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Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.

The integration of solar powered IoT irrigation systems in precision agriculture offers a sustainable solution to address water scarcity and enhance crop productivity. By leveraging real time data from soil sensors, weather APIs, and machine learning algorithms, these systems optimize irrigation schedules and improve water use efficiency. This research explores the potential of integrating renewable energy sources, such as solar power, with edge computing in smart irrigation systems to promote sustainable agricultural practices. The study aims to evaluate the performance of the proposed system in terms of water savings, crop yield, energy efficiency, and adaptability to varying climate conditions. Literature Review: Previous studies highlight the importance of smart irrigation systems in reducing water waste and improving crop yield through real time monitoring and automated decision making. However, existing systems often lack the integration of renewable energy and edge computing, which are critical for ensuring sustainability and operational efficiency in rural agricultural settings. The combination of renewable energy with IoT devices offers a promising solution to reduce energy costs and carbon emissions, while edge computing enhances real time data processing, ensuring prompt and accurate irrigation adjustments. Materials and Method: The proposed system integrates solar powered IoT devices, soil moisture sensors, weather data APIs, and edge computing devices to manage irrigation. Machine learning algorithms and evapotranspiration models are used to predict irrigation needs and optimize scheduling based on real time data. The system's performance is evaluated through metrics such as water savings percentage, crop yield improvements, and energy consumption, with a comparative analysis against traditional irrigation methods. Results and Discussion: The results indicate that the system successfully reduces water usage by 30% to 40%, increases crop yield by 25%, and operates with energy autonomy, powered entirely by solar energy. The system's adaptability to varying climate conditions ensures optimal crop growth, even under environmental stresses. The integration of renewable energy and edge computing significantly enhances the sustainability and efficiency of irrigation systems.

This study explores the integration of AI-driven process optimization in Waste-to-Energy (WtE) systems to enhance urban sustainability. The research focuses on designing a gasification-based WtE system, incorporating AI predictive control to optimize energy conversion processes. The AI system adjusts operational parameters in real-time, improving energy conversion efficiency by 25% and reducing carbon emissions by 40%. Additionally, the system's waste-to-energy conversion rate is projected to increase by 20%, and operational costs are expected to decrease by 30%. Data collection and analysis are carried out using advanced sensors to monitor key parameters such as temperature, gas composition, and energy output, which are then processed by machine learning algorithms for predictive analysis. The results show that the AI optimization significantly enhances system performance, offering a sustainable solution for urban waste management. The study highlights the technical and operational challenges of integrating AI into existing WtE systems, including the need for infrastructure upgrades and scalability considerations. It also discusses the socio-economic impacts, including job creation, reduced energy costs, and improved public health. The findings demonstrate the potential of AI-based WtE systems in reducing waste, generating clean energy, and mitigating climate change, positioning them as a viable solution for sustainable urban development.

This study aims to design and develop a prototype of an Internet of Things (IoT)-based land watering monitoring system using the ESP8266 module and soil moisture sensor. This system is designed to help farmers manage watering automatically based on soil moisture conditions, so that they can save water and labor. The process at this time begins with a literature study, hardware and software design, to prototype testing on two soil conditions, namely dry and wet. Data from the sensor is sent in real-time to the IoT platform, if the soil is detected dry, the system automatically activates the water pump. The results of the test show that the sensor works accurately in distinguishing soil conditions, and the system is able to water automatically according to needs. Furthermore, this system can also be monitored and controlled remotely via the internet. The conclusion of this study shows that the use of IoT technology is very potential to increase efficiency and effectiveness in agricultural land management.

Traditional markets are characterized by fast-paced and diverse transactions, necessitating a reliable product monitoring system to enhance the efficiency of stock management and transactions. This study develops a monitoring system based on a loadcell sensor and a TCS230 color sensor to automatically classify product weight and type. The loadcell is used to measure product weight with high accuracy, while the TCS230 detects the color characteristics of products to distinguish between different types of commodities, such as various varieties of chili peppers. The development process includes sensor calibration, dataset collection, and the training and evaluation of a classification model. Experimental results show that the classification accuracy exceeds 90%, demonstrating the effectiveness of combining weight and color data for market product recognition.

