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Heat stress is a major constraint in broiler production systems located in humid tropical climates, where high ambient temperature and relative humidity impair growth performance, feed efficiency, and animal welfare. This systematic review synthesizes peer-reviewed studies published between January 2015 and December 2025 evaluating environmental housing designs and cooling strategies for mitigating heat stress in broiler chickens. This systematic review followed the PRISMA 2020 guidelines and synthesized peer-reviewed studies retrieved from Scopus, Web of Science, and ScienceDirect, published between January 2015 and December 2025. From 500 records initially identified, 26 studies fulfilled the predefined eligibility criteria and were included in the qualitative synthesis. The findings demonstrate that evaporative cooling systems, tunnel ventilation, and automated climate control technologies consistently reduced indoor temperature (2–6°C) and improved body weight gain, feed conversion ratio (FCR), and mortality rates under hot-humid conditions. However, cooling efficiency was strongly influenced by ambient humidity, necessitating integrated and adaptive environmental control approaches. Smart sensor-based systems further enhanced microclimate stability and thermal uniformity within broiler houses. Beyond performance improvements, optimized environmental management reduced physiological stress indicators, including heterophil-to-lymphocyte ratios and corticosterone levels. Overall, integrated, humidity-adaptive, and energy-efficient cooling strategies are essential to sustain productivity, welfare, and climate resilience in tropical broiler production systems.

concern for pet owners. Consequently, cat owners seek equipment that can support and simplify their daily pet care activities. However, the busy schedules of many cat owners often make it difficult for them to provide consistent care, raising concerns about the neglect of their pets. To address this issue, this study developed an automatic cat feeding system for a cat enclosure using an experimental research method. The proposed system utilizes a load cell sensor, a Real-Time Clock (RTC), a servo motor, and an ATmega16 microcontroller. The load cell sensor is employed to determine the appropriate food portion based on the cat's nutritional requirements, while the RTC controls the feeding schedule by dispensing food twice a day at predetermined times. The experimental results demonstrate that the system was successfully implemented, with the load cell measurement exhibiting an error rate of 10.71% relative to the predefined set point. Furthermore, the servo motor effectively controlled the valve mechanism, achieving a maximum opening angle of 85 degrees. Overall, the developed system performed reliably and met the expected operational objectives..

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.

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.

The development of digital technology and internet penetration are driving changes in consumer behavior and demanding that MSMEs not only adapt but also understand the operational motives and marketing channel integration strategies to face competition in the digital era. This study aims to examine the operational motives and technical adaptation processes of Micro, Small, and Medium Enterprises (MSMEs) in Bali in integrating online and offline marketing channels through an omnichannel strategy to respond to intense market competition in the digital era. This descriptive qualitative research uses a multiple case study approach in three different MSME sectors: goods maintenance services, creative services, and fashion retail. Data collection was conducted through direct observation, in-depth interviews, and a review of digital activities, then analyzed through single-case and cross-case stages. The results show that MSMEs optimize digital channels as instruments to expand reach and trigger consumer emotional engagement through a visual storytelling approach, while physical stores are crucially maintained as points of sensory validation. Functional integration has been proven to accommodate the shift in consumer behavior towards a hybrid decision-making pattern. In this pattern, consumers conduct information searches and initial evaluations online, but tend to shift to offline interactions to validate quality and finalize transactions to minimize purchase risk. In conclusion, cross-channel operational synergy has proven essential in boosting the competitive advantage of MSMEs.

The implementation of smoking prohibition policies in Islamic boarding schools continues to depend largely on manual monitoring methods, which often face challenges related to consistency and supervision range. This study aims to design an Internet of Things (IoT)-based cigarette smoke detection system as an alternative monitoring approach that is more effective, measurable, and sustainable. The system design combines an MQ-2 gas sensor with a NodeMCU ESP8266 microcontroller programmed through the Arduino IDE platform. When smoke levels detected by the sensor exceed the predetermined limit, the system automatically triggers a buzzer and LED as warning indicators while simultaneously sending monitoring data to cloud-based platforms such as Firebase or ThingSpeak for real-time observation through web interfaces. The research outputs include a comprehensive system design consisting of system architecture, electronic circuit schematics, flowcharts, and pseudocode that are systematically arranged to support future prototype development and implementation. Through this design, the proposed system is expected to provide an initial technological solution that can enhance the effectiveness of monitoring and enforcing smoke-free regulations within Islamic boarding school environments.

Sea Surface Temperature (SST) is an important parameter in oceanographic studies because it influences climate dynamics, ocean circulation, and marine ecosystems. Continuous monitoring of SST in open sea areas requires a reliable system capable of operating autonomously. This study develops a solar-powered ocean buoy designed to measure sea surface temperature while simultaneously evaluating the performance of a solar panel as the main energy source. The system uses a DS18B20 sensor to measure SST and an INA219 sensor to monitor the voltage, current, and power of the solar panel, while an ESP32 microcontroller functions as the central data processing unit. The results show that sea surface temperature tends to remain relatively stable with small daily variations, whereas the temperature and performance of the solar panel exhibit larger fluctuations due to direct exposure to solar radiation and changing weather conditions. Solar panel performance also shows significant variations in current and power depending on the intensity of sunlight. To analyze the influence of SST variations on solar panel performance, a statistical analysis using Analysis of Variance (ANOVA) was conducted. The ANOVA results, based on the calculated F-value and the significance value (p-value) at a confidence level of α = 0.05, indicate that SST variations have a significant effect on solar panel performance, demonstrating that the proposed solar-powered buoy system can operate autonomously and has potential for long-term SST monitoring in offshore areas.

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.

