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Water is an essential need for all living things on Earth to support various vital biological processes. Without water, life cannot exist because of its role as a solvent, nutrient transport medium, and temperature regulator. Water in nature does come from various sources such as rivers, lakes, rain, and groundwater, which are not all clear because they are contaminated with particles or other substances, in contrast to mountain springs which are often purer. In this study, a water filtration system was created aimed at making turbid water purify to be suitable for use by assessing the NTU (Nephelometric Turbidity Unit) of turbid water due to suspended particles such as mud or sediment, thus producing clear water suitable for use for household or irrigation purposes. The use of two Siemens S7-1200 PLCs as controllers in the water filtration system is a reliable redundant approach to automate mechanisms such as pump, valve, and NTU sensor settings. PLC 1 is used for the filtration system and PLC 2 is used for the water distribution system. The water distribution process uses a DC pump that runs when it receives input from the water level sensor in the raw water tank and clean water tank. The water filtration process has three main stages: reading the water turbidity level using a turbidity sensor, regulating the water flow rate using a solenoid valve, and filtering the water using filter media. The system's operation can be monitored and controlled through a SCADA system. Both PLCs are connected using an OPC server for communication between the PLC and SCADA. The OPC server sends data from the PLC to the Wonderware InTouch application as the SCADA system. To monitor and control the SCADA system, users must log in to access the system.

The lompong flower is an ornamental plant that requires stable soil moisture conditions and a stable environment so that it can grow optimally. Moisture level mismatches often hinder growth and even have the potential to cause plant damage. This research aims to design and implement an Internet of Things (IoT)-based humidity monitoring system that is able to monitor the humidity condition of the pond flower in real-time. The system developed uses soil moisture sensors as input devices, microcontrollers as data processors, and internet networks as a medium for sending data to the monitoring platform. The data obtained is displayed directly so that users can know the actual humidity conditions and take appropriate maintenance actions. The results show that the system is able to display moisture data with a good level of accuracy, as well as provide relevant information for users in plant care decision-making. The implementation of this system has proven to be effective in supporting the maintenance of lompong flowers, especially in maintaining soil moisture stability. This research is expected to be a reference for the development of IoT-based ornamental plant monitoring technology, as well as contribute to improving the quality of plant care in a more efficient and measurable manner.

Corn milk yogurt is a fermented product that has a low protein content. One of the efforts to increase the protein content of corn milk yogurt is by adding spirulina and soy protein isolate as a source of high protein. The objectives of this research are 1) to study the formulation of high protein corn milk yogurt with the addition of spirulina and soy protein isolate; 2) to study the characteristics of corn milk yogurt. Corn yogurt with the addition of spirulina (0.08, 0.12 and 0.16%) and soy protein isolate (4.5, 8.5 and 12.5%) were tested for physicochemical and sensory characteristics. Results revealed Corn yogurt addition with of 0.08-0.16% spirulina and 4.5 - 12.5% soy protein isolates have protein content of 1.94 - 7.04%, water content of 76.0-81.1%, fat content of 0.66 – 1.17%, 14.5-16.6% of carbohydrate content, viscosity of 328.3-1128.7 mPas, total solids 16.01-17.93oBrix, pH of 3.41-3.67, lactic acid bacteria of 51 x 107 CFU/ml – 76 x107 CFU/ml.  Corn yogurt has sensory characteristics including yogurt taste of 2.60-3.68, green color of 2.42-3.82, yellow color of 1.45-2.53, corn flavor 2.27-2.60, beany flavor 2.70-3.17, spirulina flavor 2.23-3.08, viscosity 2.62-3.82 and preference of 2.25-2.9. The best formulation for making corn yogurt is a combination treatment with 8.5% soy protein isolate concentration and 0.12% spirulina with a protein content of 5.41%. While yogurt is preferred, this formula still needs some fine tuning to eliminate the fishy scent caused by the spirulina

