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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.

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.

Catfish farming is one of the fisheries areas with great potential in Indonesia. However, the problem of feed efficiency, use of probiotics, and air temperature control are still the main obstacles that affect the health and growth of fish. This study aims to design and implement an automatic system that can regulate the provision of feed and probiotics and control and control air temperature directly using the ESP32 microcontroller. This system has several important components, such as the ESP32 microcontroller as a control center, servo motors for feed and probiotic distribution, and DS18B20 temperature sensors to monitor air temperature. All components can be controlled and viewed through an IoT-based application with a Wi-Fi connection or applications such as Telegram. This trial aims to determine whether this system can provide feed and probiotics according to the specified time, and maintain air temperature in the optimal range (28°C–40°C) for catfish growth. In this way, the system is expected to increase efficiency in catfish farming automatically, reduce the need for human labor, and minimize errors in pond management.

This community service program was carried out to enhance the technological literacy of residents in Telang Sari Village through the introduction of an automated street lighting system based on sensor technology. The system presented to the community utilizes an Arduino microcontroller integrated with an LDR sensor to detect light intensity and an ultrasonic sensor to identify the presence of nearby objects. With this configuration, the street lights operate automatically: they turn on when the environment becomes dark and an object is detected, and turn off when the surroundings are bright or no activity is detected in the sensing area. The program activities included device installation, technical explanation, and a live demonstration to ensure that residents comprehended its functions and benefits. Additionally, the use of solar panels was introduced as an alternative power source to support sustainable operation without relying on grid electricity. The results of the program showed a positive response from the community, as the system was considered effective in improving nighttime safety, reducing energy consumption, and requiring minimal maintenance. Overall, this activity successfully increased public understanding of automation technology and renewable energy applications suitable for rural community development.

This community service program was carried out to enhance the technological literacy of residents in Telang Sari Village through the introduction of an automated street lighting system based on sensor technology. The system presented to the community utilizes an Arduino microcontroller integrated with an LDR sensor to detect light intensity and an ultrasonic sensor to identify the presence of nearby objects. With this configuration, the street lights operate automatically: they turn on when the environment becomes dark and an object is detected, and turn off when the surroundings are bright or no activity is detected in the sensing area. The program activities included device installation, technical explanation, and a live demonstration to ensure that residents comprehended its functions and benefits. Additionally, the use of solar panels was introduced as an alternative power source to support sustainable operation without relying on grid electricity. The results of the program showed a positive response from the community, as the system was considered effective in improving nighttime safety, reducing energy consumption, and requiring minimal maintenance. Overall, this activity successfully increased public understanding of automation technology and renewable energy applications suitable for rural community development.

Energy efficiency in water heaters is a crucial factor in ship operational environments due to limited electricity resources that rely on generators. This study aims to design and build an IoT-based water heater monitoring system with an innovative heat storage medium in the form of a mixture of silica sand and paraffin wax to improve thermal efficiency. Although previous studies have developed temperature monitoring and control systems in IoT-based water heaters, this study specifically fills this gap by analyzing the performance of adding silica sand to overcome the low thermal conductivity of paraffin wax. Using the Research and Development (R&D) method, this system was built with an ESP32 microcontroller as the control center, a DS18B20 temperature sensor for accurate measurements, and the Blynk and Google Sheets platforms for real-time monitoring and data recording. Performance testing was conducted by comparing the water heating rate between pure paraffin wax media and the mixed media. The results showed that the monitoring system functioned reliably, and the main finding proved that the addition of silica sand to paraffin wax significantly increased heating efficiency. This was clearly seen from the reduction in time required to raise the water temperature to 40°C, from 2.5 hours to only 1 hour in the second heating cycle. The results of this study indicate that the integration of silica sand and paraffin wax media with IoT technology can increase the efficiency of water heaters and provide an innovative solution for energy-efficient and environmentally friendly temperature control.

