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This research focuses on designing the shortest path to a docking station for the charging system of a robotic coffee delivery system on a cruise ship. With the growing demand for more efficient and innovative services in the maritime industry, autonomous robots are increasingly being used for tasks such as delivering coffee to passengers. However, these robots face unique challenges in the confined and dynamic environment of a ship, particularly in terms of power management and recharging. The goal of this research is to create an efficient charging system using a power bank and Internet of Things (IoT) technology to minimize downtime and ensure continuous robot operation with minimal human intervention. By utilizing sensors such as the MPU 6050 for motion detection and the TCS 3200 for color recognition, the robot can autonomously navigate the shortest path to the docking station for charging, while IoT enables real-time battery capacity monitoring. This helps reduce the time spent on non-operational tasks and improves the overall efficiency of the system. This research not only enhances the robot’s ability to operate autonomously in complex environments but also provides a flexible and scalable solution for the maritime industry to integrate advanced robotics into their service operations. The results of this study contribute to the increased adoption of autonomous systems in the maritime sector, particularly in improving service efficiency on board, reducing the need for human intervention, and ensuring smoother service delivery to passengers.

Fraud poses a significant threat to organizational integrity and public trust, especially within governmental institutions. This study explores the critical role of forensic accountants in uncovering fraud within the Audit Board of the Republic of Indonesia. Using a case study approach, the research examines the application of advanced forensic data analytics by forensic accountants. It highlights the essential skills and characteristics that make forensic accountants effective, as well as their contributions to enhancing internal controls and governance mechanisms. The study demonstrates the impact of forensic accounting on fraud detection, prevention, and deterrence. The findings emphasize the importance of integrating forensic accounting expertise within the Audit Board's operational framework to strengthen its capacity to combat financial fraud. It also identifies challenges faced by forensic accountants, such as technological limitations and cultural resistance, and suggests strategies to overcome these barriers. By shedding light on the role of forensic accountants in safeguarding public sector integrity, this research contributes to the growing knowledge on forensic accounting practices in Indonesia. The insights can inform policies and capacity-building initiatives aimed at improving the Audit Board’s fraud detection capabilities, ultimately fostering greater public trust and accountability.

This community service project aims to raise awareness and knowledge about health among the elderly through the GERKASA-LASAKIT program (Movement for Healthy and Disease-Aware Seniors) in Dusun 1, Tanjung Anom Village, Deli Serdang Regency. This initiative is driven by the high prevalence of degenerative diseases and the lack of accurate information regarding the prevention and management of these diseases among seniors. Through comprehensive health education, it is hoped that the elderly can improve their quality of life by adopting a healthy lifestyle and gaining a better understanding of common illnesses they may face. The methods used in this project include health education sessions, practical training on early disease detection, and physical activities tailored to the physical condition of the elderly. Health education is provided by medical professionals and public health experts. Additionally, individual consultation sessions are held to offer more personalized care for each participant. The results of this activity show an increase in health knowledge and awareness among the elderly, as evidenced by high participation and enthusiasm in each session. Seniors involved in this program have also begun adopting healthier lifestyles, such as engaging in regular light exercise and having routine health check-ups. This project has successfully built a support network among the elderly, their families, and the local community, creating a more caring and health-conscious environment. Thus, the GERKASA-LASAKIT program makes a significant contribution to improving the quality of life for the elderly in Dusun 1, Tanjung Anom Village. The sustainability of this program is expected to serve as a model for other villages in efforts to enhance the welfare and health of the elderly at the local level

This community service project aims to raise awareness and knowledge about health among the elderly through the GERKASA-LASAKIT program (Movement for Healthy and Disease-Aware Seniors) in Dusun 1, Tanjung Anom Village, Deli Serdang Regency. This initiative is driven by the high prevalence of degenerative diseases and the lack of accurate information regarding the prevention and management of these diseases among seniors. Through comprehensive health education, it is hoped that the elderly can improve their quality of life by adopting a healthy lifestyle and gaining a better understanding of common illnesses they may face. The methods used in this project include health education sessions, practical training on early disease detection, and physical activities tailored to the physical condition of the elderly. Health education is provided by medical professionals and public health experts. Additionally, individual consultation sessions are held to offer more personalized care for each participant. The results of this activity show an increase in health knowledge and awareness among the elderly, as evidenced by high participation and enthusiasm in each session. Seniors involved in this program have also begun adopting healthier lifestyles, such as engaging in regular light exercise and having routine health check-ups. This project has successfully built a support network among the elderly, their families, and the local community, creating a more caring and health-conscious environment. Thus, the GERKASA-LASAKIT program makes a significant contribution to improving the quality of life for the elderly in Dusun 1, Tanjung Anom Village. The sustainability of this program is expected to serve as a model for other villages in efforts to enhance the welfare and health of the elderly at the local level.

