Journal of Electronics Technology Exploration
Journal of Electronics Technology Exploration (JoETEX) p-ISSN: 3025-3470, e-ISSN: 3026-1066 is a peer-review and open-access journal published in every six months, namely in June and December. The Journal of Electronics Technology Exploration (JoETEX), published by SHM Publisher. The Journal aims to offer a digital platform for academics and specialists to submit novel concepts and critical reviews that consider past successes and upcoming difficulties in electronics and sustainable electrical engineering. The advantage of this journal is: 1). The fast response, for good quality articles, the following is the estimated processing time: a. Initial Decision for Review: 1 - 7 days after submission, b. Decision after review: 6 - 8 weeks after submission, c. online publication time: 1- 2 weeks after acceptance). 2). On progress to provides DOI (Digital Object Identifier) to each published article. 3). Open Access, have greater citation impact.
A Comparative Review of Performance and Energy Efficiency in ARM and RISC-V SoCs
The demand for energy-efficient computing systems is growing as workloads on embedded devices, edge computing, and scientific computing continue to expand. In this context, a comparison between the ARM and RISC-V architectures is important because both are widely used in System-on-Chip (SoC) designs but have different performance and energy efficiency characteristics. This article presents a comparative review of various benchmark studies on the Odroid XU4, Rock960, and Nezha D1 platforms using the NAS Benchmark, TensorFlow Lite Benchmark, and OpenFOAM. The summarized results show that RISC-V implementations generally have lower average power consumption but do not always yield a better performance-per-watt ratio. For some workloads, ARM platforms still demonstrate superior throughput and energy efficiency, while RISC-V performance is heavily influenced by the number of cores, accelerator support, and the maturity of the software ecosystem. Therefore, ARM remains more competitive for high-performance workloads, whereas RISC-V remains promising for power-efficient applications and continues to evolve.
Авторське право (c) 2026 Journal of Electronics Technology Exploration https://creativecommons.org/licenses/by-sa/4.0
Strategic Evolution Of Digital Transformation And Information Technology Governance: A Systematic Literature Review
Digital transformation and information technology (IT) strategy have become critical pillars of contemporary organizational management. This article presents a systematic literature review of 80 articles published between 2020 and 2025 in reputable international journals indexed in Scopus and/or Web of Science. The review examines digital transformation and IT strategy within business and organizational management using the PRISMA protocol and a structured thematic synthesis approach. The analysis covers digital transformation strategy, IT governance and strategic alignment, cloud computing and IT infrastructure, big data analytics and business intelligence, artificial intelligence, digital leadership and change management, cybersecurity, e-business and digital innovation, and enterprise resource planning and system integration. A systematic coding process was used to extract and synthesize bibliographic information, research methods, organizational contexts, key constructs, and major findings. The review shows that digital transformation is a multidimensional process of organizational change that extends beyond technology adoption and depends on the interaction of strategy, leadership, culture, governance, and the broader business ecosystem. It also highlights research gaps concerning developing-country contexts, integration across research themes, and sustainability. These findings provide an integrated overview of the field and inform a future research agenda for scholars and practitioners.
Detection of Breast Cancer Patient Mortality Status Using Machine Learning with SMOTE-Based Class Imbalance Treatment
Breast cancer is a disease with a high mortality rate, making early detection of patients at risk of death crucial for supporting medical decision-making. This study aims to develop a model for detecting patients at risk of death by comparing several machine learning algorithms. The research process included data collection, exploratory data analysis, data preprocessing, feature engineering, oversampling, modeling, and model evaluation. Data balancing was performed using SMOTE, and model optimization was conducted through hyperparameter tuning using GridSearchCV. The results show that Random Forest combined with GridSearchCV delivers the best performance in prediction, with an accuracy of 0.7491, precision of 0.2953, recall of 0.4634, an F1-score of 0.3608, and an ROC-AUC of 0.7030. This study demonstrates that the combination of Random Forest, SMOTE, and GridSearchCV is capable of optimizing the performance of the prediction model.