Cracker drying process is considered inefficient because it still depends on weather factors and manual supervision, which can affect the quality of the production results. To overcome this, a smart shrimpcrackerdryingdevicebasedonan Arduino Uno microcontroller with an automatic closing mechanism and an automatic container has been designed and built. This system is equipped with a rain sensor and an LDR sensor to detect weather conditions around the device, as well as a DHT11 sensor to monitor humidity in the cracker storage container. In addition, an ultrasonic sensor is used to automatically regulate the movement of crackers into the storage container after the drying process is complete. A DC motor controlled by a BTS7960 driver functions to move the cover roof and the cracker transfer mechanism, while a limit switch is used to limit the motor movement.Based on the test results, this device can work automatically Shrimp crackers are one of the typical food products widely produced by the people of Bunga Karang Village, Tanjung Lago District, Banyuasin Regency. The traditional shrimp following changes in environmental conditions, thereby increasing the efficiency of the drying process and maintaining the quality of shrimp crackers produced by the residents of Bunga Karang Village.

Flood is a frequent natural disaster in Indonesia, often exacerbated by delays in information delivery from conventional manual monitoring systems. This study aims to design and build an automatic flood early warning system based on the Internet of Things (IoT) integrated with a web dashboard and WhatsApp notifications. The system utilizes the ESP32 microcontroller and MaxBotix MB7389 ultrasonic sensor for real-time water level data acquisition. The Research and Development (R&D) method was employed, focusing on device accuracy validation and system integration. The measured data is transmitted to a .NET-based server and stored in a PostgreSQL database. Results indicate that the system functions according to design. Sensor accuracy testing demonstrated a high degree of precision (99.12% - 100%) compared to manual measurements. Furthermore, the system successfully sends timely warning notifications via WhatsApp when water levels exceed predefined thresholds, providing a more effective solution than manual monitoring to enhance community preparedness.

Penelitian ini bertujuan untuk mengembangkan sistem telemetri guna memantau kinerja panel surya pada beberapa lokasi dengan menggunakan komunikasi jarak jauh dan platform Internet of Things (IoT). Metode pemantauan konvensional memiliki keterbatasan dalam menyediakan data secara real-time pada area yang luas, sehingga evaluasi kinerja jarak jauh menjadi kurang efisien. Untuk mengatasi permasalahan tersebut, dirancang sebuah sistem pemantauan menggunakan mikrokontroler ESP32, sensor INA219 untuk mengukur tegangan dan arus, modul GPS Neo-M8 untuk identifikasi lokasi, modul Real-Time Clock (RTC) DS3231 untuk pencatatan waktu, serta modul LoRa RA-02 sebagai media komunikasi nirkabel. Setiap node pengirim dilengkapi dengan modul MicroSD untuk menyimpan data pengukuran secara lokal. Data hasil pengukuran dikirimkan melalui LoRa ke unit penerima dan ditampilkan secara real-time pada platform Thinger.io. Hasil kalibrasi menunjukkan bahwa sensor INA219 memiliki rata-rata galat pengukuran arus sebesar 0,71% dan galat pengukuran tegangan sebesar 0,1%. Pengujian GPS menunjukkan koordinat lokasi yang stabil dengan tingkat akurasi sekitar ±3 hingga ±8 meter. Seluruh data pengukuran berhasil dikirim, disimpan, dan ditampilkan tanpa kehilangan data yang signifikan. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu menyediakan pemantauan parameter panel surya secara jarak jauh yang andal dan efisien dalam kondisi lapangan.

The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.

The rapid advancement of the Internet of Things (IoT) has spurred the development of intelligent home systems that optimize energy use and enhance user convenience. One essential component of a smart home environment is an adaptive lighting system that can operate based on environmental conditions. This study aims to design and implement a microcontroller-based automatic lighting system that responds to variations in ambient light intensity and supports further integration with IoT-based monitoring. The system is built using an Arduino Atmega328P microcontroller, a Light Dependent Resistor (LDR) sensor for illumination detection, and a relay module to control the electrical load. This research employs an experimental design that includes system analysis, hardware assembly, software development, and performance testing. The LDR sensor was tested under bright and dark conditions, producing consistent readings, with an average threshold of 450–500 analog units for light and below 300 units for dark. The system successfully activated the lamp in 100% of dark-condition trials (n=10) and turned it off correctly in all bright-condition trials (n=10). Response time measurements showed that the system processed changes in light intensity in less than 1 second, ensuring real-time adaptability. The findings demonstrate that the proposed design operates reliably, optimizes electricity usage, and reduces the need for manual intervention. This study contributes to the development of affordable, energy-efficient, innovative lighting systems. It provides a practical foundation for future work, including full IoT integration with wireless modules for remote control and monitoring.

