. The data collection method was carried out by means of a questionnaire, the analysis technique used was PLS (Partial Least Square). The results of the study showed that competency synergy had a positive and significant effect on performance optimization and training had a positive and significant effect on performance optimization. Competency synergy had a positive and significant effect on job satisfaction, training had a positive and significant effect on job satisfaction, job satisfaction had a positive and significant effect on performance optimization. And job satisfaction can intervene the effect of competency synergy on performance optimization and job satisfaction can intervene the effect of technology utilization on performance optimization

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.

Heart disease is the leading cause of death in Indonesia and globally, necessitating an early screening system that is both accurate and clinically trustworthy. Although XGBoost demonstrates high predictive performance, its black-box nature undermines clinical trust, while low recall risks missed diagnosis an unacceptable consequence in population screening, especially in middle-income countries with limited healthcare resources. This study aims to develop a sensitive, transparent, and implementation-ready heart disease screening framework through the integration of SHAP-based Explainable AI. The CDC's Indicators of Heart Disease dataset (319,795 samples) was processed according to WHO/CDC standards, followed by class imbalance handling, hyperparameter optimization using RandomizedSearchCV, evaluation based on metrics sensitive to minority classes (AUC, recall, F1-score, AUC-PR), and threshold tuning to maximize recall. The baseline model showed a very low recall of 12.18%. After optimization and threshold tuning at 0.10, the model achieved recall >96% (96.79%) with a G-mean of 0.7477, supported by SHAP interpretation stability and the ability to capture non-linear interactions between advanced age (AgeCategory_WHO) and poor general health (GenHealth). SHAP analysis confirmed the alignment of dominant features with medical evidence, and its visualizations provide transparent explanations for healthcare professionals indicating its potential implementation as an interpretable clinical decision support system.

This research investigates intelligent cooling system design for main ship engines operating in tropical waters, integrating advanced machinery engineering with human factors to address thermal management challenges affecting engine performance, reliability, and crew operational effectiveness. Tropical maritime environments impose severe cooling demands through elevated seawater temperatures (28-32°C), high ambient conditions (28-35°C), and accelerated biofouling, reducing conventional cooling system effectiveness by 15-25% while increasing maintenance burdens and operational risks. Through qualitative analysis involving marine engineers, chief engineers with tropical operational experience, cooling system manufacturers, naval architects, automation specialists, and maritime training institutions, this study examines how intelligent cooling systems incorporating variable-speed pumps, adaptive control algorithms, predictive maintenance, and crew-centered interfaces can optimize thermal management while supporting effective human-machine collaboration. Results demonstrate that intelligent systems can reduce cooling energy consumption by 20-35%, improve temperature stability by 50-65%, extend maintenance intervals by 40-80%, and enhance crew situational awareness through intuitive monitoring interfaces, while requiring comprehensive training programs developing technical understanding and operational competencies. Key implementation challenges include control system complexity, sensor reliability in harsh marine environments, integration with existing engine management platforms, crew competency development requirements, and lifecycle cost justification. Findings reveal that successful intelligent cooling system implementation requires holistic sociotechnical approach addressing machinery engineering optimization, automation technology deployment, and human capability development through coordinated design and training strategies. This research contributes to marine engineering literature by providing integrated frameworks for intelligent system design incorporating machinery performance, automation capabilities, and human factors supporting operational excellence in tropical maritime operations.

This study is based on the need to develop a more effective concentrate ration for lactating dairy cows, as existing formulations in the field are greatly influenced by the availability of ingredients and varying quality. Therefore, this study focuses on optimizing concentrate in dairy cow feed rations to meet SNI standards, which include crude protein (CP), Total Digestible Nutrients (TDN), Calcium (Ca), and Phosphorus (P), with more efficient results in terms of price and nutrition. This study uses the Whale Optimization Algorithm (WOA) metaheuristic approach, which balances the exploration and exploitation processes in finding the best solution to optimization problems. This algorithm has fewer parameters than other metaheuristics such as GA, PSO, and DE. WOA runs naturally in continuous space without the need for genetic operators such as crossover and mutation. The dataset used contains types of dairy cow feed ingredients along with nutritional requirements and prices so that researchers can process the data into efficient feed concentrate that is suitable for lactating dairy cows.

The stability of disposal slopes is a critical aspect of open-pit mining operations because it directly affects operational safety and the continuity of overburden dumping activities. Disposal areas composed of overburden materials generally exhibit heterogeneous characteristics, particularly when soft materials such as mud are present, which can significantly reduce slope stability. Therefore, a comprehensive slope stability evaluation is required prior to further disposal development. This study aims to assess the stability condition of a disposal slope under initial conditions, evaluate the influence of material conditions, and analyze the effectiveness of counterweight application in improving both the safety factor and disposal capacity. The research methodology involved the collection of primary and secondary data, including slope geometry, lithological conditions, and the physical and mechanical properties of disposal materials obtained from laboratory testing and company technical data. Slope stability analysis was performed using the limit equilibrium method with the assistance of geotechnical software, taking into account groundwater conditions and operational loading. The analysis results indicate that the initial disposal condition yielded a safety factor of 0.718, indicating an unstable slope condition. After simulating the removal of mud material, the safety factor increased to 0.907 but remained below acceptable stability criteria. The application of a counterweight significantly improved slope stability, resulting in a safety factor of 1.498. Further optimization through slope geometry redesign produced a final safety factor of 1.101, which satisfies the requirements stipulated in KEPMEN ESDM No. 1827 K/30/MEM/2018. Additionally, the redesign increased the disposal capacity from 119,507,864.23 LCM to 119,682,378.22 LCM, representing an increase of 174,513.99 LCM. These results demonstrate that counterweight application combined with geometric optimization is effective in enhancing both slope stability and disposal capacity.

