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This study aims to provide a comprehensive analysis of Predictive Policing and its implications for law enforcement transformation in Indonesia, based on an extensive review of its global applications, benefits, and challenges. The study uses qualitative literature and international case study review methods to assess the impact and complexity of implementing digital technologies such as artificial intelligence (AI), machine learning, and big data analytics within a Predictive Policing framework. The results of this review highlight that while Predictive Policing offers significant potential for proactive crime prevention and increased operational efficiency, its implementation is consistently fraught with critical legal, ethical, and technical challenges, including regulatory gaps, risks of algorithmic bias, and data privacy concerns, which are particularly relevant to Indonesia. The findings underscore that public trust and police legitimacy in the context of adopting such technologies are strongly influenced by transparency, strong accountability mechanisms, and community involvement in shaping their use. This study contributes to the growing discourse on digital policing in developing countries and culminates in practical policy recommendations designed to guide the Indonesian police towards the development and implementation of Predictive Policing models that are effective, efficient, and fundamentally respectful of legal and human rights principles.

This meta-analytic study aims to comprehensively examine the relationship between mental health, learning capacity among health education students, and competencies in nosocomial disease risk management through cross-contextual empirical synthesis. An analysis of 47 studies involving 12,847 participants from 15 countries demonstrates a strong, statistically significant association between students' mental health and competencies in nosocomial infection prevention, as reflected by a correlation coefficient of r=0.68 (p<0.001) and a 95% confidence interval of 0.61-0.74. Students with high mental health scores (M=78.4; SD=8.2) exhibited substantially superior understanding of infection prevention protocols, namely 43% higher than the control group (M=54.7; SD=12.1; t(846)=18.42; p<0.001; d=2.31). Structural equation modeling confirmed learning capacity as a significant partial mediator (β=0.52; p<0.001), with an indirect effect reaching 35.4% and a 95% CI range of 28.6-42.1%. Mindfulness-based psychoeducational interventions were shown to enhance nosocomial risk identification abilities by 38.7% (F(2,564)=42.18; p<0.001; η²=0.41) while reducing clinical anxiety by 31.2% (t(382)=9.84; p<0.001). These findings extend the frameworks proposed by Song (2024) and Schutte et al. (2025), which primarily emphasize cognitive aspects, by demonstrating that the integration of psychological dimensions yields a multidimensional predictive model explaining 64.3% of the variance in risk management competence (R²=0.643; F(5,841)=304.76; p<0.001), surpassing conventional models that account for only 38-45% of the variance.

William Shakespeare’s Romeo and Juliet portrays deep reflections on human nature and moral values, emphasizing the importance of dignity, freedom, and emotional connection in human life. The tragedy explores the conflict between personal feelings and social constraints, revealing how human ideals often clash with rigid traditions and authority. Through the struggles of Romeo and Juliet, Shakespeare presents the essence of humanity love, empathy, and the pursuit of understanding amid division and hostility. This study aims to analyze the values of humanism in William Shakespeare’s tragedy Romeo and Juliet. The research focuses on how humanist ideals such as dignity, freedom, solidarity, and communicative rationality are reflected through the characters’ dialogues, decisions, and relationships. Using a qualitative descriptive method, the analysis applies Hardiman’s theory of humanism, supported by references from academic books and journals. The findings reveal six categories of humanism values: appreciating opinions (20%), cooperation (17%), sacrifice (23%), caring for others (13%), helping (13%), and solidarity (14%). Among these, sacrifice appears as the most dominant value, especially reflected in the willingness of Romeo and Juliet to risk everything for love and moral ideals. These findings indicate that Romeo and Juliet embodies humanist values that emphasize empathy, moral responsibility, and the importance of understanding in overcoming social barriers. Although the story ends in tragedy, the play highlights that love, empathy, and dialogue are essential for building a humane society. Therefore, Romeo and Juliet is not only a timeless love story but also a literary work that reflects enduring humanist ideals relevant to modern life.