Novelty Half-Bridge LLC Resonant Converter with Magnetizing Inductance and Hybrid Rectifier
Half-Bridge LLC Resonant converters are widely used in high-power supply applications due to their high efficiency and ability to operate at high frequencies. However, under hold-up conditions or during large fluctuations in input voltage, conventional topologies often experience reduced output stability and increased losses. Therefore, a method that can maintain efficiency and output voltage stability without excessively broadening the switching frequency range is required. To address this, this study proposes a Novelty Half-Bridge LLC Resonant Converter with Magnetizing Inductor and Hybrid Rectifier (NHB-LLCRC-MIHR), incorporating a magnetizing inductor (Lm) in the primary path and a MOSFET-based hybrid rectifier on the secondary side. The research methodology was conducted using MATLAB/Simulink simulation, focusing on five main areas, including optimal switching frequency conditions, operating thresholds, DC conversion ratio (Vo/Vs), comparison of output voltage with conventional topologies, and analysis of output voltage ripple. Simulation results demonstrate that NHB-LLCRC-MIHR can maintain a more stable output voltage, lower ripple, and increase efficiency compared to conventional converters. Thus, this topology shows significant potential for industrial applications that demand high efficiency and optimal power stability.
Disease Detection in Cocoa Fruit Using YoloV4 Convolutional Neural Network Architecture
Detecting diseases in cocoa beans is important to prevent a decline in crop quality and economic losses. This study aims to detect diseases in cocoa beans using the YOLOv4 Convolutional Neural Network (CNN) architecture. The dataset used consists of images of cocoa beans, which then undergo preprocessing, data division, data augmentation, and modeling. The test results show that the YOLOv4 model is capable of detecting diseases in cocoa beans with an accuracy rate of 97%, demonstrating good performance in the classification and object detection process. However, this study still has limitations in terms of the amount of data and the variety of image capture conditions. Therefore, further research is expected to use a larger dataset and test the model in more diverse environmental conditions to improve the reliability of the system.
Milkfish Freshness Detection Based On Eye Images Using Convolutional Neural Network (CNN) With Mobilenetv3 Architecture On A Mobile Application
Indonesia has abundant fishery resources, making it one of the world's largest producers and consumers of fish. One of the most commonly consumed types is milkfish (Chanos chanos). Before consumption, it is important to determine the freshness level of the fish. This freshness can be identified using a Convolutional Neural Network (CNN) model with the MobileNetV3 architecture, which is efficient and suitable for mobile application implementation. This study aims to detect the freshness level of milkfish based on eye images using the MobileNetV3 CNN architecture implemented in a mobile application. The dataset used consists of 500 images, divided into training, validation, and testing sets with proportions of 70%, 20%, and 10%, respectively. The data underwent preprocessing, including resizing and image augmentation, to increase data variation. The model was developed using hyperparameter tuning with both random search and grid search methods. The results show that random search achieved better performance with a training accuracy of 92.88%, validation accuracy of 89.90%, and an overall test accuracy of 91%. The trained model was successfully implemented into a mobile application named ScanBang, which can classify the freshness level of milkfish and display its confidence score in a practical and user-friendly manner.
Авторське право (c) 2025 Journal of Electronics Technology Exploration https://creativecommons.org/licenses/by-sa/4.0
Classification of Pancreatic Cancer Diagnosis with CatBoost Using Urine Biomarker Combination
Uncontrolled cell growth in the pancreatic gland, is one of the most aggressive types of cancer with a high mortality rate, called pancreatic cancer. This research focuses on improving early diagnosis methods for pancreatic cancer by using CatBoost. Urine biomarker datasets were collected and subjected to pre-processing, including label coding, standardized scaling, and balancing via the Synthetic Minority Oversampling Technique (SMOTE). The CatBoost model achieved an accuracy of 98.89%, specificity of 99.35%, sensitivity of 98.71%, and Area Under the Curve (AUC) of 0.9951. These results show that the CatBoost model significantly outperforms the diagnosis models in previous studies, overcoming the challenges of early detection and classification of pancreatic cancer. This study shows that CatBoost is effective for diagnosing pancreatic cancer and suggests that future research explore other models on larger and more diverse datasets.
