Fire is one of the incidents that disturbs homeowners because of the fire that will drain property and can claim lives when there is a lack of anticipation and minimal ignorance of the incident, such as when a fire occurs there is no early warning to the homeowner, this is also a cause of fire. The cause of the fire can be caused by a gas leak, electrical short circuit or caused by the community itself. Based on these problems, a solution is needed to create a safety system technology to be applied to the home kitchen, namely the design of an early detection system for fires based on the Internet Of Things (IoT) using ESP8266 which is useful for monitoring kitchen conditions and will automatically send a notification of the kitchen condition if the sensor reads an incident. The Hardware Design uses several tools consisting of ESP8266, Arduino R3. Jumper Cables, Fire Sensors, MQ-2 Gas Sensors, Mini Fans, Mini Water Pumps, Relays, Buzzers. and for the software design using Arduino IDE for system programming and Blynk as an application to display notifications. The designed system will be tested using black box testing which is used to determine whether the designed system will meet the specified parameters or not, and the results of the design state that the system is in accordance with the parameters used.

Facial Emotion Recognition (FER) is a key technology for identifying emotions based on facial expressions, with applications in human-computer interaction, mental health monitoring, and customer analysis. This study presents the development of a real-time emotion recognition system using Convolutional Neural Networks (CNNs) and OpenCV, addressing challenges such as varying lighting and facial occlusions. The system, trained on the FER2013 dataset, achieved 85% accuracy in emotion classification, demonstrating high performance in detecting happiness, sadness, and surprise. The results highlight the system's effectiveness in real-time applications, offering potential for use in mental health and customer behavior analysis.

This research focuses on evaluating the Combox web application, a digital tool designed to help Food and Beverage (F&B) business owners strengthen their online presence. The analysis was carried out through Black Box Testing, specifically using the Equivalence Partitioning method, to assess core functionalities like login, logout, product management, and pagination. The findings reveal that while most features function as intended, there are issues with product addition and editing, as well as pagination when no data is available. These results highlight areas that need refinement to improve the application’s reliability and user experience. In summary, this research supports the advancement of a digital platform that enables F&B businesses to harness technology effectively in today’s competitive landscape.

Smart grids incorporate IoT devices that enhance energy management, monitoring, and overall grid efficiency. However, this interconnectivity also increases vulnerability to cybersecurity threats, posing risks to critical infrastructure. This research investigates the implementation of blockchain technology to secure data transactions within IoT-based smart grids. By leveraging blockchain's decentralized, tamper-resistant characteristics, the study demonstrates improvements in data integrity and cybersecurity for smart grids, providing a potential framework for resilient and secure energy infrastructures.

CV. Aren Jaya is an AC service provider located in South Tangerang. In 2017 this shop began to develop by opening new and used AC sales services, AC installation services, used washing machine sales services and washing machine service services. In carrying out the company's business process activities, of course there will always be possible risks and potential risks that can threaten and disrupt business process activities. The need for a risk analysis of existing IS/IT resources in the company, using the ISO 31000:2018 method related to risk management. The purpose of this study is to minimize all possible risks that are currently being experienced or will also occur and provide appropriate recommendations regarding risks that may occur at any time. The research method used in the risk analysis uses the ISO 31000:2018 framework. The results of this risk analysis are in the form of an analysis of the possibility of existing risks, evaluations and risk mitigation plans so that they can produce improvements to existing risks. The final result of the research produces risk recommendations, so the company can adjust to the priorities of the existing risk level, so that it does not interfere with business activities at CV. Arena Jaya.
 

Cloud-based systems are increasingly vulnerable to a range of cybersecurity threats, driving the need for advanced encryption methods. This paper investigates the potential of quantum cryptography in securing cloud environments, focusing on the use of quantum key distribution (QKD) protocols. By leveraging the principles of quantum mechanics, the study demonstrates significant improvements in data security, offering enhanced protection against eavesdropping and paving the way for more resilient cloud security frameworks.

Power systems are critical infrastructure that face significant challenges due to increasing demand and inherent complexity. Predicting failures in power systems is crucial for enhancing grid reliability, minimizing downtime, and optimizing maintenance processes. This study evaluates various deep learning models, specifically convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models, for predicting power system failures. By analyzing these models’ performance metrics on historical power grid data, the study provides insights into the strengths and weaknesses of each approach. The findings contribute to the development of more robust predictive models for power system reliability.

Accurate image segmentation is a pivotal process in medical imaging, essential for supporting diagnosis, treatment planning, and monitoring disease progression. This study evaluates the effectiveness of machine learning algorithms, including U-Net, Fully Convolutional Networks (FCNs), and Mask R-CNN, in achieving high-precision segmentation of medical images. Experimental results demonstrate that these models significantly enhance segmentation accuracy, enabling more precise diagnostic outcomes in clinical settings and advancing the development of automated medical imaging technologies.

Energy efficiency is a critical concern in wireless sensor networks (WSNs) due to the limited power resources available in sensor nodes. Prolonging network lifespan while ensuring reliable data transmission is essential for successful deployment in various applications, such as environmental monitoring, military operations, and industrial automation. This paper presents a mathematical model designed to optimize energy consumption across various nodes in WSNs. By implementing simulations and analyzing data from these models, the study demonstrates significant improvements in extending network lifespan while maintaining reliable data throughput. The findings contribute valuable insights into energy management for large-scale sensor deployments.

Industrial IoT (IIoT) networks, critical for automation and smart manufacturing, are susceptible to faults due to their complexity and the large number of connected devices. This paper introduces a deep learning-based approach for early fault detection in IIoT networks. By leveraging recurrent neural networks (RNNs) and convolutional neural networks (CNNs), the system effectively identifies anomalies in real-time, helping to reduce system downtime and enhance operational efficiency in industrial settings.