Akumulasi limbah yang tidak terkelola secara efektif menimbulkan ancaman serius bagi keberlanjutan lingkungan dan kesehatan masyarakat global, khususnya di kawasan urban padat penduduk. Meskipun teknologi pemilahan sampah otomatis telah berkembang, sistem eksisting sering kali terkendala efisiensi energi yang rendah dan isu higienitas akibat ketergantungan pada kontak fisik atau penggunaan sistem kamera yang aktif terus-menerus (always-on). Penelitian ini bertujuan merancang Sistem Pemilah Sampah Cerdas Hibrida yang mengintegrasikan teknologi visi komputer algoritma YOLOv8 Nano dengan sensor ultrasonik HC-SR04 untuk mewujudkan solusi pemilahan nirkontak (touchless). Menggunakan metode Research and Development (R&D), sistem ini menawarkan mekanisme manajemen daya efisien, di mana sensor ultrasonik bertindak sebagai pemicu jarak dekat (<10 cm) yang secara otomatis membuka pintu sampah dan mengaktifkan kamera akuisisi citra hanya ketika objek terdeteksi. Model YOLOv8 dilatih menggunakan dataset sebanyak 25.077 citra yang bersumber dari repositori publik Kaggle, dan berhasil mencapai performa Presisi sebesar 80,6% serta mAP@50 sebesar 61,7%. Berdasarkan pengujian real-time, sistem mampu mengklasifikasikan dan menginstruksikan aktuator servo untuk memisahkan sampah ke wadah organik atau anorganik secara akurat. Dilengkapi modul IoT ESP8266, sistem ini juga memfasilitasi pemantauan kapasitas volume sampah. Kesimpulannya, integrasi cerdas antara pemicu sensor dan deep learning ini tidak hanya memperbaiki akurasi pemilahan, tetapi juga menghadirkan solusi yang lebih higienis, responsif, dan hemat energi sebagai pendukung ekosistem Smart City.

Design and Development of Fire Monitoring System Based on Internet of Things Using Flame Sensor and Smoke Sensor (Case Study at Balai Desa Dlimas). Fire is a disaster that can cause significant losses in both material and human life. Early fire detection is crucial to minimize the potential impact. Balai Desa Dlimas, located in Banyuputih District, Batang Regency, currently lacks an adequate fire detection and monitoring system. Therefore, this study aims to design and implement a fire monitoring system based on the Internet of Things (IoT) using an ESP32 microcontroller, a flame sensor, an MQ-2 smoke sensor, and a DHT22 temperature sensor. The research method used is Research and Development (R&D) with a prototyping model. The system is designed to detect the presence of fire, smoke, and room temperature in real time. Data from the sensors is processed by the ESP32 and sent to the Blynk application to notify users. Warning outputs are also provided through a buzzer and LED lights as on-site alerts. The test results show that the system can detect fire, smoke, and temperature increases with high accuracy. Users can receive real-time notifications via the Blynk application within less than 3 seconds of fire detection. This system is expected to help Balai Desa Dlimas minimize fire risk and can be further developed for implementation across various public facilities.

Children with disabilities live with one or more conditions, physical, cognitive, sensory, or social, that limit their daily activities. These conditions necessitate specialized care and attention tailored to their specific needs. This study aims to explore how parents come to terms with their child’s condition, the factors that influence this acceptance, and its overall impact on family well-being. The results suggest that parental acceptance is rarely a straightforward process. It involves a complex emotional journey, often moving through stages of denial, anger, bargaining, and depression before reaching a point of acceptance. The speed and depth of this process are heavily influenced by both internal and external factors. Internally, spiritual values, educational background, and effective coping strategies play a vital role. Externally, the presence of social support and access to healthcare services are crucial. When parents embrace their child’s condition with unconditional love and focus on their potential, it significantly enhances the child’s developmental outcomes. Conversely, a lack of acceptance can increase the burden of caregiving and hinder the child’s progress.

Gas turbine generators play an important role in providing electrical energy, especially in the maritime sector, but they are vulnerable to disturbances such as overcurrent and undervoltage, which can cause equipment damage. This study aims to design and test an automatic protection system based on the ESP32 microcontroller with the INA219 sensor to detect current and voltage, as well as a relay as a circuit breaker. The method used is an experimental approach including static and dynamic testing, both with and without a 5W AC lamp load as a simulation of real loading conditions. Test results show that the average sensor reading error is 2.65% for current and 1.76% for voltage, which is still within the ±3% tolerance limit. The system is able to disconnect the load when any parameter exceeds the protection threshold, although there are slight inconsistencies in the relay response due to sensor reading fluctuations. In conclusion, this automatic protection system is proven to be 85% accurate and responsive in maintaining the operational reliability of the gas turbine generator, making it applicable as a preventive solution against electrical disturbances in marine environments.