Malaria Disease Detection System in Humans Using Convolutional Neural Network (CNN)
Malaria is a deadly disease transmitted by the Plasmodium parasite. Detection is performed by trained microscopists who analyze microscopic images of blood smears. This analysis can be done automatically using modern deep learning techniques. The need for skilled labor can be significantly reduced by developing accurate and efficient automated models. In this article, we propose a fully automated convolutional neural network (CNN)-based model for diagnosing malaria from microscopic images of blood smears. Various techniques including knowledge distillation, data augmentation, autoencoder, feature extraction with CNN model to optimize and improve model accuracy and reasoning performance. Our deep learning model can detect malaria parasites from microscopic images with 95% accuracy requiring more than 27,600 images. This shows that the mode is able to provide more accurate predictions compared to malaria disease detection models using other algorithms such as in previous studies with an accuracy of 90%. By using CNN algorithm, this article can contribute novelty in the development of effective malaria detection methods for malaria disease.
Agrivoltaics in Japan: A Review of Current Practices, Challenges, and Future Directions
This review examines agrivoltaics in Japan integrating solar photovoltaic (PV) systems with agricultural production as a dual-use land strategy to address constrained arable land, decarbonization goals, and energy security. Using a thematic synthesis of published studies and documented Japanese cases, the paper maps current deployment practices, reported agronomic and energy outcomes, and the main constraints shaping adoption. The literature indicates that well-designed agrivoltaic configurations can maintain crop production while adding renewable electricity generation, with outcomes strongly influenced by site conditions, crop type, shading design, and farm management. Evidence also points to potential co-benefits such as reduced heat stress and improved microclimate stability, but trade-offs may emerge for light-sensitive crops or under suboptimal PV spacing and height. Key barriers in Japan include high upfront investment, complex permitting and compliance requirements, and concerns over land-use integrity and long-term agricultural continuity. Future research should prioritize longitudinal field data on crop yield and quality, soil and water dynamics, and ecosystem effects, alongside standardized performance metrics and policy/financing mechanisms that align farmer incentives with grid and climate objectives.
Integrating Renewable Energy Solutions through Collaborative Research: A Pathway to Sustainable Urban Development
The rapid pace of global urbanization has intensified the demand for sustainable energy solutions, prompting the integration of renewable energy into urban development plans. This paper explores the critical role of collaborative research in overcoming the challenges associated with transitioning to renewable energy in urban areas, focusing on the contributions of bibliometric analysis. This study uses a bibliometric approach to analyze global research trends, key publications, and influential authors in renewable energy and urban planning. The analysis highlights the growing body of literature, revealing significant clusters of research activity centered around sustainable development, urban planning, and renewable energy technologies. Case studies of cities like Freiburg and San Francisco, which have successfully integrated renewable energy technologies, demonstrate how collaborative approaches can lead to more resilient and sustainable urban environments. Despite significant progress, challenges such as infrastructural investments, retrofitting complexities, and regulatory hurdles remain. The paper emphasizes the importance of continued collaborative research to address these barriers and ensure that the benefits of renewable energy are equitably distributed among urban populations. In addition, the study underscores the necessity of interdisciplinary collaboration and public engagement in fostering broader adoption and support of renewable energy technologies. The findings advocate for strengthened partnerships and continued innovation, supported by bibliometric insights, to facilitate the global transition towards sustainable urban energy systems. This research provides a comprehensive understanding of the evolving landscape of renewable energy research and its critical role in shaping sustainable urban development.