Type 2 Diabetes Mellitus is a chronic metabolic disease characterized by elevated blood glucose levels due to impaired insulin action. Blood glucose control is influenced by various factors, one of which is dietary patterns. Diets that do not adhere to balanced nutrition principles can lead to poor glycemic control and increase the risk of complications. This study employed a quantitative design with a cross-sectional approach. The research sample consisted of 95 patients with Type 2 Diabetes Mellitus at Sari Asih Karawaci Hospital in 2025, selected using purposive sampling techniques. Dietary pattern data were collected using a questionnaire, while blood glucose level data were obtained from medical examination results. Data analysis was conducted using univariate and bivariate analysis with the Contingency Coefficient test. The results showed that most respondents had good dietary patterns (58.9%), and blood glucose levels within the normal category (51.6%). Bivariate analysis using the Contingency Coefficient test indicated a p-value of 0.005, demonstrating a significant relationship between dietary patterns and blood glucose levels in patients with Type 2 Diabetes Mellitus. There is a significant relationship between dietary patterns and blood glucose levels in patients with Type 2 Diabetes Mellitus. Proper dietary management is an important factor in controlling blood glucose levels and should be a primary focus in nursing care and health education for patients with Type 2 Diabetes Mellitus.

Artificial Intelligence (AI) is increasingly recognized as a transformative enabler of early disease detection, with the potential to improve diagnostic accuracy, support predictive risk stratification, and advance preventive healthcare. Despite rapid methodological progress, many existing reviews remain performance-centric, offering limited insight into generalizability, ethical governance, and real-world implementation constraints. This paper presents a narrative and integrative review with an adoption-focused, translational perspective, synthesizing recent developments in AI-driven early disease detection across oncology, cardiology, neurology, and infectious disease surveillance. Drawing on peer-reviewed literature published primarily between 2016 and 2025, the review examines reported performance gains alongside persistent limitations related to data heterogeneity, population bias, explainability, and regulatory fragmentation. Through cross-sectional synthesis, we identify three recurring gaps in prior reviews: (i) overgeneralization of AI’s diagnostic superiority, (ii) insufficient consideration of ethical and legal accountability, and (iii) a lack of actionable guidance for scalable clinical implementation. Integrating technical, ethical, and policy dimensions into a unified conceptual framework, this review demonstrates that while AI systems can consistently enhance diagnostic accuracy and early risk stratification in well-defined tasks, sustained clinical adoption depends on aligning technical performance with governance readiness, interpretability, and workflow integration. The analysis further highlights how implementation mechanisms—such as explainable AI, continuous post-deployment monitoring, and clinician-centered deployment strategies—mediate the translation of algorithmic innovation into real-world healthcare impact. Overall, this review provides a critical reference for researchers, clinicians, and policymakers seeking to translate AI innovation into safe, equitable, and trustworthy clinical practice.

This study evaluates the implementation of spot-check testing for road preservation works in Manado City, specifically on the Kairagi–Mapanget segment, the Manado City–Wori boundary, and the Liwas Terminal Access Road, to identify existing problems and assess the effectiveness of engineering-based mitigation efforts. The objective is to examine construction quality compliance with technical specifications and identify operational challenges encountered on site. The methodology encompasses the measurement of asphalt layer thickness and density via a core drill, the examination of contract documents, the analysis of laboratory test results, and the execution of field observations. The findings show that most samples meet the required standards, although several locations require corrective action. Challenges arise from weather conditions, heavy traffic, equipment limitations, and the need for adaptation to updated technical regulations. Operational mitigation strategies, staff training, the use of core drill technology, and digital documentation were found to enhance accuracy and efficiency in evaluation. The research demonstrates the value of stakeholder collaboration and capacity building in supporting quality control for road preservation works. A multidimensional approach is effective in resolving technical and operational complexities in urban road projects.

Early Warning Score (EWS) is early detection instrument for predicting severity or worsening of a patient's condition. Therefore, nurses' compliance with the assessment is crucial. The goal is to reduce the incidence of code blues in general care units. Code blue is a procedure code used for conditions where a victim is found to be at risk of cardiac arrest or respiratory arrest. Methods: This study is a quantitative analytical survey, a form of research that examines the effects and risk factors. The study design was cross-sectional, examining the dynamics of correlation between dependent and independent variables. The number of respondents in this study was 150, drawn from a total sampling of the nurse population in general care units. The data obtained were subjected to statistical analysis using a non-parametric Chi-Square test. Results: Respondents, based on their level of compliance with the EWSS monitoring form, were mostly in the compliant category (94%), and most had an impact on the absence of code blues (94%) in the emergency risk group. Conclusion: There is a significant relationship between nurses' compliance in monitoring the completion of the EWSS and the occurrence of code blue in inpatient rooms (P-Value <0.05).