Household waste management remains a critical challenge in creating clean and sustainable residential environments. Manual monitoring methods often lead to delayed waste collection, overflowing bins, and operational inefficiency. This community engagement program implements an Internet of Things (IoT)-based Smart Trash Monitoring SIstem (STMS) at Graha Panyindangan Estate, Indramayu. The sIstem utilizes ultrasonic sensors and ESP32 microcontrollers to detect the fill level of household waste containers and transmit data in real time to a user-accessible dashboard. The results demonstrate that the STMS provides accurate waste-level information, reduces manual inspections, enhances collection efficiency, and fosters data-driven behavior among residents. Social changes also emerged, including improved environmental awareness, new social norms, and the emergence of local leaders supporting sIstem sustainability. This program contributes to the realization of smart living in modern residential communities, ensuring better waste management, cleaner environments, and more sustainable practices for future generations. It also provides valuable insights into the role of technology in addressing environmental issues at the community level.

This study discusses the design and development of an automatic safety system for a wood cutting machine using Arduino Uno, a PIR (Passive Infrared) sensor, and a servo motor as the main components. The system is designed to automatically stop the movement of the wood cutting machine when human motion is detected around the cutting area, thereby minimizing the risk of work-related accidents. The research method includes hardware design, microcontroller programming, and system response testing using two types of test objects: the human body and a wooden block. The results show that the system operates according to the programmed logic. When the PIR sensor detects human motion, the servo motor stops and the red LED lights up as a danger indicator. In contrast, when no human motion is detected, the servo motor continues to move normally and the green LED remains on as a safe indicator. The system’s average response time is 0.6 seconds, indicating a fast and accurate performance. Therefore, the designed system is considered effective and can serve as a prototype of a simple safety tool to enhance operator safety in wood cutting machines.

The main problem with the water heating system on offshore platforms is the absence of water level monitoring and automatic overflow detection. This has the potential to cause hot water spills that endanger workplace safety and operational efficiency. This research designs and implements a water level monitoring system based on the Arduino Uno microcontroller with HC-SR04 ultrasonic sensors. The system is equipped with LED indicators, a buzzer alarm, and a 16x2 LCD to display water level status in real-time. Water levels are classified into three zones (low, medium, high), and overflow is detected if the water is within 3 cm of the sensor. Testing was conducted on a 5-liter simulation tank representing actual 500-liter tank conditions. Test results showed a reading accuracy of 96% and a quick system response to overflow conditions (<1 second). This system is economical, easy to develop, and highly applicable for offshore environments. In addition, this system can be integrated with IoT technology for remote monitoring.

The distinctive markings on the fingers are known as biometrics. The fingerprint attendance method uses biometrics. Without developing a usable fingerprint tool, research on fingerprint attendance is restricted to processing attendance data. As a result, an Arduino-based fingerprint approach was used to construct an attendance tool. The education sector is where SMK Negeri 1 Indralaya Utara. The school's student attendance system is still operated manually. In this study, the system uses fingerprints and camera recording to track students' locations. Using an Arduino Mega as a microcontroller, this tool will display student attendance information on an LCD, save it on an SD card module, and send the ESP32CAM-generated photographs over Telegram. The literature review is the first step in the research process, followed by hardware and software design. Following completion, the tool is put through testing to evaluate its functionality. Each of the ten participants scanned their thumb and index finger. The thumb and index finger have an average of five tests, with a 0.28% mistake rate, depending on the number of trials. Each component's error percentage is less than 1% while still falling within the 5% tolerance limit. The image test results indicate two conditions: 20 cm and greater. The ESP32CAM will activate when we take a picture at a distance of 20 cm.

Motorcycle theft remains a widespread issue that demands improved security solutions supported by modern technology.This research discusses the development and implementation of a motorcycle security system design using an ESP8266 microcontroller integrated incorporating the GPS Neo-7M positioning module, SW-420 vibration detection sensor, and an electronically controlled solenoid lock. The  implemented mechanism is monitored and controlled via the Telegram application, allowing the owner to receive real-time updates about the motorcycle’s status through a Telegram Bot. The development process involves hardware integration, ESP8266 programming, and functional testing. The SW-420 sensor detects vibrations as indicators of possible theft, triggering a buzzer alarm and sending instant notifications through Telegram. The GPS Neo-7M module provides location data in real time, automatically or upon user request, through a Google Maps link. Users can also lock or unlock the motorcycle remotely by sending the lock or /unlock commands via Telegram. Test results show that the system responds to vibrations in less than one second, delivers Telegram notifications within 3–5 seconds, and determines location with an accuracy of 3–10 meters. Overall, the proposed system offers an effective, practical, and low-cost solution to enhance motorcycle security.

