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Agentic artificial intelligence (AI) systems capable of autonomous goal-directed behavior, multi-step planning, tool use, multi-agent coordination, and iterative self-correction represent a transition from passive clinical AI tools toward systems that can participate in complex healthcare workflows. However, empirical evidence remains fragmented across clinical decision support, patient monitoring, and administrative applications, and no systematic synthesis has evaluated which agentic principles have been technically demonstrated and which have accumulated sufficient evidence to support responsible clinical deployment. We conducted a PRISMA-informed systematic review of peer-reviewed empirical studies published between January 2025 and April 2026. Searches across five bibliographic databases and Google Scholar, supplemented by citation tracking, identified 443 unique records for screening, of which 25 met the predefined PICOS and quality appraisal criteria. Evidence was synthesized using an evidence-informed seven-principle framework derived from the integration of agentic AI, clinical AI, and healthcare governance literature. This framework provides a structured lens for examining how agentic principles are evaluated individually and in combination, enabling a deployment-readiness perspective that extends beyond capability-focused assessments alone. The evidence base was concentrated on technical capability principles, whereas human oversight, safety, compliance, and equity-related evaluation received comparatively limited attention. Most studies remained at the laboratory, benchmark, or proof-of-concept stage, and none reported demographic-stratified performance outcomes. Overall, the findings suggest a structural asymmetry in agentic healthcare AI: empirical research is advancing agentic capabilities more rapidly than it is generating evidence for the oversight, safety, equity, and governance mechanisms required for responsible clinical translation.

Clean water is a basic human necessity that plays a vital role in public health and well-being. However, access to clean water remains a challenge in many drought-prone areas. This condition requires effective planning and evaluation to ensure the sustainability of clean water supply programs. This article aims to examine the application of the Problem Solving Cycle (PSC) method in the planning and evaluation of clean water supply programs in drought-prone regions. The study employed a literature review method by analyzing various scientific articles and relevant documents. Data were analyzed descriptively through identification, classification, and information synthesis. The findings indicate that PSC supports program planning and evaluation through the stages of problem identification, cause analysis, action planning, implementation, monitoring, and evaluation. Clean water supply programs contribute to improving community access to safe water and adequate sanitation, although several challenges remain, including limited resources, infrastructure management issues, and program sustainability. Therefore, PSC can serve as an effective approach to support the success of clean water supply programs in drought-prone areas.

Introduction: Congestive Heart Failure (CHF) is a complex clinical syndrome caused by the inability of the heart to pump blood adequately to meet the body's metabolic demands. This condition may result in dyspnea, edema, activity intolerance, fatigue, and reduced quality of life. Comprehensive nursing care is essential to address patients' needs and improve health outcomes. Methods: This study employed a descriptive case study approach. Data were collected through interviews, observation, physical examination, and medical record review. Nursing care was provided using the nursing process, including assessment, nursing diagnosis, planning, implementation, and evaluation. Results: The assessment of Mrs. T identified four nursing diagnoses: ineffective breathing pattern, hypervolemia, activity intolerance, and knowledge deficit. Nursing interventions included semi-Fowler positioning, fluid balance monitoring, passive Range of Motion (ROM) exercises, and health education. Following two days of nursing implementation, three nursing diagnoses were resolved, namely ineffective breathing pattern, activity intolerance, and knowledge deficit. Hypervolemia was partially resolved due to persistent fluid imbalance. Discussion: The findings indicate that the application of systematic nursing care can improve several clinical problems experienced by CHF patients. However, fluid balance management remains a challenge, highlighting the importance of patient adherence to fluid restriction and continuous monitoring to prevent worsening symptoms.

The thesis supervision process in higher education institutions is still frequently conducted manually, which may lead to inefficiencies in recording supervision data. This study focuses on the development and implementation of a web-based thesis supervision attendance application designed to facilitate attendance documentation, supervision session administration, and systematic monitoring of thesis supervision history. The methodology applied in this study is the Waterfall model, which includes the phases of requirements analysis, system design, implementation, and testing. The application was developed using the Laravel framework and a MySQL database. The system design was modeled using Unified Modeling Language (UML), while the validation process was conducted through Black Box Testing techniques. The research findings indicate that the developed application is capable of performing real-time supervision attendance recording, managing supervision information, and generating attendance reports effectively and efficiently. It can be concluded that the web-based thesis supervision attendance application improves the efficiency and accuracy of supervision record management and supports a more effective thesis supervision monitoring process..