This study comprehensively analyzes the impact of sectarian conflict on mental health in Kirkuk through an ethnoreligious meta-analysis of 42 studies with a total of N=15,427 participants published between 2003 and 2023. The quantitative synthesis indicates a PTSD prevalence of 47.3% (95% CI: 44.2–50.4) among victims of sectarian conflict, with an uneven distribution across groups, where the Turkmen minority records the highest prevalence at 56.8%, exceeding Arab Sunni at 43.2% and Kurdish at 41.5%. Logistic regression analysis demonstrates that direct exposure to sectarian violence increases the risk of major depression by 3.4 times (OR=3.42, p<.001), anxiety by 2.8 times (OR=2.83, p<.001), and psychosomatic disorders by 2.5 times (OR=2.54, p<.001). Further meta-regression identifies duration of conflict exposure (β=.426, p<.001) and intensity of sectarian violence (β=.389, p<.001) as significant predictors of symptom severity. In contrast to the findings of Cummings et al. (2013) and Eltally (2019), which emphasize the general impact of conflict, this study reveals specific intergenerational trauma patterns within distinct ethnoreligious communities, with the highest transmission observed among families of sectarian massacre victims (d=0.82), while simultaneously highlighting variations in communal resilience across groups as a differential determinant of post-conflict psychosocial dynamics.

Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly in resource-limited settings and among elderly populations, where timely diagnosis and continuous monitoring are often constrained by limited clinical infrastructure. This study presents an edge–cloud–integrated framework for early pneumonia risk monitoring, leveraging multimodal wearable sensors and deep learning to support continuous short-duration monitoring. The proposed system is designed to operate in near real time under simulated deployment conditions, continuously acquiring and analyzing physiological signals (respiratory rate, heart rate, SpO₂, and body temperature) alongside event-driven acoustic biomarkers (cough sounds) within a distributed architecture. A lightweight edge module performs local signal preprocessing and anomaly triage, selectively transmitting salient information to a cloud-based multimodal deep learning model for refined risk estimation and interpretability analysis. The framework was evaluated using a multi-source dataset comprising public repositories (MIMIC-III and Coswara) and a clinically supervised wearable study conducted in two Nigerian hospitals, resulting in 718  hours of quality-controlled multimodal monitoring data. In a pooled multi-source evaluation, the system achieved an AUC of 0.95, while in a clinically realistic local-only evaluation, the AUC was 0.86, reflecting a consistent but preliminary diagnostic signal. These results highlight the importance of local data adaptation for real-world applicability and suggest that multimodal AI can provide meaningful early risk indicators under resource constraints. Beyond predictive performance, this work demonstrates the feasibility of integrating multimodal learning, edge–cloud computation, and explainable analytics into a deployment-aware, privacy-preserving monitoring framework for low-resource healthcare environments.

Hypertension is a major modifiable risk factor contributing to the development of Acute Coronary Syndrome (ACS), which includes STEMI, NSTEMI, and unstable angina. The increasing prevalence of hypertension worldwide raises concern regarding its impact on cardiovascular outcomes. This study aimed to describe the profile of ACS patients with hypertension receiving angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) therapy in the Intensive Coronary Care Unit (ICCU) of RSUD Dr. Soetomo Surabaya. Using a descriptive cross-sectional method, data from 91 patients treated between July 2021 and October 2024 were analyzed. Variables included demographic characteristics, clinical classification of ACS, hypertension degree, comorbidities, types and doses of ACEI/ARB administered. The results showed that most patients were male (73%) and aged over 65 years (40%). Chi-square analysis revealed no significant relationship between hypertension degree, ACS classification, or most comorbidities with drug selection or dosage (p>0.05), except for a significant association between coronary heart disease comorbidity and ARB selection. These findings suggest that in hypertensive ACS patients, the choice between ACEI and ARB therapy is predominantly based on individual comorbidity profiles rather than blood pressure severity or ACS type. The study highlights the importance of personalized treatment approaches considering patient comorbidities to optimize cardiovascular outcomes.