This study reports the green synthesis of vanadium pentoxide (V₂O₅) using virgin olive oil as a natural and environmentally benign reducing agent. The approach aims to minimize the environmental impacts associated with conventional synthesis routes. Structural and physicochemical characterizations confirmed the successful formation of nanoscale V₂O₅. X‑ray diffraction (XRD) analysis indicated an average crystallite size of approximately 16.57 nm, evidencing high crystallinity. Fourier‑transform infrared spectroscopy (FTIR) revealed characteristic V=O and V–O–V vibrations with bands associated with physisorbed water, confirming the correct oxide framework. Field‑emission scanning electron microscopy (FE‑SEM) showed irregularly shaped nanoparticles with a representative particle diameter of ~32.62 nm. Brunauer–Emmett–Teller (BET) and Barrett–Joyner–Halenda (BJH) analyses yielded a specific surface area of 10.817 m²/g, a total pore volume of 0.024277 cm³/g, and a broad mesoporous distribution (20–90 nm). Energy‑dispersive X‑ray spectroscopy (EDX) confirmed the purity of V₂O₅ with weight fractions of V (69.40%) and O (30.60%), consistent with the stoichiometric composition. Overall, the results demonstrate the effectiveness of olive oil as a green reducing agent for preparing nanoscale V₂O₅, which is promising for catalysis, energy storage, sensors, and clean‑energy applications.

The braking system is a crucial component in a vehicle, where its performance is highly influenced by the wheel's rotational speed and the geometry of the brake pad. This study aims to analyze the impact of wheel rotational speed and variations in pad geometry on the temperature of the brake pad. The braking process generates heat due to friction, which, if not properly managed, can reduce braking performance and accelerate brake pad wear. The experiment was conducted at four levels of wheel rotational speed: 1000 RPM, 1500 RPM, 2000 RPM, and 2500 RPM. The testing system was designed using a braking system simulator equipped with a speed sensor (LM393) and a temperature sensor (K-type thermocouple), which were connected to an Arduino microcontroller and displayed in real-time through a Graphical User Interface (GUI) in MATLAB. The test results indicated that both the geometric shape of the brake pads and the wheel rotation speed significantly affected the resulting temperature. Standard brake pads produced the highest temperature at a speed of 2500 RPM, reaching 63.33°C. In contrast, brake pads with holes offered the best performance by maintaining a lower temperature of only 43.00°C. Furthermore, an increase in wheel rotation speed led to a noticeable rise in temperature; for standard pads, the temperature increased from 36.67°C at 1000 RPM to 63.33°C at 2500 RPM. This demonstrates that RPM is a major factor in heat generation due to friction. The MATLAB GUI effectively visualized the relationship between RPM and temperature, facilitating the analysis and evaluation of the data.

Waste management remains a major challenge, particularly due to the lack of public awareness and habits in sorting waste from its source. Innovative technology-based solutions are needed to support more effective waste management systems. This study aims to design and develop an Internet of Things (IoT)-based smart trash bin capable of automatically sorting metal and non-metal waste. The system utilizes an ESP32 microcontroller as the main controller, an LJ12A3-4-Z/BY sensor to detect metal materials, an E18 sensor to detect non-metal materials, and an ultrasonic sensor to monitor bin capacity. The waste separation mechanism is operated by a servo motor controlled by the system, while the Blynk application is integrated for real-time monitoring and full-capacity notifications sent directly to the user’s smartphone. The research stages included hardware design, microcontroller programming, IoT platform integration, and functionality testing to ensure system performance and reliability. The results showed that the smart trash bin operated as expected, successfully identifying and separating metal and non-metal waste automatically, while also sending real-time notifications to the user when the bin approached full capacity. The implementation of this technology has the potential to modernize waste management processes, improve the efficiency of waste sorting, and reduce dependency on manual labor. Furthermore, this research opens opportunities for future development by integrating additional sensors and cloud-based data management systems to support smart city initiatives and sustainable waste management practices.