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The Papua Special Autonomy Fund (Dana Otonomi Khusus Papua) represents a key instrument of Indonesia’s asymmetric fiscal decentralization aimed at reducing historical inequalities, accelerating regional development, and promoting social justice for Indigenous Papuans. However, after more than two decades of implementation, concerns persist regarding its effectiveness in producing equitable welfare outcomes, particularly with respect to accountability, targeting accuracy, and distributive justice. This literature review critically examines existing scholarly research on the governance, implementation, and impacts of Dana Otsus Papua, with an emphasis on how institutional arrangements shape policy performance and equity outcomes. The study employs a narrative–critical literature review enriched with systematic elements, including transparent search procedures, explicit inclusion and exclusion criteria, and thematic synthesis. Peer-reviewed journal articles and reputable conference proceedings were analyzed using thematic analysis and conceptual mapping to identify dominant findings, methodological approaches, and research gaps. The synthesis reveals recurring patterns across the literature. Accountability mechanisms remain fragmented and weakly integrated across planning, budgeting, monitoring, and evaluation processes. Targeting accuracy is inconsistent, with fiscal benefits frequently failing to reach Indigenous Papuans as intended. Moreover, distributive justice outcomes depend more on institutional recognition, participation, and governance capacity than on the size of fiscal transfers alone. The review also highlights a critical gap in integrative evaluations that link governance arrangements, implementation processes, and equity outcomes. The article concludes that improving Dana Otsus Papua requires a shift from expenditure-focused assessments toward governance- and justice-oriented evaluation frameworks. The study contributes theoretically by integrating accountability, implementation, and distributive justice perspectives, and offers practical insights for strengthening oversight, refining targeting mechanisms, enhancing participatory governance, and embedding digital tools within accountability systems.

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.

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).

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 aimed to analyze the effect of parent empowerment in optimizing tummy time on head control in infants aged 0–6 months at community health posts in Jatisampurna, Bekasi. Early motor development requires adequate sensory–motor stimulation, yet many parents have limited knowledge and practice of prone positioning activities. A quasi-experimental one-group pretest–posttest design was employed involving nine healthy infants selected through purposive sampling. Parents received education and practical training on tummy time and were instructed to implement a home program for two weeks with a minimum duration of 30 minutes per day. Head control was assessed before and after the intervention using the Head Control Scale across prone, supine, pull-to-sit, and supported sitting positions. The results demonstrated improvements in head control scores in all positions, with the most notable gains observed in infants who had lower baseline abilities. The findings indicate that repeated prone stimulation enhances postural control through increased neck and trunk muscle activation and improved sensory–motor integration. Parent empowerment also increased knowledge, adherence to home stimulation, and active participation in child development monitoring. This community-based intervention shows potential as a promotive and preventive strategy to support early motor development through family-centered approaches at primary health services.

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in economic development; however, their competitiveness is often constrained by weak branding and limited digital marketing capabilities. This community engagement program aimed to strengthen brand identity and digital capacity of the “Pukle” catfish cracker MSME in Karangsuwung Village, Cirebon Regency, through an integrated branding and sharia-based digital marketing approach. A participatory–collaborative method was employed, consisting of needs assessment, Workshops, technical mentoring, and monitoring and evaluation. The interventions included the development of brand identity elements (logo, tagline, and visual guidelines), improvement of informative packaging, activation and optimization of WhatsApp Business, and utilization of TikTok Shop as a social commerce channel. In addition, a standard operating procedure for sharia-compliant digital content based on the principles of shiddiq (truthfulness), amanah (trustworthiness), fathanah (competence), and tabligh (communicativeness) was formulated to enhance consumer trust. The results indicate improvements in product professionalism, digital channel management, content consistency, and customer interaction. The integration of branding and sharia digital marketing proved relevant in building differentiation, expanding market access, and strengthening long-term business reputation. The program highlights that MSME digital transformation requires capacity building, continuous mentoring, and ethical value integration to ensure sustainable competitiveness.