Hydroquinone is a skin lightening agent that effectively inhibits melanogenesis, but its use in cosmetics is restricted due to the potential for serious side effects. Although banned in over-the-counter cosmetics, hydroquinone is still found in unregistered night cream products. This study aims to detect the presence, determine the levels of hydroquinone, and evaluate its safety risks in night cream products sold in District X, Sukoharjo Regency. The study was conducted experimentally in a laboratory using a purposive sampling technique on 15 night cream samples consisting of 8 BPOM-registered products and 7 non-BPOM products. Qualitative analysis was carried out using FeCl₃, Benedict's, and o-phenanthroline color reaction tests, while quantitative analysis used the HPLC method with a C18 column and a UV-Vis detector at a wavelength of 280 nm. The results showed that all BPOM-registered samples did not contain hydroquinone, while several non-BPOM samples were detected to contain hydroquinone at levels ranging from 0.0688%–0.2043%. These findings indicate a higher safety risk in non-BPOM products. The implications of this research emphasize the importance of strengthening regulatory oversight and increasing public literacy in choosing safe and legal cosmetic products.

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 study aims to: 1) describe the classical interpretation of QS. An-Nisa' [4]:9, particularly the concept of protecting descendants (khaufu 'ala al-dzurriyah); 2) explain the moral message of QS. An-Nisa' [4]:9 regarding parental responsibility through Abdullah Saeed's contextual hermeneutical framework; 3) demonstrate how a reinterpretation of QS. An-Nisa' [4]:9 can provide a new, more positive, and more balanced perspective on the childfree phenomenon in contemporary society. This research adopts a qualitative method grounded in library research. The data sources include classical and contemporary tafsir works, the writings of Abdullah Saeed, thematic literature on family studies, and academic references discussing the modern emergence of the childfree phenomenon. The analysis applies a contextual hermeneutical approach that emphasizes reading the Qur'anic verse through its historical setting, universal moral values, and relevance to contemporary social realities. This approach enables an integrated understanding between the fundamental message of the verse and present-day ethical challenges. The findings reveal that classical interpretations of QS. An-Nisa' [4]:9 focuses on the protection of orphans and the obligation to prevent descendants from falling into states of weakness. The moral message of the verse, viewed through Abdullah Saeed's framework, directs attention to the heavy moral responsibility of raising children, which requires comprehensive readiness. The reinterpretation of the verse highlights that the childfree choice does not inherently conflict with Islamic values, as this decision may arise from moral responsibility to avoid producing descendants who may be at risk of weakness, as warned in the verse. This perspective encourages society to view childfree choices more positively and aligns them with the Qur'anic objective of safeguarding the well-being and strength of future generations.

The use of cosmetics, especially day creams, is increasing along with the high public interest in facial skin care. However, day cream products are still found to potentially contain hydroquinone, a skin whitening agent whose use is restricted because it can cause harmful side effects on skin health. This study aims to identify the presence and determine the levels of hydroquinone in day cream products circulating in District X, Sukoharjo Regency, and to assess their compliance with the regulations of the Food and Drug Monitoring Agency (BPOM). This study was a descriptive analytical study using a purposive sampling technique. A total of 15 day cream products were analyzed, consisting of 8 BPOM-registered products and 7 products not registered with BPOM. Qualitative analysis was conducted using color reaction tests with FeCl₃, Benedict’s, and o-phenanthroline reagents. Furthermore, quantitative analysis was performed using the High Performance Liquid Chromatography (HPLC) method to accurately and specifically confirm the presence and determine the levels of hydroquinone. The results showed that the color reaction test has limitations in specifically identifying hydroquinone. HPLC confirmation revealed the presence of hydroquinone in several day cream samples, with concentrations ranging from 0.024% to 0.150%. These findings indicate the need for stricter monitoring of day cream distribution to ensure the safety of cosmetic products for the wider public.

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.

Background: Hand grip strength is an essential indicator of physical capacity and nutritional status that can identify obesity risk. Adipose tissue accumulation in central obesity contributes to decreased hand grip strength quality through impaired muscle force transmission. Previous studies showed inconsistent results regarding the relationship between waist circumference and hand grip strength, especially in populations with high physical activity. Objective: To analyze the relationship between waist circumference and hand grip strength in male construction workers with high physical activity in Medan City. Methods: This descriptive-analytic study with cross-sectional design involved 100 male construction workers aged 30-39 years selected using purposive sampling technique. Waist circumference was measured using measuring tape, while hand grip strength was measured using handgrip dynamometer. Data analysis used Chi-Square test with significance level α=0.05. Results: The majority of respondents had normal waist circumference (64%) with mode 70 cm and normal grip strength category (53%) with mode 41.7 kg. Chi-Square test showed significant relationship between waist circumference and hand grip strength (p=0.039). Conclusion: There is a significant relationship between waist circumference and hand grip strength in construction workers. High physical activity does not completely prevent central obesity, therefore nutritional management and health education programs are needed for optimization of body composition and muscle functional capacity.