The circulation of narcotics in Aceh's territorial waters is a serious problem that affects the security and welfare of the community. This research aims to analyze the role of the Directorate of Water and Air Police (DITPOLAIRUD) of the Aceh Regional Police in efforts to control narcotics through preventive and repressive measures. The research method uses a descriptive qualitative approach with data collection techniques through interviews, literature studies and document analysis. The research results show that Ditpolairud's preventive actions include routine patrols in water areas, strict monitoring of smuggling-prone routes, as well as quick response through a quick response system. However, the implementation of this action is hampered by limited resources, such as patrol boats, ship detection technology and integrated hotlines. Meanwhile, repressive measures include arresting perpetrators, investigating and compiling case files which also face challenges in terms of the availability of personnel and forensic facilities. This research concludes that optimizing the role of the Aceh Regional Police's Ditpolairud requires increased support for facilities, infrastructure and personnel training to maximize the effectiveness of countering narcotics in Aceh's territorial waters.

Image processing has become a key technology in visual data analysis, making significant contributions across various fields such as healthcare, security, and the creative industry. This article provides a comprehensive review of the application of image processing technology in visual data analysis, focusing on the latest methods, tools, and practical applications. We discuss various image processing techniques, including segmentation, edge detection, and pattern recognition, as well as how these techniques are applied to process and analyze visual data. The study also includes performance evaluations of various commonly used image processing algorithms and software. Additionally, we explore the challenges faced in applying this technology, such as image resolution issues, noise, and high computational demands. By offering an extensive overview of the development and implementation of image processing technology, this article aims to be a valuable reference for researchers and practitioners working in the field of visual data analysis.

Spentwash, a liquid waste byproduct of bioethanol production, is a potential raw material for biogas production due to its high organic content and abundant availability. PT Energi Agro Nusantara (ENERO) utilizes spentwash through anaerobic digestion processes to produce biogas, which is used as an alternative energy source.However, optimizing biogas production still faces various challenges, including the need for efficient monitoring and increased production volume. Internet of Things (IoT)-based technology offers a solution through real-time monitoring systems, enabling direct and accurate methane level measurements.This study aims to develop a prototype of a stirred digester equipped with an IoT-based methane gas detection sensor, using the MQ-2 sensor to detect methane gas.

Background: Hypertension is a common condition in primary health care, affecting approximately 25% of the adult population and more than 50% of those aged over 65 years. As the "silent killer of hypertension", it can cause stroke, which is a major cause of morbidity and mortality worldwide. This study aims to detect early signs of stroke using the BE-FAST method in the elderly in Karangasem, Bali, and to spread the effectiveness of this method in reducing the risk of stroke. Method: This community-based education program uses the POAC (Planning, Organizing, Actuating and Controlling) approach. A total of 79 elderly participants were recruited and underwent health examinations, including blood pressure measurements and the BE-FAST screening test. The BE-FAST method assesses balance, eyes, face, arms, speech, and timing to identify early signs of stroke. Results: The results showed that 55.7% of participants had hypertension, and 67.1% had difficulty moving their legs and arms on one side of their body. Additionally, 36.7% had difficulty opening their eyes or experienced vision problems, and 32.9% experienced facial weakness or numbness. The BE-FAST method is effective in detecting early signs of stroke, with a sensitivity of 92%. Conclusion: This study highlights the importance of early detection of stroke using the BE-FAST method in the elderly population. The research results show that this method is effective in identifying early signs of stroke and can be easily taught and applied in the community. Application of this method can reduce the risk of stroke and associated morbidity and mortality.

Milk consumption and the need for beef cattle for food purposes or sacrificial purposes continue to increase from year to year. This increase is in line with the increasing level of the economy and awareness of the need for nutritious food. However, the increase in demand still faces many production constraints due to the difficulties faced by farmers, the decline in cattle production from year to year in Indonesia. This causes Indonesia to have to import beef for 35.95% of the total national beef consumption. With many factors that affect the decline in the production rate of dairy cows or beef cattle every year, one of them is air pollution and poor air quality in cages. Along with the development of technology, a monitoring and detection system for air cleanliness in cattle pens was created, using the MQ-7 sensor as the concentration of carbon monoxide, the MQ-135 sensor as the concentration of ammonia gas, the MQ-2 sensor as the smoke detection, and When the air pollution value of each sensor input is according to the air pollution standard index (ISPU), the outputs namely indicator lights, buzzers and blowers will be on, then there will be temporary air neutralization in the cage. This tool is made easier by internet of things technology, so that it can send notifications via telegram and can see the level of air pollution levels through the web.