This study examines the juridical aspects of guardianship applications by parents to obtain permission to sell a minor’s inherited land and analyzes the judicial considerations in Decision Number 199/Pdt.P/2025/PN Blt. The research employs an empirical juridical method with a sociological legal approach. Primary data were collected through interviews and case documents at the Blitar District Court, while secondary data consist of statutes, doctrine, and related literature. Qualitative-descriptive analysis was applied to interpret the findings. The study reveals that the guardianship application process involves both administrative and judicial stages. Judges scrutinize material evidence and the probity of sale objectives, weighing important principles such as utility, legal certainty, fairness, and justice. Guardians are granted limited authority to sell a minor’s property only if it can be proven to be in the child's best interest and legal protections are assured. Recommendations include strengthening post-decision monitoring, enhancing legal outreach, improving procedural transparency, and ensuring comprehensive implementation.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.

Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.

Type 2 diabetes mellitus in the elderly significantly impacts quality of life (QoL) due to physiological changes in aging, comorbidities,and the risk of chronic complications. Nutritional management is considered a key intervention to improve glycemic control and functional status in the elderly.Objective: To review the current evidence regarding the effect of nutritional interventions on the quality of life in elderly with type 2 diabetes mellitus through a Literature Review approach.Methods: The Literature Review was conducted by identifying articles from the Scopus database published in 2020–2025. The review process included screening based on inclusion-exclusion criteria, eligibility assessment, and narrative synthesis of relevant studies,including controlled trials,quasi-experimental studies, observational studies, and systematic observations.Results: From the selected studies, interventions with a low glycemic index diet and a balanced diet have been shown to reduce HbA1c levels and the risk of complications; a multicomponent program combining diet and exercise produces synergistic effects on glucose control, physical function,and quality of life.Structured nutrition education (DSME) and family involvement improve satisfaction and metabolic outcomes.The use of technology (mobile apps) shows potential in monitoring intake and glucose,although digital literacy challenges and limited sample size and study design are often limitations.Conclusion: Targeted nutrition interventions, especially when combined with exercise, education,family support,and age-friendly technology,have the potential to improve metabolic control and quality of life in older adults with T2DM.Recommendation: Large-scale RCTs and more robust longitudinal studies are needed to establish the long-term effectiveness and cost-effectiveness of multicomponent nutrition interventions and technology adaptations for older adults with multimorbidity.

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.

The management of kWh meter replacement data at PLN ULP Ampera Palembang is still largely handled through manual recording, which often causes data inconsistencies and delays in monitoring activities. This study aims to design an information system that supports the monitoring of kWh meter replacement data based on actual user needs. The research applies a descriptive qualitative method using the User Centered Design (UCD) approach, where users are actively involved throughout the design process. The stages include understanding the work context, identifying user requirements, developing system design solutions, and evaluating the proposed design. The outcome of this research is a kWh meter data monitoring system design that is expected to facilitate data management, improve accuracy, and support more efficient monitoring processes.

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 motivated by the importance of systematic evaluation in ensuring the quality and effectiveness of the implementation of educational programs, particularly the Al-Qur'an Halaqah Program as a means of developing the ability to read and memorize the Al-Qur'an and shaping the religious character of female students. This study aims to identify the gap between the established standards and the actual implementation of the program using the Discrepancy Evaluation Model (DEM). The approach used is qualitative evaluative with five stages of evaluation, namely design, installation, process, product, and comparison. Data was obtained through interviews, observations, and documentation studies, then analyzed through the stages of data collection, processing, presentation, and conclusion drawing using triangulation techniques. The results showed that the design and installation stages met the standards, while in the process stage, a gap was found in the consistency of female student attendance. In the product stage, the success rate reached 93.48%, but there was still an imbalance in memorization achievement. The implication is that the program should be continued with strengthened attendance monitoring and individual mentoring strategies to improve the equity of outcomes and the sustainability of the program.

This study aims to examine the role of Computer-Assisted Audit Techniques (CAATs) in evaluating internal control within accounting information systems (AIS) to detect fraud in the expenditure cycle. The research employs a literature review method by analyzing five relevant studies selected based on publication criteria within the last ten years and a focus on technology-based auditing, internal control, and fraud. The findings indicate that CAATs, through features such as test data and parallel simulation, are effective in identifying system weaknesses, detecting transaction anomalies, and strengthening controls in the expenditure cycle. Fraud in this cycle is commonly caused by weak authorization, incomplete documentation, and expenditures conducted without proper procedures. CAATs address these challenges through data-driven and automated audit approaches. In conclusion, CAATs represent a strategic solution for enhancing monitoring accuracy, preventing fraud, and supporting organizational transparency and accountability in the digital era.

Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.

The development of automation and robotics technology has driven innovation in various industrial fields, particularly in automatic sorting systems. Manual sorting processes often lead to inefficiencies and human errors, creating the need for an automatic, fast, and accurate system. This research employs a qualitative method which includes experimentation, testing, and system documentation. The system is designed as a robotic arm for sorting objects based on color, utilizing a TCS3200 color sensor and an ESP32 microcontroller. An ultrasonic sensor detects the presence of objects, while the sorting results are displayed through a real-time web monitoring system. The test results show that the prototype successfully sorts four primary colors (red, green, blue, and yellow) with a high level of accuracy. This research is expected to serve as a reference for the development of automation systems and robotics learning tools in both educational and industrial applications. In addition, this research also contributes to the development of technology that can increase efficiency and accuracy in industrial production processes and provide more environmentally friendly solutions by reducing the need for manual labor.

Low levels of maternal knowledge and appropriate nutritional practices, combined with limited continuity of nutrition education from health workers, remain significant risk factors for maternal and child health problems, including stunting. The GEMPITA Program (Gerakan Edukasi MPASI Tepat dan Aman / Safe and Appropriate Complementary Feeding Education Movement) was implemented as a community service initiative to improve nutritional knowledge, skills, and practices among families in the working area of Teratak Public Health Center. The program employed structured methods, including socialization, education, training, demonstrations, mentoring, and non-formal monitoring and evaluation. The target participants were mothers with children aged 6–24 months, as well as health workers and community health cadres. Program effectiveness was assessed using pretest posttest measurements and direct observation of behavioral changes. The results demonstrated a significant improvement in maternal knowledge regarding appropriate complementary feeding, enhanced skills in age-appropriate food preparation, and improved hygienic and nutritious feeding practices. In addition, the capacity of health workers to deliver nutrition education was strengthened. Overall, the GEMPITA Program proved effective in improving family nutrition practices and reinforcing the role of the public health center in stunting prevention through a sustainable community empowerment approach.

This study analyzes the strategy of the Community Monitoring Group (Pokmaswas) in developing mangrove forests in Pelambung Village, Tanjung Balai Karimun, Riau Islands. This study aims to explore Pokmaswas management patterns and their impacts on local communities and coastal ecosystems. Mangrove forests play a crucial role in maintaining ecosystem balance, protecting coasts from abrasion, flooding, and storms, and supporting the biodiversity of fish, shrimp, and crabs. In Indonesia, mangrove area has decreased drastically from 5.2 million hectares in 1982 to around 2.5 million hectares due to land conversion, logging, and pollution. In Pelambung Village, Pokmaswas, established in 2023, began rehabilitation by planting 5,000 mangrove seedlings such as Rhizophora (mangrove), Avicennia (api-api), and Sonneratia. This qualitative, descriptive study, using observation, local community interviews, and documentation techniques, was conducted from July to August 2024. The Community Development Group (Pokmaswas) is active in nursery, harvesting, maintenance, monitoring, and ecotourism development, generating economic benefits (job creation, mangrove coffee MSMEs and miniature canoes), ecological benefits (protection from abrasion, increased fisherman's catch), and biological benefits (marine biota habitat). Challenges include limited funding, pest attacks, and integration with regional development.    

Hand washing is a major pillar in clean and healthy living behaviors (PHBS). However, there are still children who do not understand the correct handwashing technique so that germ cleaning is not optimal. This community service activity aims to improve students' knowledge and motor skills in practicing the six steps of hand washing according to World Health Organization (WHO) standards. The method used in this activity is interactive counseling combined with live demonstrations. The stages of the activity include providing material through audiovisual media (video/poster), direct demonstrations by instructors, and independent practice by all participants. In addition, a question and answer session was held to ensure student understanding and corrective feedback on inappropriate movements. Evaluation instruments in the form of observation sheets were used to assess the accuracy of the sequence, duration, and coverage of the hand area when washing. The result of this activity was the success of all participants in practicing the six-step sequence of handwashing correctly independently. The involvement of teachers and school health workers also strengthens the sustainability of this practice through regular monitoring. Through this education, it is hoped that early awareness will arise which has become a routine habit to reduce the risk of disease transmission in the school and home environment and support the creation of a culture of clean living from an early age.