The high rate of motorcycle theft in Indonesia is a serious issue requiring an innovative, technology-based solution. This study aims to design and develop a motorcycle security system that integrates biometric authentication using a fingerprint sensor, real-time GPS tracking, and remote control based on the ESP32 microcontroller. The system allows users to start or stop the engine using registered fingerprints only, monitor vehicle location via GPS Tracker EV02, and control the motorcycle remotely through an internet-based application. Test results show that the system can accurately recognize fingerprints within 2 seconds, activate a buzzer for one minute upon unauthorized access attempts, and display the vehicle's location with an accuracy of 3–12 meters. Additionally, the use of the waterproof R503 fingerprint sensor ensures the system functions reliably in wet conditions. This integrated system has proven effective in enhancing the security of two-wheeled vehicles and offers a viable alternative to traditional security mechanisms.    

Aquaculture is a vital sector in global food production, providing essential protein sources. However, the industry faces significant challenges, including high energy consumption and environmental impact. The integration of renewable energy, particularly solar power, with automation and IoT systems offers a promising solution to enhance energy efficiency, sustainability, and productivity in aquaculture operations. This study aims to evaluate the effectiveness of solar powered autonomous systems in reducing energy usage, improving operational efficiency, and promoting environmental sustainability in aquaculture. Literature Review: Recent research has explored various technologies, such as Digital Twins (DTs) and Precision Fish Farming (PFF), which integrate IoT sensors for real time monitoring and optimization of fish farming operations. The combination of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, has further advanced the industry by enabling automated decision making and predictive analytics. Solar power integration with IoT systems has been shown to significantly reduce operational costs, minimize carbon emissions, and enhance the sustainability of aquaculture practices. These advancements have the potential to address the challenges of energy consumption and environmental degradation in the industry. Materials and Method: This research utilizes a hybrid solar powered IoT system for aquaculture, integrating solar panels, IoT sensors, and automated control systems. The system monitors key water quality parameters, such as pH, dissolved oxygen, turbidity, and temperature, to maintain optimal conditions for aquatic life. Data is collected through IoT sensors and analyzed through a cloud-based platform. A pilot study is conducted on a small scale aquaculture farm to evaluate the system's performance, including energy consumption, water quality management, and fish health. Energy savings, operational efficiency, and environmental impact are assessed. Results and Discussion: The integration of solar powered IoT systems significantly reduced energy consumption compared to traditional systems, with a notable decrease in grid electricity reliance. The system successfully maintained optimal water quality conditions, enhancing fish health and growth. Solar powered systems proved reliable, even in regions with variable sunlight, and demonstrated improvements in operational efficiency through automation. The environmental benefits were evident, with a reduction in carbon emissions and lower operational costs. The study highlights the feasibility of solar powered IoT systems as a sustainable solution for modern aquaculture operations.

This research aims to design a Plasma Blood Separator that can separate plasma from red blood cells automatical-ly using an Optocoupler sensor and a specimen tube cutter. This sistem is equipped with an Arduino Uno as the microcontroller, a servo motor to clamp the hose, a servo to open the blood bag pressure, a series of push buttons to open the servo, and a buzzer to indicate the completion of separation After testing the Arduino Uno-based Plasma Blood Separator, all functions can work well by showing the results of three experiments that varied the length of the hose. The length of the hose that was varied was 30 cm, 50 cm, 70 cm. The estimated separation time was 20 seconds, 30 seconds, and 40 seconds for mode 1, The estimated separation time was 40 seconds, 60 seconds, and 80 seconds for mode 2,  In the results of the measurement of the voltage of the tool, the measurement results were obtained with a small percentage of error with a value of <5% and a high accuracy value with >90%..