Objective: This scoping literature review is intended to identify factors that influence Psychological well-being and see the gap synthesis of previous studies. Introduction: Students as the next generation of the nation often face various challenges, both in academic, social, and personal fields. These challenges can affect their psychological well-being, which is an important aspect in supporting the success of studies and life as a whole. Methods and analysis: This study uses 5 main stages, namely the stage of identifying questions, identifying relevant studies, selecting study results, data extraction, data synthesis. Specifically using the PCC framework and paying attention to inclusion and exclusion criteria so that the final results are obtained. Results: The results found from the data selection found a total of 8,401 articles from 1 database and duplication was done twice (in the first check using Rayyan.ai the second work during data selection with Rayyan.ai n = 807 After duplication of data selection based on inclusion exclusion criteria, 63 relevant articles were found. After manual re-detection, the final results were 13 articles that were most relevant to the research topic.    

Face detection and facial landmarks are an important technique in the field of computer vision with a wide range of potential applications, including expression recognition, security systems, and human-computer interaction. This study explores the implementation of facial landmarks detection using Python and OpenCV, focusing on the use of the Haar Cascade algorithm for face detection and the Local Binary Features (LBF) model for the identification of landmarks. The proposed method implements real-time detection via webcam, capable of recognizing 68 important points on the human face. The results show that the approach using OpenCV and LBF models has good accuracy in detecting and tracking facial features in different lighting conditions and viewing angles. This research contributes to the development of efficient and reliable facial detection methods, with wide application potential in the fields of computer vision, security, and behavioral analysis.

Hypertension is one of the main health problems faced by people around the world, including Indonesia. Hypertension is known as a "silent killer" because it often does not show symptoms until it reaches a serious stage. At the Tamansari Health Center in Tasikmalaya City, hypertension is the highest disease most suffered by the community with a total of 586 cases from January to October 2024. The high prevalence of hypertension in the Tamansari Health Center work area, a prevention and control program is needed through screening or early detection programs and counseling about hypertension. The implementation method carried out in this community service is to provide information using the lecture or counseling method. To measure public knowledge, pre-test and post-test are carried out. In addition to providing counseling, this activity also provides basic health checks. Based on the results obtained, there was a difference in the level of community knowledge before and after the provision of counseling which can be seen from the difference in the average percentage of pre-test and post-test.

Traffic accidents are one of the leading causes of death worldwide, where drowsiness while driving is a significant factor that reduces driver alertness. This study develops a real-time driver drowsiness detection system using the Eye Aspect Ratio (EAR) method to avoid this. EAR calculates the ratio of the upper and lower eyelid distances to detect signs of drowsiness based on changes in eye shape. This system utilizes the OpenCV and Dlib libraries to identify faces and measure EAR, with a threshold of 0.25 as a warning trigger. If the EAR value drops below the threshold in several consecutive frames, the system automatically activates an alarm to increase driver alertness. With the advantages of cost efficiency and ease of implementation without additional hardware, this system is suitable for various types of vehicles. The results show that this system is effective in providing early warnings, thus helping to reduce the risk of accidents due to drowsiness.

This study discusses the implementation of computer vision technology for face detection in photos using two sample images with variations in lighting and face pose. The developed system combines the Viola-Jones algorithm and Convolutional Neural Networks (CNN) to enhance resilience against lighting and face orientation variations. Experimental results show high accuracy even with only two sample images. This research also develops preprocessing techniques to handle extreme lighting conditions and demonstrates efficient implementation using Python and OpenCV.  

Indonesia is a country that faces a high risk of multiple disasters, one of which is fire disasters that can be prevented by the community through preparedness and early detection. The community service program, implemented by a team of lecturers and students from the Disaster Management Study Program at Budi Luhur University in collaboration with the Tanjungpinang SAR Office, conducted a basic fire extinguishing training session at SMAN 2 Tanjungpinang. The target audience for this training was students involved in the Scout, Paskibraka, and PKS (School Security Patrol) extracurricular activities, aimed at increasing student preparedness for fire emergencies. The activities included both theoretical materials and practical exercises on extinguishing fires and techniques for victim evacuation. The evaluation results showed an increase in participants' knowledge, particularly regarding the basic concepts of fire and victim movement, with understanding levels ranging from 45% to 100%, depending on the material. Although the results were positive, areas such as the type of fire extinguisher material and victim transfer techniques require further emphasis. This collaboration between educational institutions and disaster response agencies has proven effective in building student awareness to deal with emergency situations. Recommendations for future training include adopting a more interactive approach and incorporating intensive simulations to enhance students' understanding and readiness to face disaster.

