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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.

Absensi karyawan merupakan aspek penting dalam manajemen sumber daya manusia yang memengaruhi disiplin kerja serta akurasi administrasi dalam suatu organisasi. PT. Bahtera Komunikasi Kreatif saat ini masih menggunakan sistem absensi berbasis sidik jari yang belum terintegrasi dengan pengelolaan cuti seperti izin, cuti sakit, dan work from home (WFH), sehingga proses pencatatan dan pelaporan masih dilakukan secara manual dan berpotensi menimbulkan keterlambatan serta kesalahan administrasi. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem manajemen absensi dan cuti karyawan berbasis web yang lebih fleksibel, terintegrasi, dan mudah diakses. Metode penelitian yang digunakan meliputi observasi, wawancara, tinjauan pustaka, dan kuesioner, dengan pendekatan pengembangan sistem menggunakan model Software Development Life Cycle (SDLC) metode Waterfall. Sistem diimplementasikan menggunakan PHP Native, MySQL, serta teknologi web seperti HTML, CSS, JavaScript, dan Bootstrap, dengan dukungan HTML5 Geolocation API dan fitur kamera untuk validasi kehadiran. Pengujian sistem dilakukan menggunakan Black Box Testing dan User Acceptance Testing (UAT) untuk memastikan fungsi sistem berjalan dengan baik serta dapat diterima oleh pengguna. Hasil penelitian menunjukkan bahwa sistem mampu meningkatkan efisiensi, akurasi data, serta mempermudah monitoring dan pelaporan secara real-time dengan nilai UAT sebesar 4,45 yang termasuk kategori sangat baik. Selain itu, sistem yang dikembangkan juga mampu mengurangi ketergantungan pada proses manual serta meningkatkan transparansi dalam pengelolaan data kehadiran karyawan, sehingga dapat mendukung peningkatan kinerja operasional perusahaan secara keseluruhan.

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.

This study aims to analyze the effectiveness of the e-SIDAK application as a tool for recording and evaluating the performance of civil servants at BKPSDM Belitung Regency. The research employs a descriptive qualitative approach with 12 purposively selected informants, including managers, administrators, and application users. Data were collected through semi-structured interviews, observations, and documentation, and analyzed thematically using NVivo 12 Pro based on the digital service effectiveness indicators proposed by Pedrosa et al., namely ease of use, simplicity, understandability, consistency, trustworthiness, availability, speed, and usefulness. The findings indicate that the e-SIDAK application fulfills most effectiveness indicators, particularly in terms of ease of use, process simplicity, clarity of information, interface consistency, and service usefulness. The application facilitates systematic work activity recording, simplifies reporting, improves employee discipline, and supports performance monitoring integrated with the employee performance allowance system. However, its effectiveness remains limited in terms of system reliability, service availability, and access speed due to server capacity constraints, network conditions, and the accumulation of data input at the end of reporting periods. In addition, the point-based evaluation system has not fully reflected task duration, complexity, and workload. This study recommends strengthening system infrastructure, developing user guidelines, regulating input timing, and improving the point-based evaluation system to enhance digital service effectiveness in local government institutions.

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.

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.

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.

The increasing demand for electrical energy and the limited availability of fossil fuels have driven the development of renewable energy sources, including marine current energy, which remains underutilized in coastal and remote maritime regions. This study presents the design and realization of a small-scale marine current power generation prototype using a horizontal axis propeller turbine with a NACA S814 blade profile and analyzes the effect of turbine rotational speed on electrical power output. The system converts marine current kinetic energy into mechanical energy through turbine rotation and subsequently into DC electrical energy using a generator, which is stabilized by a Buck–Boost Converter and Maximum Power Point Tracking (MPPT) for charging a 12 VDC battery. Real-time monitoring of electrical and mechanical parameters is implemented using an Internet of Things (IoT)–based system comprising an ESP32 microcontroller, a PZEM-017 sensor, and an RPM sensor. Experimental results demonstrate a positive correlation between water flow rate, turbine rotational speed, and generator output voltage. The system begins operating at a minimum flow rate of 35.2 L/s at 56 RPM, producing 0.2 V, while optimal performance is achieved at 45.3 L/s and 516 RPM, generating up to 13.3 V. These results indicate that the proposed prototype is a viable alternative renewable energy source for marine applications.