A traffic accident is a social risk event that causes serious losses, either in the form of injury, loss of property, or death of a family member. The State provides protection through the Road Traffic Accident Fund as stipulated in Law Number 34 of 1964 and Government Regulation Number 18 of 1965. However, in practice, the regulation of the status of victims and the coverage of the heirs of the beneficiaries still raises juridical and social problems because they do not fully reflect substantive justice. This study aims to analyze the concept of victims and heirs in the applicable Road Traffic Accident Fund regulations, as well as formulate a more equitable reconstruction of the regulation. This research is normative legal research with a legislative, conceptual, and comparative approach. Analysis is carried out on positive legal norms and social realities that develop in society. The results of the study show that the definition of victim and the limitation of the coverage of heirs in the current positive law are narrow and administrative, so they are less responsive to the complexity of social relations and the economic dependence of the families of traffic accident victims. The mismatch between normative boundaries and social realities has implications for the non-achievement of the goals of social protection and substantive justice. Therefore, this study offers a reconstruction of the interpretation of the concept of victims and an expansion of the coverage of heirs that is more responsive, inclusive, and oriented towards social protection.

This study explores the influence of Electronic Word of Mouth (E-WOM) on Umrah ticket purchasing decisions through the Instagram account @umrohbarengyuk.id. In today’s digital era, social media serves as a primary channel in shaping consumer perceptions of services, particularly in religious travel industries like Umrah. E-WOM, consisting of interaction intensity, opinion valence, and content quality, plays a vital role in providing trustworthy information and building consumer confidence. Using a quantitative method involving surveys and simple linear regression analysis, the study finds that digital reviews and testimonials strongly influence prospective pilgrims’ decisions. The @umrohbarengyuk.id account is perceived as providing transparent, responsive, and relevant communication, making it a key reference in users’ information-seeking and service evaluation processes. These findings reinforce E-WOM as a powerful digital marketing communication strategy that not only increases service appeal but also enhances customer trust and purchasing confidence. The study offers practical insights for Umrah service providers in developing personalized, credible digital content and strategies to reach broader audiences effectively.

The rapid development of digital technology has led to an increased adoption of digital payment methods in online transaction-based businesses. However, in practice, failures and limitations in the implementation of digital payment systems still occur, potentially disrupting transaction processes and reducing customer convenience. Payment related obstacles may result in transaction cancellations and increase the risk of customer churn. This study aims to analyze the impact of failures and limitations in digital payment methods on customer churn using a classification-based approach. The data used in this research are secondary e-commerce customer data obtained from the Kaggle platform, including transaction information, payment methods, customer behavior, and historical transaction records. The research methodology consists of data preprocessing, time-based feature engineering, and classification modeling using logistic regression, decision tree, and random forest algorithms. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the decision tree model demonstrates superior capability in identifying churn customers compared to the other models, although it does not always achieve the highest accuracy. In addition to digital payment methods, other factors such as purchase value, transaction frequency, purchase timing patterns, and product return rates also influence customer churn. The findings highlight the importance of optimizing digital payment systems as part of customer experience enhancement strategies and customer retention efforts in online transaction–based businesses.

The interactive learning video innovation "Human Blood Circulation" through Edpuzzle, with embedded questions, is designed to address the difficulties faced by fifth-grade elementary school students in understanding abstract biological concepts. Science education in elementary schools often encounters challenges in explaining invisible processes, such as the heart's working mechanism and blood circulation, leading students to struggle with comprehension or merely memorize the material. Conventional learning videos tend to make students passive, necessitating media that can transform the learning process into an active one. Edpuzzle, as an educational platform, allows teachers to insert questions at critical points in the video, requiring students to answer before continuing to watch. This innovation aligns with the cognitive development stage of fifth-grade students, who are in the concrete operational stage.The research objectives include product development, validity and feasibility testing, analysis of conceptual understanding improvement, influence on focus and learning independence, and student responses. Media development refers to Mayer's cognitive theory of multimedia and the characteristics of interactive videos according to Heinich et al. Additionally, media development models such as ADDIE and Dick and Carey are discussed. The main benefits of using Edpuzzle include increasing engagement, facilitating real-time formative assessment, and enhancing learning motivation. However, challenges exist such as technology access, teacher readiness, the risk of technology dependency, and long-term evaluation. The practical implications can support digital learning and the transformation of elementary science education.