The research focuses on creating an automated attendance system using face recognition through the Convolutional Neural Network (CNN) approach at IPB University's Vocational School. The current manual attendance methods show limitations, such as potential inaccuracies in recording and the risk of cheating, like attendance proxies. To overcome these challenges, this study applies the CNN approach with Python and OpenCV, enabling automatic face detection and recognition for students. The system accurately logs attendance by identifying faces in real time. Testing indicates that the system records attendance reliably, whether with a single individual or with multiple faces present within a single frame.

The global clean water crisis is exacerbated by significant losses in water distribution networks (WDNs), resulting in inefficient use of both water and energy resources. Traditional methods of leak detection and pressure management often fail to address these inefficiencies, leading to substantial water wastage and high operational costs. This research aims to design a sustainable, smart water distribution system using advanced technologies such as Machine Learning (ML) for leak detection and automated pressure control. The system employs real-time monitoring through IoT sensors, which continuously gather data on water pressure, flow rates, and other critical parameters. This data is analyzed using various ML algorithms, including supervised and unsupervised learning models, to detect anomalies indicative of leaks. Additionally, the system integrates automated pressure control mechanisms that dynamically adjust pressure to prevent over-pressurization, reducing both water loss and energy consumption. By combining leak detection and pressure control, the proposed system offers a more efficient, sustainable solution to water resource management compared to traditional methods. The expected outcomes include a significant reduction in water loss, enhanced energy efficiency, and improved water service quality. However, the implementation of such a system in rural or small-town infrastructure faces challenges, including sensor maintenance, algorithm reliability, and regulatory issues. A cost-benefit analysis suggests that while the initial investment in smart technologies may be high, the long-term savings in water and energy costs outweigh these costs. This study underscores the potential of ML-based systems in enhancing water conservation, operational efficiency, and sustainability in water management.

This community service program is designed to enhance mothers' capacity to prevent infections in infants and toddlers in Sumber Kokap Village, Bondowoso Regency, which faces limited access to health information and services. Utilizing a community-based participatory approach, the program involves mothers, health volunteers, and community leaders in educational activities that include needs assessments, group discussions, and training on hygiene practices and early infection detection. Findings indicate a significant improvement in mothers' understanding of the importance of infection prevention, as well as strengthened community support for implementing preventive health practices. This intervention is expected to contribute to a reduction in maternal and neonatal infection incidence and serve as a model for health empowerment in rural communities.

The rapid development of technology in the globalization era has significantly impacted various aspects of life, including the healthcare sector. RSU Bidadari Binjai, as a healthcare provider, faces challenges in diagnosing and preventing Gastroesophageal Reflux Disease (GERD), a condition with high prevalence and serious complications such as Barrett’s esophagus and esophageal cancer. Therefore, a predictive system capable of early detection is needed to ensure quicker and more effective medical intervention. This research develops a computer-based predictive system using the backpropagation method in artificial neural networks to assist in diagnosing GERD by processing patient symptom data. The system's test results show an accuracy rate of 100% in predicting GERD complications based on the given symptoms, thus supporting more timely and accurate medical interventions.    

Tropical forest fires pose a serious threat to ecosystem sustainability, particularly in Kalimantan, which is prone to seasonal fires. Early detection is key to prevention efforts, but conventional and satellite-based monitoring methods often face limitations, particularly in identifying small-scale hotspots obscured by forest canopies. This study aims to test the effectiveness of integrating drone technology with thermal sensors in tropical forest monitoring as an early fire detection system. The research method uses a field study design with an experimental approach. Drone flights were conducted over tropical forest areas in Kalimantan, systematically capturing thermal imagery according to a predetermined flight path. Thermal image data were analyzed to identify hotspots, then compared with satellite hotspot data (MODIS and VIIRS). Field validation was also conducted through direct temperature measurements using a portable infrared thermometer. Data analysis involved comparing detection results, accuracy testing, and measuring system sensitivity with a confusion matrix. The results showed that drones with thermal sensors were able to detect more hotspots than satellites, with a higher level of accuracy compared to field validation results. For example, in several study areas, drones successfully identified small hotspots that were not detected by satellites. This confirms that drones with thermal sensors have high sensitivity and can be used as early detection tools for tropical forest fires. In conclusion, the integration of drone technology and thermal sensors has proven effective as a monitoring system that complements satellite-based methods. Further development using big data and machine learning, as well as cross-institutional collaboration, is needed for optimal implementation on a large scale.