This study investigates the performance of Internet of Things (IoT)-based monitoring systems using a mobile hotspot and IoT sensors for temperature and humidity data transmission. The research is based on the IoT concept, which enables electronic devices to communicate and exchange data through internet networks without direct human intervention. System performance was evaluated using standard Quality of Service (QoS) parameters, including throughput, packet loss, delay, and jitter. The experimental setup utilized a NodeMCU ESP32 microcontroller and a DHT22 sensor, with measurements conducted at various transmission distances through wireless communication media. The objective was to determine the reliability of hotspot connectivity and sensor communication in supporting IoT applications. The results indicate that the optimal performance was achieved at a distance of 20 meters using a 40-lambda variation. Furthermore, the communication signal between the ESP32 device and the mobile hotspot remained detectable up to a maximum distance of 32 meters. These findings demonstrate the effectiveness of the proposed IoT system for environmental monitoring applications within specific transmission ranges.

Postpartum depression is a postpartum mental health disorder that significantly impacts maternal well-being, infant development, and family functioning. The high prevalence of postpartum depression in Indonesia is due to limited access to health services, low mental health literacy, and social stigma in the community. This indicates a significant gap between the need for maternal mental health services and the availability of existing interventions, making education a crucial component in efforts to prevent postpartum depression early. This study aims to analyze the prevention of postpartum depression in postpartum mothers through telenursing-based education and screening using the Edinburgh Postnatal Depression Scale (EPDS) in the community. A descriptive case study design was used, involving one respondent, a 25-year-old primigravida mother residing in the Bantul area. The intervention was implemented online via WhatsApp and video calls, including structured health education on postpartum psychological changes, adaptive coping strategies, and the importance of social support. The intervention also included daily remote monitoring of the respondent's condition via the WhatsApp mobile application. The EPDS was administered as a pre-test and post-test to evaluate changes in the respondent's psychological condition. The findings showed a significant decrease in the EPDS score from 16 (moderate depression) to 6 (minimal depression), indicating significant psychological improvement. These results imply that integrating EPDS screening, structured health education, and daily monitoring is an effective and accessible community-based approach to preventing postpartum depression, particularly for mothers with limited mobility and access to health services.

Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.

One of the goals of a building is to create a comfortable environment that does not affect the health and operations of its occupants, therefore a system needs to be created to ensure comfort in classrooms. To fulfill a comfortable situation, there is a standard that regulates comfort, especially thermal and visual comfort. Thermal comfort is regulated in SNI 03-6572-2001 and visual comfort is regulated in SNI 03-6575-2001. The aim of this research is to design a tool to automatically monitor temperature and lighting, determine greater accuracy, determine temperature and lighting comfort distances, and test Smart Comfort measurement results in accordance with the SNI-03-6571-2001 and SNI-03-6575-2001 conformity standards. This design uses ESP32 with IoT-based LDR and DHT11 sensors which can be seen on the web and application, determines the accuracy and range of Smart Comfort values for monitoring temperature and lighting and determines the suitability of measurement quantities in the SDN PINANG 3 classroom.

This study presents an innovation design for an Internet of Things (IoT)-based watering and liquid fertilizer control system for chili plants using a NodeMCU ESP32. The main problem addressed is the manual watering and fertilizing process, which makes it difficult for farmers to monitor soil moisture, temperature, air humidity, and light intensity in real time. The recommended method used in this draft is Research and Development (R&D) with a prototyping approach because the study focuses on designing, building, integrating, and testing an IoT device through iterative stages. The system is designed using a soil moisture sensor, DHT11, LDR sensor, two-channel relay, two 12 V DC pumps, 16x2 I2C LCD, and the Blynk Mobile application for remote monitoring and control. Sensor data are transmitted to Blynk as percentage values and plant condition statuses, while the water and fertilizer pumps can be controlled using virtual buttons. The control logic defines the optimal condition for chili plants based on soil moisture of 60-80%, temperature of 25-30°C, air humidity of 60-80%, and light intensity of 50-90%. Prototype documentation and functional testing data will be completed in the next stage.