The Islamic conception of science views knowledge as a unity originating from Allah SWT and directed towards realizing human welfare. Islamic science does not separate the rational, empirical, and spiritual dimensions, but rather integrates revelation, reason, and sensory experience in obtaining truth. Science from an Islamic perspective is understood as a human effort to read and understand the verses of kauniyah as a manifestation of God's power in the universe. This study aims to analyze the Islamic conception of science and its relationship with science, and examine the urgency of integrating the two in facing the challenges of modern science. The research method applied in this study is a literature study with a qualitative approach, through descriptive and interpretive analysis of various relevant literature sources. The research findings indicate that the integration between Islamic science and science plays a strategic role in overcoming the scientific dichotomy, strengthening the ethical foundation of science, and building a scientific paradigm oriented towards the values ​​of monotheism and humanity. Thus, the Islamic conception of science and science offers an alternative, holistic and sustainable scientific framework.

This community service program is entitled Improving Microcontroller Competence of Students of SMK Negeri 3 Pematangsiantar through Arduino Training Based on Industrial Practice and is implemented as a follow-up to the school's official request to invite university practitioners as guest teachers in order to strengthen industry-based vocational learning. The activity was carried out on November 27, 2025 at the Computer Laboratory of SMK Negeri 3 Pematangsiantar involving 50 grade XI students, and guided by Andi Setiadi Manalu, S.Kom., M.Ti as the main instructor of Arduino material which focuses on the introduction of microcontrollers, basic programming, sensor integration, and simple automation project design. The purpose of this activity is to improve students' technical competence in the field of embedded systems while fostering work readiness through real-world practice-based learning experiences. The implementation method uses an experiential learning and project-based learning approach, which combines brief conceptual explanations with direct practice, technical discussions, and problem-based project assignment completion. The evaluation results show an increase in students' understanding of microcontroller functions, programming logic structures, and the ability to connect hardware and software in one work system. In addition, students demonstrated increased motivation, confidence, and interest in industrial technology, as reflected in their active participation during the practicum and their successful independent completion of functional prototypes. Overall, this activity proved effective in strengthening the technical competencies of vocational high school students and supporting the implementation of the link and match policy between vocational education and the needs of the industrial world, while also emphasizing the strategic role of higher education institutions in supporting the improvement of human resource quality at the vocational high school level.

In this study, a device was designed and implemented to control the water pH and nutrient density (concentration) in a hydroponic system using the Mamdani method of fuzzy logic, thus maintaining nutrient solution parameters within an optimal range for plant growth. This system relies on three input values ​​obtained from a water pH sensor, a nutrient TDS sensor, and a flow meter. These three sensors are used to control four peristaltic motors, each of which functions to increase and decrease the pH and nutrient levels in the solution. The speed of the peristaltic pump motor, when the water pH is set at 6.5 and the nutrient concentration is set at 700 ppm, is influenced by the difference between the sensor reading and the set point. The greater the difference, the higher the peristaltic pump motor speed. Conversely, the smaller the difference between the sensor reading and the set point, the lower the peristaltic pump motor speed. Furthermore, the amount of water flowing through the pipe also influences the peristaltic pump motor speed.

This study aims to design, implement, and test a prototype that automates three functions, namely watering, fertilizing, and pest control based on Arduino Uno with the ability to directly monitor soil moisture and pH. This system is equipped with four main types of sensors. Soil condition monitoring involves an FC-28 soil moisture sensor and a soil pH sensor, water level measurement involves an HC-SR04 ultrasonic sensor, and pest detection in the plant area involves a RIP sensor. All data obtained from these sensors is then processed by the Arduino Uno microcontroller to automatically activate actuators such as water pumps, liquid fertilizer pumps, buzzers, and DC motors according to soil conditions and plant needs. Prototype testing was conducted on simulated land with various scenarios of moisture, soil pH, and pest activity. The test results revealed that the system was proven to be able to significantly optimize water and fertilizer utilization, as well as reduce pest disturbances that could potentially damage plants.  In addition, this system also displays the operational status directly through an LCD screen, making it easy for users to monitor. The advantage of this system is its multi-function integration in a single device that is cost-effective and easy to operate. In the future, the functionality of this system can be improved through integration with Internet of Things (IoT) technology, enabling remote monitoring and control with greater efficiency. More broadly, this study is expected to support increased production and sustainable agricultural practices in Indonesia.