The increasing demand for housing in tropical regions requires building materials that are fast to apply, environmentally friendly, and resilient to extreme climate conditions as well as disaster risks. Conventional interlocking bricks are often chosen for their ease of construction, yet they still face challenges such as moisture and early cracking. This study proposes the innovation of the Hybrid Living Green Brick, a combination of lightweight bricks made from rice husk ash and fly ash waste (FRCB) with a biological layer of cyanobacteria. FRCB improves compressive strength by approximately 30% with the addition of 5% rice husk ash, achieving 65 kg/cm², thereby meeting Class 50 requirements (≥50 kg/cm²) according to SNI-15-2094-2000. The incorporation of 3% cyanobacteria provides an additional though not significant strength improvement, while still within the Class 50 category. It also reduces brick weight by 4.3%, with further optimization potential through cyanobacteria integration, and lowers carbon emissions from the firing process. Cyanobacteria induce the formation of CaCO₃ layers that seal pores, reduce water absorption by an average of 10%, and provide self-healing properties for microcracks. Preliminary observations indicate that FRCB offers stable mechanical performance, while biological activity was observed on the 7th day with the formation of pale-white mineral layers continuing until the 28th day. This hybrid innovation shows potential to support sustainable and disaster-resilient tropical construction by combining the mechanical strength of waste-based materials with the biological durability of cyanobacteria against extreme climates. Despite challenges related to moisture control and production standardization, the Hybrid Living Green Brick concept opens new pathways for developing environmentally friendly construction materials that are more adaptive to disaster-prone tropical conditions.

Malnutrition and stunting among children under five remain major public health challenges in developing countries, including Indonesia. These conditions are associated with increased morbidity, mortality, impaired cognitive development, and reduced productivity in adulthood. Stunting reflects chronic nutritional deficiencies, while severe malnutrition indicates acute or prolonged inadequate nutrient intake, often exacerbated by infections, poor caregiving practices, and unfavorable socioeconomic conditions. This case report aims to describe the clinical condition, contributing factors, management, and outcomes of a 14-month-old female toddler diagnosed with severe malnutrition and stunting without complications in Blang Dalam Geuteung Village, Nisam Subdistrict, North Aceh Regency. The assessment was conducted using a family medicine approach, incorporating detailed history taking, physical examination, anthropometric measurements, and nutritional status evaluation based on World Health Organization Z-score indicators. Interventions included nutritional rehabilitation through appropriate dietary intake, family education on balanced nutrition and proper feeding practices, routine growth monitoring, and optimization of primary healthcare services. The results showed gradual improvement in the child’s nutritional status, evidenced by weight gain, better appetite, and improved feeding patterns following the intervention. This case highlights the importance of early detection, comprehensive family-centered management, and continuous monitoring in addressing malnutrition and stunting at the primary care level. Strengthening community-based interventions, improving caregiver knowledge, and enhancing collaboration between healthcare providers and families are essential to reduce the burden of malnutrition and support optimal child growth and development.

This study aims to analyze the financial performance of the East Java Provincial Government based on the Budget Realization Report (LRA) for the 2022–2024 period. This study is important to assess the effectiveness of revenue achievement and the efficiency of regional expenditure management. The research method used is quantitative descriptive with secondary data in the form of Budget Realization Reports obtained through documentation techniques. Data analysis was performed using financial ratio analysis, namely effectiveness and efficiency ratios. The results show that the revenue performance of the East Java Provincial Government is in the highly effective category, as reflected in the realization of revenue that was able to reach and even exceed the budget target. However, regional expenditure performance is still considered inefficient because the realization of expenditure is close to the set budget. The conclusion of this study shows that even though revenue optimization has been achieved, the local government still needs to improve expenditure control so that regional financial management becomes more efficient in the future.

This study aims to measure the financial performance of the Government of West Java Province during the 2020–2024 period based on the analysis of the audited Budget Realization Reports. The research uses a descriptive quantitative approach with secondary data obtained from the Regional Financial Statements. Financial performance is measured through several ratios, namely the effectiveness ratio of Regional Original Revenue, regional financial efficiency ratio, regional financial independence ratio, and expenditure harmony ratio. The results show that the effectiveness of Regional Original Revenue fluctuated, with effective performance only in 2022 and 2024, while in other years it was categorized as ineffective. The efficiency ratio also indicated inconsistency, where inefficiency occurred in 2020, 2021, and 2023, and efficiency was achieved in 2022 and 2024. The regional financial independence ratio showed a relatively high level, reflecting low dependence on central government transfers, although there was a slight decline in 2023–2024. The expenditure harmony ratio indicated that budget allocation was still dominated by operational expenditure compared to capital expenditure. The findings imply the need for improving revenue optimization and more balanced expenditure allocation to support sustainable regional development.