This study aims to evaluate the effectiveness of the e-monev application at the Jambi Province Public Works and Public Housing Agency (PUPR) and to identify the constraints encountered in its management. The research used a qualitative descriptive method with data collection through in-depth interviews, observation, and documentation involving seven informants directly engaged in the application's management and operation. The analysis framework adopts the e-government success element theory by Indrajit (2002), comprising three dimensions: Support, Capacity, and Value. The findings reveal that the Support dimension is already strong, marked by leadership commitment, annual socialization and training, clear policy directives, and effective inter-unit coordination. In the Capacity dimension, human resource competence is generally adequate; however, technical constraints such as network instability, system slowdowns during concurrent access, and dependency on external programmers remain significant challenges. The Value dimension confirms that the e-monev application provides positive contributions to reporting efficiency, real-time monitoring, and data-based decision-making. Nevertheless, the overall effectiveness has not yet reached the optimal level due to persistent technical and infrastructure constraints. This study is expected to serve as an empirical basis for improving the quality of digital monitoring and evaluation systems in regional government agencies.

This article analyzes the legal status of golf course development, specifically Gunung Geulis Country Club (GGCC) in the Puncak-Bogor Area within Indonesia's spatial planning policy and Green Open Space (RTH) protection framework, while evaluating its ecological impacts from an environmental justice perspective. Employing a normative juridical method with statutory, conceptual, and case-based approaches, this research identifies two core issues. First, golf course development occupies a legal grey area: it is recognized as private RTH under Article 29(2) of Law No. 26 of 2007 on Spatial Planning, yet substantively fails to fulfill the ecological functions mandated by law due to its exclusivity and inaccessibility to the public. Second, from an environmental justice perspective, GGCC's development generates layered distributive, procedural, and corrective injustices: surrounding communities bear the burden of groundwater contamination, reduced spring discharge, increased surface runoff, and flood risk, while economic and recreational benefits are concentrated among a small number of high-fee members. Weak AMDAL instruments, minimal public participation, and inconsistent spatial monitoring in the Puncak-Bogor protected area exacerbate these conditions. This article recommends policy reforms including stricter private RTH definitions, strengthened participatory AMDAL processes, and consistent application of the polluter-pays principle.

Non-performing loans represent one of the risks faced by banks in their lending activities, particularly in housing loan (KPR) products. This study aims to analyze the procedures for resolving problematic housing loans and to identify the factors causing the decline in debtors’ repayment ability, as well as the obstacles encountered in the implementation of such procedures at PT Bank Tabungan Negara (Persero) Tbk, Tasikmalaya Branch Office. This research employs a qualitative method with a descriptive approach. Data were collected through direct observation and interviews with relevant parties within the bank. The results show that the procedures for resolving problematic loans are carried out through several stages, including submission of restructuring applications, document verification, analysis of the debtor’s repayment capacity, determination of restructuring schemes, approval, agreement signing, and post-restructuring monitoring. These procedures are supported by the application of the 3R concept, namely rescheduling, reconditioning, and restructuring, which are implemented flexibly according to the debtor’s condition. The findings also indicate that the decline in debtors’ repayment ability is mainly caused by decreased income, job loss, increased living expenses, and unstable economic conditions. In practice, several obstacles were identified, such as incomplete documentation, lack of debtor cooperation, and issues related to collateral ownership that has been transferred from the original debtor. Therefore, improved supervision, better communication, and stronger coordination between the bank and debtors are necessary to ensure the effectiveness of loan resolution procedures.