Human–Robot Interaction (HRI) systems increasingly rely on data-driven approaches to interpret multimodal sensory inputs and support natural interaction. However, purely neural-based HRI models often suffer from limited interpretability and insufficient context-aware decision-making, which can reduce user trust and adaptability in dynamic interaction scenarios. To address these limitations, this study proposes a hybrid neural–symbolic HRI framework that integrates multimodal neural perception with explicit symbolic reasoning for adaptive and interpretable robot behavior. The proposed system combines deep neural networks for processing visual, speech, and gesture inputs with a rule-based symbolic reasoning layer that models interaction context, user states, and behavioral constraints. A loosely coupled integration strategy enables neural outputs to be transformed into symbolic representations, allowing logical inference to guide action selection while preserving perceptual accuracy. The framework was evaluated through controlled HRI experiments comparing a neural-only baseline with the proposed hybrid configuration across multiple interaction scenarios. Experimental results demonstrate that the hybrid neural–symbolic system significantly improves interaction accuracy, contextual responsiveness, and user satisfaction, while achieving substantial gains in interpretability. These findings indicate that symbolic reasoning effectively complements neural perception by enhancing transparency and context-aware adaptation without compromising performance. The study concludes that hybrid neural–symbolic architectures provide a promising foundation for developing trustworthy, adaptive, and human-centered HRI systems.

 Autonomous robot navigation in dynamic and unstructured environments remains a critical challenge due to unpredictable obstacles, sensor uncertainty, and limited adaptability of traditional planning algorithms. Although conventional navigation methods such as graph-based, potential field–based, and sampling-based approaches have been widely adopted, their performance under real-time dynamic conditions is still constrained. This study aims to design and implement a comprehensive experimental framework to evaluate the effectiveness and limitations of conventional navigation algorithms for autonomous mobile robots operating in dynamic unstructured environments. The research adopts an experimental and comparative methodology by implementing A*, Dijkstra, Artificial Potential Field (APF), and Rapidly-Exploring Random Tree (RRT) algorithms in simulated static and dynamic scenarios. Performance is assessed using quantitative metrics including path length, computation time, success rate, collision rate, and path smoothness. The experimental results demonstrate that graph-based algorithms achieve high success rates and optimal path efficiency in static environments but exhibit limited adaptability to dynamic changes. APF offers fast computation but suffers from high collision rates due to local minima, while RRT shows better adaptability in dynamic environments at the cost of longer and less smooth paths. These findings confirm that conventional navigation methods are insufficient for robust autonomous navigation in highly dynamic and unstructured environments. The study highlights the necessity of adaptive and learning-based navigation frameworks, such as deep reinforcement learning, to enhance real-time decision-making, robustness, and autonomy in future robotic systems.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

The integration of Digital Signal Processing (DSP) algorithms in low power microcontroller based embedded systems has emerged as a promising solution to optimize energy efficiency without compromising signal accuracy and performance. This study focuses on the design and optimization of DSP algorithms specifically for microcontrollers, aimed at achieving real-time, reliable monitoring for applications such as healthcare, environmental sensing, and IoT devices. The research highlights the system's ability to handle complex signal processing tasks while maintaining low power consumption, ensuring long-term, continuous operation in remote or battery-powered environments. The system employs various techniques, including advanced power management strategies such as dynamic voltage scaling (DVS) and adaptive voltage scaling (AVS), along with lightweight AI algorithms and model pruning, to minimize energy use. The results show significant reductions in power consumption compared to traditional systems, particularly during continuous monitoring tasks. Despite this, the optimized DSP algorithms maintain or even enhance signal accuracy, ensuring that critical monitoring data remains reliable. Furthermore, the system demonstrates robust performance and reliability over extended periods, making it suitable for long-term deployment in critical applications such as wearable medical devices and industrial sensors. This research provides a foundation for the development of future low power embedded systems, emphasizing the importance of DSP-aware optimization in achieving energy-efficient and high-performance monitoring. Future improvements may include advanced AI-driven power optimization techniques, enhanced scalability, and cross-domain interoperability, ensuring that these systems can be effectively deployed across diverse applications, from healthcare to environmental monitoring.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.