This research aims to design and develop a prototype wave power plant that utilizes the vertical motion of a buoy as a source of mechanical energy, which is then converted into electrical energy using a recoil starter mechanism. The system is designed to be installed at the stern of a prototype ship. The vertical movement of the buoy caused by ocean waves is transmitted to the recoil starter through a drive rope, producing a stable one-way rotational motion. This rotation is further transmitted to a gearbox to increase rotational speed before driving a DC generator. The electrical energy generated is stored in a 12 VDC battery, supported by a buck–booster converter to stabilize the output voltage. This study employs an experimental engineering approach to evaluate system performance based on empirical test data. The main components of the system include a buoy as a wave energy collector, a recoil starter as the initial rotating mechanism, a DC generator as the electrical energy producer, a buck–booster converter as a voltage regulator, a 12 VDC battery as an energy storage unit, and a monitoring system based on an ESP32 microcontroller integrated with a PZEM-017 sensor. Experimental results show that the recoil starter operates effectively in driving the generator under both no- load and buoy-loaded conditions. Increases in generator rotational speed are directly proportional to increases in output voltage and current. The PZEM-017 sensor demonstrates a high level of measurement accuracy, approaching 100% when compared with a multimeter. Overall, the proposed wave power generation system functions reliably and shows potential for further development as a small-scale alternative renewable energy source.

Waste management remains a critical environmental issue due to the lack of public awareness in separating organic and inorganic waste, resulting in accumulation and environmental pollution This study aims to analyze and evaluate the development of automatic waste sorting systems based on proximity sensors with full-capacity notification using a Systematic Literature Review (SLR) approach.. The proposed system utilizes a combination of sensors, including proximity sensors for material identification and ultrasonic sensors for detecting object presence and bin capacity, integrated with a microcontroller for real-time processing. Additionally, the system is equipped with IoT-based monitoring that allows users to receive notifications when the waste bin reaches its capacity. The research method involves system design, hardware and software integration, and functional testing to evaluate system performance. The results indicate that the system is capable of sorting waste automatically with a high level of accuracy and responsiveness, while also providing real-time monitoring to support waste management operations. The implementation of this system can reduce manual intervention, increase operational efficiency, and promote better waste segregation practices. Furthermore, this study highlights the potential of integrating smart technology into environmental management systems, contributing both theoretically and practically to the development of sustainable waste management solutions.

Diabetic foot ulcer is a disease of the feet of DM sufferers with the characteristics of sensory, motor, autonomic neuropathy as well as macrovascular and microvascular disorders. Diabetic foot injuries are one of the complications that DM sufferers fear because they can result in amputation. Modern Dressing wound treatment with the Moist Wound Healing method is effective in healing ulcer wounds in DM patients. The wound healing method aims to maintain the moisture of the wound by using a moisture-retaining bandage so that wound healing and tissue growth can occur naturally. To determine nursing care in dm patients with leg injuries the application of Modern Dressing wound care using metcovazin and foam in diabetic ulcer patients. This study presents a descriptive study describing the treatment of ulcer wounds in DM patients with Modern Dressing wound treatment using a case study approach. Based on the results of the application of Evidence Based Practice (EBP) dapart, it is concluded that the application of Modern Dressing wound care is more effective than conventional wound treatment. as evidenced by the change in the decreased score in Mrs. N with 3 intervention processes, namely by decreasing the initial score of 30 down to 22 after Modern Dressing wound treatment. Modern dressing is more effective for diabetic foot ulcer care because it accelerates new tissue growth, reduces pain, and lowers the risk of infection.

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.

The rapid development of technology in the era of the Industrial Revolution 4.0 has driven the education sector to continuously adapt to the evolving demands of digital-based industries. One of the key technological innovations supporting this transformation is the Internet of Things (IoT), which enables data acquisition, real-time monitoring, and remote control of systems through internet networks. In response to these developments, a community service program was conducted to enhance the understanding and technical skills of students at SMK Negeri 1 Sindang through the provision and utilization of an IoT Trainer Kit Simulator as a practical learning medium. This activity aimed to bridge the gap between theoretical knowledge and industry-relevant technological applications by introducing students to hands-on IoT system implementation. The program included demonstrations and guided practice on the use of sensors, microcontrollers, and web-based monitoring platforms to simulate real-world industrial scenarios. The results indicate that students showed high enthusiasm and active participation throughout the activity. Moreover, participants were able to grasp the fundamental concepts of IoT systems, understand component integration, and recognize the relevance of IoT applications in supporting automation and digital transformation. Overall, this community service activity contributed positively to strengthening students’ digital competencies and preparedness for the demands of the contemporary industrial and technological landscape.

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

Traditional fermented foods made from glutinous rice play a significant role in Indonesia’s local food system, with fermented glutinous rice known as tape ketan being one of the most popular products. Tape ketan is produced through a fermentation process using traditional starter cultures that involve diverse microorganisms, resulting in distinctive sensory characteristics. This review article aims to examine and synthesize previous studies related to the fermentation process of tape ketan and the characterization of metabolites formed during fermentation. The study employed a qualitative approach with descriptive analysis based on a literature review of relevant national and international scientific journals. The findings indicate that tape ketan fermentation involves starch hydrolysis, sugar fermentation, and the formation of primary and secondary metabolites influenced by fermentation time and microbial composition. Primary metabolites such as simple sugars, organic acids, and alcohol contribute to the basic sensory attributes of the product, while secondary metabolites including phenolic compounds and bioactive components enhance its functional potential. The characterization of these metabolites demonstrates that tape ketan is not only a traditional fermented food but also has promising potential for development as a value-added functional food based on local wisdom.

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.

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.

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.

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.

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.