National milkfish aquaculture production in 2024 reached 792,864 tons, highlighting the strategic role of this commodity in supporting the economy of coastal communities. central java,as one of the main contributors, has significant potential for pond development, including in Ujungwatu Village, Jepara Regency. However, pond management still faces challenges regarding human resource (HR) quality and the optimal utilization of technology. This community service program aims to enhance students’ capacity through outreach and training on Internet of Things (IoT)-based milkfish pond development as an effort to strengthen HR at the Sidomaju 2 SME. Implementation methods include the stages of observation and problem identification, work program planning, WebGIS design, IoT outreach and implementation, and evaluation. The materials covered included an introduction to IoT concepts based on ESP8266, pond monitoring, milkfish feed management, and the implementation of WebGIS as a digital mapping system for the Kalingga milkfish ponds.The activity was attended by 20 students from MA NU Ujungwatu and was conducted in a participatory manner through experiential learning. The results of the activity demonstrated an increase in students’ understanding of the application of technology in milkfish farming, as well as heightened awareness of the importance of efficient and sustainable pond management. The main output of the program is the Kalingga Milkfish Pond WebGIS, which can be utilized as a digital medium for education and monitoring of pond areas. This program contributes to improving students’ technological literacy while supporting the strengthening of pond operational systems based on digital innovation.

Pressure ulcers are a common complication in patients with impaired consciousness due to prolonged immobilization. This condition can increase morbidity and prolong the length of stay, thus requiring effective preventive interventions such as progressive mobilization. To determine the outcomes of implementing progressive mobilization on the incidence of pressure ulcers in patients with impaired consciousness at the Anggrek 2 High Care Unit (HCU) of Dr. Moewardi General Hospital. This study employed a case study design involving two participants with impaired consciousness. The intervention, consisting of progressive mobilization, was conducted over 3 days with monitoring of skin condition and pressure ulcer risk. Findings: Following the implementation of progressive mobilization, both participants showed improvement in skin condition and a reduction in the risk of pressure ulcers, as evidenced by the absence of progression to more severe wounds and an increase in skin integrity. The application of progressive mobilization is effective in preventing and reducing the risk of pressure ulcers in patients with impaired consciousness. This intervention can serve as a safe and planned nursing intervention in the intensive care unit.

Suspicious urban sound recognition is a critical component of intelligent public safety and urban monitoring systems, enabling the automated identification of anomalous acoustic events such as gunshots, sirens, and other security-sensitive sounds. However, existing deep learning approaches often struggle to simultaneously capture long-range temporal dependencies and global contextual relationships, particularly under noisy and acoustically complex urban conditions. This limitation can reduce reliability in safety-critical scenarios where missed detections carry significant risk. To address these challenges, this study proposes a Multi-Branch Bidirectional Long Short-Term Memory (BiLSTM) framework with Multi-Head Self-Attention (MHSA) for enhanced sequential and contextual feature modeling. Mel-frequency cepstral coefficients (MFCCs) are extracted from a curated subset of the UrbanSound8K dataset, comprising five suspicious sound classes, and used as input to the proposed architecture. The multi-branch design enables complementary temporal representations, while the self-attention mechanism provides lightweight contextual weighting of BiLSTM outputs. Experimental results demonstrate that the proposed model achieves a test accuracy of 95.59%, outperforming conventional Dense and LSTM-based baseline models under identical experimental settings. An ablation study further confirms the contribution of multi-branch integration and attention-based enhancement to overall performance. Class-wise evaluation reveals consistently high recall across all sound categories, particularly for safety-critical classes such as gunshots and sirens. These findings indicate that the proposed framework provides robust and reliable performance, making it suitable for real-time smart city surveillance and public safety applications.

This study aims to design and develop an Augmented Reality (AR)-based application for real-time data monitoring of an automatic weather station at PT. XYZ. The system was developed using a prototyping method, involving five key stages: sensor data acquisition via CBOX devices and the Modbus RTU protocol, data transmission through a Flask-based Web API, storage in a MySQL database, QR code-based target image creation using the Vuforia Engine, and a 3D visualization interface developed with Unity integrated with the Vuforia SDK on Android devices. The system successfully received and stored weather data—such as temperature, humidity, and wind speed—into the MySQL database. The AR application also displayed a stable 3D interface panel over QR code markers, providing real-time data updates through an HTTP-based mechanism. This research demonstrates the successful integration of SCADA, Flask, MySQL, and Unity, enhancing both the functionality and commercial appeal of PT. XYZ’s weather station products. Future research should focus on field testing, cloud network integration, and device compatibility.