Micro and small businesses such as grocery kiosks face various operational risks that can threaten business sustainability; however, most business owners have not yet implemented systematic risk management practices. This study aims to analyze the risk management process at Kios Pelita Harapan, a small business established in September 2023 in Maumere, using a qualitative approach through direct interviews with the business owner. The findings reveal that the risk management process at this kiosk encompasses four stages: risk identification, risk analysis and assessment, risk evaluation and prioritization, and implementation and monitoring. The primary risks identified include stockouts of fast-moving goods, product damage due to short expiry periods, fire hazards, power outages, and theft. The owner addresses these risks through daily stock checking, inventory recording, monitoring customer purchasing patterns, and coordinating with suppliers during emergencies. The application of a simple yet structured risk management approach has been proven effective in stabilizing revenue and maintaining customer trust. This study provides practical implications for kiosk business owners in implementing effective risk management despite limited resources.

The development of information technology encourages government agencies to switch from manual systems to digital systems that are more efficient and accurate. One important aspect of office administration is recording the absence of village officials, which is often done conventionally so it is prone to errors, data manipulation, and takes longer in the recapitulation process. Based on these problems, this research designs and implements a Qr-Code based attendance system at the Palatta Village Government Office. The design of this system is to provide a faster, more accurate and transparent attendance solution by utilizing Qr-Code technology which is integrated into a web-based application. The system is built with main features such as user login, Qr-Code scanning, attendance history, as well as an admin dashboard to manage data and generate reports. The results of implementation and testing using the Black Box method show that all functions run as required. Testing on users also shows that the system is easy to use, speeds up the attendance process, and helps admins in monitoring and recapitulating data. Thus, this Qr-Code attendance system is suitable to be implemented as a substitute for manual methods to increase work efficiency and discipline in the village office environment.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

This study aims to analyze and evaluate the effectiveness of bureaucratic reform in realizing good governance and improving the quality of public services in Indonesia. The research employs a library research method by reviewing various relevant academic sources, including journals, books, and official reports. The findings indicate that bureaucratic reform has a positive impact on enhancing efficiency, effectiveness, transparency, and accountability in public administration. The implementation of reforms through service digitalization, procedural simplification, and the application of standard operating procedures (SOPs) has significantly improved the quality of public services, particularly in terms of speed, accessibility, and service certainty. Furthermore, bureaucratic reform promotes the implementation of good governance principles, such as transparency, participation, and accountability. However, several challenges remain, including resistance to change, limited human resource competencies, and uneven policy implementation across government institutions. Therefore, continuous efforts are required through capacity building, strengthening monitoring systems, and optimizing the use of information technology to ensure the sustainability and success of bureaucratic reform.

The Sunter C Polder/Pump construction project is a strategic effort to address flooding issues by increasing drainage system capacity. This project involves various high-risk construction activities that have the potential to cause workplace accidents and health issues. This study aims to identify hazards, assess risk levels, and determine control measures. The HIRADC (Hazard Identification, Risk Assessment, and Determining Control) method involves a systematic identification of potential hazards at the project site, risk assessment based on impact and probability, and the formulation of appropriate control recommendations. This is a descriptive study designed to illustrate Occupational Health and Safety (OHS) hazards and risks, located at the Sunter C polder/pump construction project. The research was conducted from January to May 2025. Data were collected through interviews, observations, and information obtained directly from internal company documents. The results indicate that the application of the HIRADC method accurately identifies various primary hazards, such as the risk of falls, being struck by materials, chemical exposure, and ergonomic issues. The risk assessment suggests that most activities fall under the medium to high-risk categories, necessitating controls such as the use of Personal Protective Equipment (PPE), OHS training, and routine monitoring of working conditions. The implementation of the HIRADC method in this project is proven to enhance the culture of occupational health and safety and can serve as a reference for other construction projects in managing operational risks effectively.