This study aimed to evaluate the effect of varying honey concentrations on the antioxidant activity, chemical properties, and organoleptic quality of a probiotic kombucha beverage prepared from Clitoria ternatea L. The research was motivated by increasing exposure to free radicals due to environmental pollution and the demand for alternative natural antioxidants. Kombucha was produced with honey concentrations of 10%, 20%, and 30% (w/v), and analyses were performed using the DPPH assay for antioxidant activity, measurements of pH, total acidity, and total phenolic content for chemical properties, as well as sensory evaluations by trained panelists. The results showed that kombucha with 10% honey exhibited the lowest IC₅₀ value, indicating the highest antioxidant activity, while higher concentrations increased the total phenolic content without significantly enhancing the effective antioxidant potential. These findings suggest that excessive honey addition does not necessarily improve the functional benefits of the product, indicating the need for further formulation optimization to develop a high-quality, marketable probiotic beverage.

Efficient and accurate fuel management systems are becoming increasingly important as energy needs and operational complexity increase in various industrial sectors. This study aims to design and implement a fuel flow monitoring and control system using a variable control valve. This system allows real-time monitoring of the level and flow of fuel in the service tank on the ship, as well as automated control. In its implementation, ultrasonic sensors, control valves are combined with LM2596 connected to Arduino nano for data collection, analysis and visualization. The test results show that this system is able to provide accurate data on the flow and level of fuel in the service tank on the ship by displaying the measurement results on the LCD and providing warnings via a buzzer. The LCD display shows the reading of the fuel level and valve opening in the tank. At a fuel level of 0 - 25% the valve will open 100% and requires an average filling time of 25 seconds, At a fuel level of 25% - 50% the valve will open 75% and requires an average filling time of 27 seconds, At a fuel level of 50 - 75% the valve will open 50% and requires an average filling time of 30 seconds, At a fuel level of 75 - 99% the valve will open 25% and requires an average filling time of 32 seconds.

The increasing waste problem requires innovative solutions that are efficient and sustainable. This study aims to design and build an Internet of Things (IoT) based garbage cleaning robot that can be monitored and controlled remotely. This system is designed by utilizing a microcontroller as the main brain, sensors to detect the presence of garbage, and an IoT-based communication module that allows monitoring and control of the robot via mobile devices or the web. Based on the results of the analysis and testing carried out, this study shows that the use of ESP8266 in the RC Trans Robot motor control with the Blynk application has succeeded in significantly increasing system efficiency. The system successfully responds to user input via a mobile application and can control the movement of the robot in real-time. The performance of the robot system controlled via a WiFi network shows good stability in various test scenarios. The robot can operate effectively within a distance that matches the range of the WiFi network, with fast control response and reliable communication.

The 2011 Tohoku tsunami stands as one of the most catastrophic natural disasters in Japan’s history, resulting in massive loss of life, extensive property damage, and severe disruption to infrastructure along the northeastern coast. This study aims to examine the physical characteristics of the Tohoku tsunami and critically evaluate the factors that led to the failure of Japan’s disaster mitigation systems. Utilizing an essay-based synthesis approach, this analysis draws from a range of previous research findings and post-disaster assessments. The tsunami was caused by a 9.0 magnitude megathrust earthquake off the eastern coast of Honshu, generating wave heights that exceeded 10 meters in several locations. The tsunami’s immense energy produced destructive currents that penetrated deep inland, surpassing the protection capabilities of existing coastal defenses.The study identifies several contributing factors to the mitigation system’s shortcomings: the design limitations of seawalls, which were not built to withstand such extreme events; delays and limitations in the early warning system’s ability to reach all at-risk populations in time; and inadequate public preparedness, as the event exceeded historical precedents and training scenarios. These vulnerabilities highlight the need for integrated, adaptive disaster risk management strategies.Drawing lessons from Japan’s experience, this study proposes recommendations for improving tsunami mitigation efforts in Indonesia. Key suggestions include reinforcing and redesigning coastal defense structures, advancing early warning technologies such as seabed sensors and coastal radar systems, and enhancing disaster literacy through public education and scenario-based training. By adopting a science- and technology-driven approach, Indonesia can strengthen its resilience and reduce vulnerability among coastal populations facing future tsunami threats.