The principle of the presumption of innocence is a fundamental principle in criminal law that aims to protect the rights of suspects from the beginning of the legal process. This principle is enshrined in the 1945 Constitution and regulated in Article 8 of Law Number 8 of 1981 concerning the Criminal Procedure Code (KUHAP), which states that a person cannot be considered guilty unless a court decision with permanent legal force declares them guilty. The application of this principle at every stage of examination (investigation, prosecution, and trial) is crucial to ensuring the protection of the rights of perpetrators (suspects or defendants) and preventing the abuse of power that may harm individuals who have not been proven guilty. This research focuses on examining the consequences of violations of this principle in investigations and the protection of suspects’ rights within the criminal justice system, along with efforts to prevent such violations. The method used is a literature review with a juridical-normative approach. The findings indicate frequent neglect of the presumption of innocence, leading to human rights violations. To prevent this, stricter supervision by external monitoring bodies, such as the National Commission on Human Rights, is necessary to safeguard the rights of suspects.

Health program planning is an essential process in improving public health status through a systematic and data-driven approach. This study aimed to analyze health program planning based on situational analysis and the problem solving cycle (PSC), as well as to identify the root causes of problems using the fishbone approach. The research employed a descriptive qualitative method supported by simple quantitative data. Data were collected through in-depth interviews, observations, and documentation involving purposively selected informants. The results of the situational analysis identified several major health problems, including stunting, low posyandu attendance, and limited maternal knowledge regarding nutrition. Priority setting using the USG method determined stunting as the main health issue. Furthermore, root cause analysis using a fishbone diagram revealed that stunting was influenced by various factors related to man, machine, method, material, environment, money, and management. The application of the problem solving cycle facilitated systematic intervention planning, including nutrition education, community empowerment, and child growth monitoring. Program implementation demonstrated improvements in maternal knowledge and posyandu attendance. In conclusion, health program planning based on situational analysis, fishbone analysis, and the problem solving cycle proved effective in producing targeted and sustainable interventions.

Health program planning is an essential process in improving public health status through a systematic and data-driven approach. This study aimed to analyze health program planning based on situational analysis and the problem solving cycle (PSC), as well as to identify the root causes of problems using the fishbone approach. The research employed a descriptive qualitative method supported by simple quantitative data. Data were collected through in-depth interviews, observations, and documentation involving purposively selected informants. The results of the situational analysis identified several major health problems, including stunting, low posyandu attendance, and limited maternal knowledge regarding nutrition. Priority setting using the USG method determined stunting as the main health issue. Furthermore, root cause analysis using a fishbone diagram revealed that stunting was influenced by various factors related to man, machine, method, material, environment, money, and management. The application of the problem solving cycle facilitated systematic intervention planning, including nutrition education, community empowerment, and child growth monitoring. Program implementation demonstrated improvements in maternal knowledge and posyandu attendance. In conclusion, health program planning based on situational analysis, fishbone analysis, and the problem solving cycle proved effective in producing targeted and sustainable interventions.

This study aims to design and develop an Android-based public infrastructure damage reporting application on Lepat Island, South Bangka Regency, as an effort to support the Smart Island concept. The background of this study is the continued use of a manual reporting system that causes delays in handling and a lack of transparency. The method used is Agile which includes the stages of system design, implementation, testing and periodic evaluation. Data collection techniques are carried out through observation, interviews, and literature studies. The results of the study are an Android-based application that allows the public to report infrastructure damage with features to upload photos, descriptions, and GPS-based locations. In addition, the application provides report history and status monitoring features to increase transparency. The implementation results show that the use of mobile technology can improve service efficiency, speed up the reporting process, and increase community participation. This application is expected to support the implementation of Smart Governance and Smart Island in the region.