The Industrial Internet of Things (IIoT) has revolutionized industrial processes, offering automation and data-driven decision-making. However, this interconnectedness brings new security challenges, especially in crucial infrastructure sectors. Traditional security measures are inadequate, leading to the exploration of innovative solutions. Blockchain technology has emerged as a promising solution due to its decentralized and immutable nature. This paper proposes a Hybrid Blockchain-Based Authentication Mechanism for IIoT, combining Delegated Proof of Stake (DPoS) and Elliptic Curve Cryptography (ECC). The hybrid architecture utilizes public and private blockchains to ensure scalability, efficiency, and security. Lightweight consensus algorithms, DPoS, are incorporated to optimize performance, while ECC provides efficient cryptographic techniques suitable for IIoT environments. An interoperable framework facilitates seamless integration with existing infrastructure, ensuring regulatory compliance and compatibility. Decentralized identity management further enhances security and privacy. Results and analysis demonstrate the effectiveness of the proposed solution, positioning hybrid blockchain architecture as the most suitable approach for enhancing security in IIoT environments.

This paper proposes Blockchain-Enhanced Multi-Factor Authentication (BEMFA) to address the limitations of existing authentication mechanisms in the Industrial Internet of Things (IIoT). BEMFA combines multi-factor authentication (MFA) with blockchain technology to ensure robust, scalable, and tamper-resistant security tailored to IIoT environments. This method dynamically manages roles and permissions, detects malicious devices, and ensures data integrity and authenticity. Our results demonstrate that BEMFA significantly enhances security, addressing critical access control challenges and mitigating risks posed by malicious devices while maintaining data integrity.

The need for electrical energy is increasing, especially in rural areas, so building an environmentally friendly energy power plant such as a micro hydro generator can be used to produce electrical energy. This PLTMH planning aims to help the community produce environmentally friendly electrical energy and overcome frequent power outages. The research discusses determining the type of turbine, water discharge, turbine power and generator power at (PLTMH). The water discharge (Q) value of 2.67 m3/sec is the water discharge during the rainy season. From the calculation results, it can generate 25.65 kW of electrical energy, the type of turbine used is an open flume propeller type turbine, the planning of a micro-hydro power plant with a turbine power capacity of 60 kW and a generator of 50 kW greatly influences the power that will be produced by the turbine, which This means that the greater the water discharge, the greater the power generated by the turbine, conversely, if the water discharge is small, the power produced by the turbine is relatively small.
 
 

Data transmission efficiency is crucial in wireless sensor networks (WSNs), where limited battery life and signal reliability are significant concerns. This research explores various machine learning algorithms aimed at optimizing data transmission in WSNs, focusing on reducing energy consumption and enhancing network stability. Simulation results indicate marked improvements in efficiency, making WSNs more viable for long-term deployment across diverse environments.

Renewable energy systems, particularly those incorporating solar and wind power, are prone to stability issues due to the intermittent and fluctuating nature of these energy sources. This paper investigates several nonlinear control techniques—namely, adaptive control, sliding mode control, and fuzzy logic control—that are designed to improve stability in renewable energy systems. Through simulations, we demonstrate how these methods can effectively reduce fluctuations and maintain stable energy output, offering a robust approach to enhancing the reliability of renewable energy systems.

As electric vehicles (EVs) gain popularity, understanding the demand for EV charging infrastructure becomes essential for effective urban planning. This paper develops mathematical models to predict EV charging demand based on various factors, including population density, traffic patterns, and energy consumption data. The models provide valuable insights for city planners regarding the optimal placement and capacity of EV charging stations to meet future demand, facilitating the transition to a more sustainable urban environment.

Battery storage systems are essential for stabilizing renewable energy grids, especially for sources like solar and wind power that are inherently variable. This study evaluates various battery technologies and their effectiveness in storing and redistributing energy within renewable energy grids. Through simulations and analysis of performance metrics, the study offers insights into optimal operating conditions for different battery types, highlighting their role in enabling sustainable and stable energy distribution.

The development of technology with digital image processing is often utilized to solve various problems in image processing, such as facial recognition, object detection, and interaction between users. In this study, we developed an interactive hand gesture-based game titled "Slap Mosquito" that utilizes image processing techniques to control the game through hand gestures. Using Rapid Application Development (RAD), Python, OpenCV, and Pygame methodologies, this game allows users to slap mosquitoes virtually in real-time through hand gesture recognition that is read by the camera and translated into in-game actions. RAD allows rapid development iterations and improvements based on user feedback, which is essential for improving system responsiveness and accuracy. This study focuses on detection precision, system responsiveness, and the impact of lighting on game performance, as measured using frames per second (FPS) and user gameplay results. The test results show that optimal lighting meets high detection accuracy, while low lighting conditions have a negative impact on accuracy and responsiveness. The results of this study provide insights for further development of gesture-based applications, especially regarding the importance of optimizing technical parameters and RAD methodology in improving user experience.
 
 

The Industrial Internet of Things (IIoT) faces various challenges in ensuring secure communication, authentication, and data integrity due to its distributed nature and evolving threat landscape. To address these issues, this paper proposes the integration of blockchain authentication as a robust solution to enhance security and reliability in IIoT networks. By leveraging Federated Learning with blockchain technology, the proposed solution aims to improve authentication mechanisms by training models across multiple edge devices, increasing fault tolerance, and adaptability while reducing the risk of single points of failure. The use of blockchain technology ensures a tamper-proof and transparent ledger for securely storing authentication data and model updates, enhancing security and integrity in IIoT networks. The results and analysis demonstrate that the integration of Federated Learning and blockchain technology effectively addresses interoperability issues, performance optimization concerns, and security vulnerabilities within IIoT networks, offering a more efficient, secure, and scalable authentication alternative.