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.

Overhead cranes are an important device in the manufacturing and construction industry that functions to move heavy loads efficiently and safely. However, human error often occurs which can cause work accidents, equipment damage, and decreased productivity. Therefore, this research aims to design and build an Arduino-based automatic overhead crane system that can increase work efficiency and safety. This system is designed using an Arduino microcontroller as a control center, equipped with an ultrasonic sensor to detect the position and distance of the load, as well as a servo motor to regulate crane movement automatically. The system also features a wireless communication module to monitor and control the crane remotely. The research method used is Research and Development (R&D), which includes the stages of needs analysis, system design, hardware and software implementation, and performance testing. Test results show that this automatic overhead crane is capable of moving loads with high accuracy and a speed that can be adjusted according to needs. The system also succeeded in reducing manual intervention, thereby increasing work safety and reducing the risk of accidents by up to 30% compared to manual systems. The Arduino-based automatic overhead crane design succeeded in meeting the research objectives, increasing work efficiency and safety. Routine maintenance and periodic checks on mechanical and electronic components need to be carried out to ensure the system continues to function optimally and reduces the risk of damage.

Benign paroxysmal positional vertigo (BPPV) is a common cause of peripheral vestibular dysfunction, often resolving with canalith repositioning maneuvers (CRM). However, residual dizziness and imbalance may persist, impairing functional mobility. This case report describes a 55 year old female diagnosed with right posterior canal BPPV through a positive Dix Hallpike test. Following successful CRM, she reported residual postural instability and difficulty walking. A structured four week vestibular rehabilitation therapy (VRT) program was implemented, consisting of habituation (Brandt Daroff), adaptation (gaze stability, VOR x1, VOR x2), sensory substitution, and progressive balance training, delivered three times weekly for 45 minutes per session. Balance and mobility were assessed using the Berg Balance Scale (BBS) and Timed Up and Go (TUG) test at baseline and after the intervention. Results showed improvement in BBS from 42/56 to 53/56 (+11 points) and a reduction in TUG from 14.8 to 9.6 seconds (−5.2 seconds), both exceeding minimal detectable change values for older adults. The patient reported complete resolution of dizziness, increased walking confidence, and return to independent daily activities without adverse effects. This case suggests VRT as a valuable adjunct to CRM in BPPV patients with residual functional deficits, warranting further research in larger controlled studies.

Traffic congestion and limited parking spaces in the tourist area of Jalan Braga, Bandung City, have become significant issues that require innovative solutions. This study aims to design and implement a Smart Parking System using Internet of Things (IoT) technology and a mobile application to enhance efficiency, transparency, and convenience in parking management. The system is designed to detect real-time parking availability using ultrasonic sensors integrated with microcontrollers, and deliver this information to users through an Android-based application. Additionally, the system features parking slot reservation, digital payment, and parking data management for operators. Test results show that the system can reduce the time needed to find parking by up to 40% and increase user satisfaction. The implementation of the Smart Parking System in Jalan Braga is expected to support smarter and more environmentally friendly urban mobility in Bandung City.