Community service at Toko Entus Collection aimed to improve operational efficiency and sales through the implementation of a digital system. Students actively participated in managing social media, online promotions, and digital stock monitoring. Observations showed that digital media can disseminate product information widely, enhance consumer interaction, and support sales growth. Challenges included limited digital knowledge of the store manager, limited time for promotions, and inconsistent consumer responses. Direct guidance, socialization of digital media use, and adjustment of promotional strategies helped overcome these challenges. Students’ hands-on experience allowed the application of digital marketing theory to real business situations, while store managers benefited from improved operational performance. Therefore, this community service provides dual benefits: practical learning for students and increased competitiveness for SMEs.

The transformation of public services through information technology has become one of the government’s strategies to improve service quality for the community. The implementation of e-government enables public services to be delivered more effectively, efficiently, transparently, and with easier access. This study aims to analyze the transformation of public services through the implementation of e-government in the KLAMPID New Generation application at the Department of Population and Civil Registration of Surabaya City. The study employs a qualitative approach with a Case Study paradigm. Data were collected through interviews, observations, and documentation involving government officials and the public as service users. The research analysis uses the e-government development stage model proposed by Karen V. Layne and Jungwoo Lee, which includes the stages of presence, interaction, transaction, and transformation. The results indicate that the implementation of the KLAMPID New Generation application has supported the transformation of population administration services in Surabaya City. At the presence stage, the application provides digital information on population administration services. At the interaction stage, the system enables communication between the public and service providers through features such as notifications and service status monitoring. At the transaction stage, the public can submit service requests online by uploading required documents via the application system. Meanwhile, at the transformation stage, the KLAMPID New Generation application has integrated various population administration services into a single digital platform.

This study aims to design and develop a functionally efficient and operationally effective Internet of Things (IoT)-based air quality monitoring system for radiology departments. The system utilises a DHT22 sensor integrated with an ESP32 microcontroller to monitor the temperature and humidity of diagnostic rooms in real time, and to display the data via the UdaraKu mobile application. The research method employed a quantitative experimental approach focused on measuring system performance, specifically the accuracy of the temperature and humidity sensors. The research model used was the Research and Development (R&D) method, aimed at transforming conventional air quality monitoring in radiology into a real-time digital system based on IoT. The research results indicate that the IoT-based monitoring system is capable of maintaining room temperature and humidity stability within the ideal range, namely 22–24°C and 50–60% RH, in accordance with international standards. This improvement in environmental stability has a direct impact on reducing noise in digital radiography images, as evidenced by an increase in the Signal-to-Noise Ratio (SNR). Instrument validation demonstrated a high level of reliability with a Cronbach’s Alpha value of 0.848, reinforcing the reliability of the data and the system. Overall, the IoT-based air quality monitoring system has proven effective in controlling noise in digital radiography images, improving the quality of diagnostic services, and supporting patient safety principles and operational efficiency within radiology departments.

Community service at the Visi Iy Nike production unit was carried out using a descriptive qualitative and participatory approach to understand real conditions and challenges in the field. The activities began with direct observation of the production process to map workflows, identify operational obstacles, and evaluate the efficiency of resources, including labor, machinery, and production time. In addition, in-depth interviews and group discussions were conducted to collect information regarding production constraints and strategies applied by the operators. The data were analyzed descriptively to identify gaps between production targets and actual outcomes. Based on these findings, several interventions were implemented, including operator skill training, assistance in production planning, and the application of simple lean manufacturing principles to reduce waste and improve efficiency. The results showed that the average production efficiency reached 77%, supported by skilled operators, adequate raw materials, and flexible management practices. Routine documentation and systematic recording of production targets also contributed to better monitoring, evaluation, and faster decision-making processes.