Benign Paroxysmal Positional Vertigo (BPPV) is the most common cause of peripheral vertigo, characterized by brief episodes of vertigo due to otoconia displacement. Although most previous studies have focused on intrinsic factors such as age, gender, osteoporosis, and metabolic disorders, evidence regarding the role of environmental factors, particularly occupational noise exposure, is limited. Chronic noise has the potential to affect vestibular function through both sensory and vascular mechanisms. This study aims to narratively review the effect of occupational noise exposure on the risk of BPPV by integrating clinical, epidemiological, and experimental findings. The method used is a literature-based narrative review of the PubMed, Scopus, Web of Science, and Google Scholar databases without year restrictions, using the keywords "BPPV", "occupational noise exposure", "vestibular dysfunction", "VEMP", and "otoconia displacement". The search results obtained 25 relevant articles linking BPPV to otolith, hormonal, vascular, lifestyle factors, and occupational noise exposure. The results indicate that chronic noise can cause sensory damage (otoconia and vestibular hair cells), vascular disorders (hypertension, cardiovascular disorders, and inner ear microvascular circulation disorders), and exacerbate lifestyle comorbidities (sedentary lifestyle, osteoporosis, hypertension, diabetes). The discussion confirms that these multifactorial mechanisms explain the susceptibility of industrial workers to BPPV despite normal hearing function. The conclusion of this study is that workplace noise exposure has been shown to play a significant role as a risk factor for BPPV, therefore, prevention strategies, vestibular health monitoring, and healthy lifestyle interventions need to be optimized in occupational health programs.

The City of Singkawang faces significant challenges in drinking water management, characterized by limited production capacity, high non-revenue water (NRW), and the absence of digital infrastructure such as AMR, LoRaWAN, and DMA, necessitating a structured development framework to support the transition toward a smart water system. This study formulates an IoT-based smart water roadmap aligned with the regional development plan (RPJMD) and the operational capacity of the local water utility through a performance gap analysis with benchmark cities (Surabaya and Balikpapan), capacity assessment using six objective parameters, and the use of secondary data from official reports and technical documents. The resulting roadmap comprises three sequential phases covering basic infrastructure reinforcement, network digitalization through IoT sensors and telemetry, and the implementation of DMA and SCADA to enable real-time monitoring and control. This approach provides a realistic and adaptive implementation framework for medium-sized cities with limited resources, strengthening NRW reduction efforts, improving service reliability, and supporting the integration of digital technologies in sustainable water utility management.

Inclusive education requires a comprehensive understanding of the characteristics and learning needs of students with special needs, particularly children with autism spectrum disorders. Assessment plays a crucial role as an initial step in identifying abilities, challenges, and educational needs to design appropriate learning interventions. This study aims to describe the results of developmental assessment of a child with autism in an inclusive education setting at SKH 01 Nurbayan Grade 5. This research employs a descriptive approach using assessment instruments that cover five developmental domains: social interaction, communication, behavior, emotional regulation, and sensory perception. The results reveal significant difficulties in social communication, including low social interest, delayed language development, limited nonverbal communication, and the presence of repetitive behaviors. Emotional regulation remains underdeveloped, and sensory processing issues are evident. These findings indicate that children with autism require individualized, structured, and needs-based educational interventions. Therefore, assessment serves as a fundamental basis for planning effective and sustainable inclusive learning programs.

Acute pain is a complex phenomenon frequently experienced by post-surgical patients. If not properly managed, it can hinder the recovery process and increase the risk of chronic complications. This study aims to conduct an in-depth analysis of the concept of acute pain in surgical patients from a nursing perspective to improve the quality of care. The method used was a narrative literature review, analyzing various research articles and clinical protocols related to surgical pain management. The study findings indicate that acute post-surgical pain involves sensory and emotional dimensions influenced by the type of surgical procedure, individual pain threshold, and the effectiveness of pharmacological and non-pharmacological interventions. Furthermore, the role of nurses in conducting accurate pain assessments and patient education is a key factor in successful pain management. The implications of this study emphasize the importance of implementing integrated multimodality pain management protocols and improving nurses' competency in conducting intensive monitoring. Optimizing pain management is expected to accelerate patient mobilization, shorten hospital stays, and increase patient satisfaction with nursing services.