Oka Jamur Bali is one of the oyster mushroom cultivation farmers. Lack of efficiency in regulating temperature and humidity in mushroom barns is an important factor in the growth of mushroom fruiting bodies. This condition requires an innovative solution to regulate and monitor temperature and humidity in the barn area. This research aims to build a prototype using Arduino UNO, ESP32, DHT11, pump and Internet of Things (IoT) technology.  The monitoring system design uses Arduino UNO, NodeMCU ESP32, DHT11 and DC pump with 12V voltage. Arduino UNO functions as a control system to manage and control the temperature and humidity parameters of the mushroom barn environment with the DHT11 sensor used to accurately measure temperature and humidity. The combination of NodeMCU ESP32 and Arduino UNO aims for monitoring through the ThingSpeak platform which can be accessed with a smartphone connected to the internet network. Based on the results of the prototype of the automatic temperature and humidity control system, the system is able to measure the environmental conditions of the mushroom barn with humidity measurement results in the range of 79% - 82% and the average temperature during the test reached 29.35°C. The application of the system was able to reduce the temperature by 2.28% with an average temperature of 28.19°C and increase the relative humidity by 6.27% with an average humidity of 81.1% in the mushroom barn area. The results of income between the income generated by using the automatic temperature and humidity control system tool and the income of farmers without using the tool increased by Rp. 16,416.

This research develops a prototype of an automatic watering device based on the Internet of Things (IoT) and a soil moisture sensor to improve the efficiency of watering crops in agricultural fields. The main problem addressed is the consistency and efficiency of watering in large agricultural fields, where traditional management is prone to human error and inefficient water use. The developed prototype uses an ESP8266 microcontroller (NodeMCU) as the brain of the system connected to a Wi-Fi network, allowing remote monitoring and control via the Blynk application. A soil moisture sensor is used to detect soil conditions in real-time, triggering watering only when needed. This helps optimize water use and increase agricultural productivity sustainably. This research outlines the methodological steps from design, manufacturing, to implementation of the prototype in the field. Evaluation of the results shows that the system successfully regulates watering effectively based on soil conditions, with positive responses from farmers as end users.

Along with the development of the industrial sector, companies are competing to create quality and affordable products. To support production, automation tools have emerged that help the production process so that it is faster. One of the machines that is widely used by large factories is a 3-phase induction motor. 3-phase induction motors are often used in driving equipment in industry because they have a simple construction, are relatively cheap, lightweight, have high efficiency, and are easy to maintain. This study utilizes the A3114 hall effect sensor device, PZEM-PP4T Sensor, 20x4 LCD, ESP32 module, Relay, Mitsubishi D700, MCP 4725 Sensor. The tests carried out showed that the performance of the sensor for measuring speed, voltage, and current on 3-phase motors has a high level of accuracy. Testing of motor speed shows a difference in speed value (Rpm) between the hall effect sensor and the tachometer, with an average accuracy percentage of 4,12%, indicating that the hall effect sensor has a fairly good level of accuracy. For voltage measurement, the Pzem sensor compared to the Multitester produces an average accuracy percentage of 0,90%, indicating that the pzem sensor is very reliable. Meanwhile, current testing shows a difference in value between the pzem sensor and the multitester with an average accuracy percentage of 0,98%, indicating a fairly high level of accuracy. Overall, this system can be relied on for measuring operational parameters of 3-phase motors because it has consistent and good accuracy. With this research, the 3-phase induction motor monitoring system which was originally done manually has changed to automatic and has a fairly high level of work efficiency.

The reliability of a data center is highly dependent on its air conditioning and cooling system. This research evaluates the existing cooling system of Universitas Krisnadwipayana’s data center using the PPDIOO Network Life-Cycle approach. The study finds that the current cooling system, which relies on AC Split, fails to meet TIA-942 standards, posing significant overheating risks and increasing downtime probability. Observational analysis shows that the cooling distribution is inefficient due to inadequate airflow and the absence of a structured cooling layout. To address these issues, this research proposes an optimized cooling system design that incorporates Computer Room Air Conditioning (CRAC), hot aisle-cold aisle arrangement, and raised floor implementation. The recommended improvements also include installing temperature and humidity sensors for real-time environmental monitoring and implementing N+1 redundancy for enhanced system reliability. These solutions are expected to improve cooling efficiency, reduce energy consumption, and mitigate downtime risks. Future research should focus on evaluating the practical impact of this design by conducting real-world trials and exploring liquid cooling technology as a potential alternative for further efficiency improvements.