Ev Battery Controller Tuning For Efficient Thermal Management Based On Grasshopper Algorithm And Particle Swarm Optimization Algorithm
Electric Vehicles (EVs) offer low emissions and reduced fossil fuel dependence but require efficient battery thermal management to ensure performance and safety. This research aims for tuning proportional-derivative(PD), proportional-integral(PI) and proportional-integral-derivative (PID) controller for Electrical Vehicle (EV) Thermal Management System using Particle Swarm Optimization (PSO) and Grasshopper Optimization Algorithm method (GOA) method to optimize the compressor power consumption to contribute to the development of better EV battery thermal management systems. By minimizing and maximizing the factors involved in the challenges, optimization is the process of identifying the best way to make something as useful and effective as feasible. Simulation results show that GOA outperforms PSO for all controllers. Objective function values for GOA are lower, 1.6783 (PD), 0.8517 (PI), and 0.8114 (PID), compared to PSO, 1.7578, 0.8665, and 0.8254, respectively. Improvement percentages of GOA over PSO are 4.73% (PD), 1.70% (PI), and 1.65% (PID). The PID controller achieved the best performance overall, showing 51.65% improvement over PD and 4.91% over PI. The findings confirm that GOA is more effective than PSO in optimizing controller performance, and that PID is the most suitable for stable and efficient EV battery thermal management.
Programming the 8031 Minimum System in Proteus Simulator using the C: Issues and Solutions
An essential required course in electrical engineering, computer science, and informatics is the microprocessor. Students may consider using Proteus software in cases wherein microprocessor trainers are unavailable. Yet, the simulation of the 8031 microprocessor-based minimum system circuit that Proteus executes fails to operate correctly, despite the fact that the source code and circuit wiring comply to programming and circuit theory standards. This is in contrast to other microcontroller-based minimum system circuits that it can be simulated successfully and as intended. This research aims to get hints in programming the 8031 minimum system circuit simulated using Proteus. The problem was investigated and analyzed by observing the parameters that become the properties of each element in the circuit, especially the RAM, then comparing them with the specifications of the microprocessor. The experimental results showed that some adjustments on the program code were necessary either written using assembly language or C program code.
Implementation of Retrieval-Augmented Generation (RAG) and Large Language Models (LLM) for a Document and Tabular-Based Chatbot System
The challenge of accessing information from disparate sources—unstructured documents and structured tabular data—hinders efficiency in enterprise information systems. This study addresses this challenge by presenting the design, implementation, and validation of a unified chatbot system powered by Retrieval-Augmented Generation (RAG) and Large Language Models (LLM). For unstructured documents, the system implements a RAG pipeline utilizing ChromaDB for vector indexing and OpenAI embeddings. Meanwhile, for structured data, it leverages a Text-to-SQL engine to translate natural language queries into SQL commands, with results visualized via QuickChart. The architecture is built upon a modular FastAPI backend with role-based access control and was rigorously validated through blackbox functional testing. Results demonstrate 100% functional success across all endpoints, confirming the architecture's reliability. This study confirms the viability of a unified RAG and Text-to-SQL architecture, offering a practical blueprint for creating more intelligent and integrated data interaction systems in enterprise environments.
Performance Analysis of Long Short-term Memory (LSTM) Model for Remaining Useful Life Prediction on Turbofan Engine
Accurate Remaining Useful Life (RUL) prediction is critical for the predictive maintenance and operational safety of aircraft turbofan engines. This research develops and evaluates a stacked Long Short-Term Memory (LSTM) network for RUL prediction using the NASA C-MAPSS FD001 dataset as a fundamental case study. A systematic data preprocessing pipeline was employed, including sensor selection, RUL value clipping at 130 cycles, and feature normalization to prepare the data for modeling. The LSTM model was trained with regularization techniques and an EarlyStopping callback to ensure robustness and prevent overfitting. Evaluation results on the unseen test data show the final model achieved a solid and competitive performance with a Root Mean Squared Error (RMSE) of 15.22 and a PHM08 Score of 311.20. These results demonstrate that a well-configured LSTM architecture provides a reliable baseline for engine prognostic tasks, exhibiting strong generalization capabilities on new data.
Improving V2V Communication Reliability in Dynamic Vehicular Networks: A Software-Defined Radio-Based Approach
Smart Transportation Systems (STS) leverage Vehicle-to-Vehicle (V2V) communication to enhance road safety, traffic efficiency, and urban mobility. However, ensuring reliable V2V communication remains challenging due to signal power instability, environmental interference, and scalability limitations. This study explores the optimization of V2V communication using Software Defined Radio (SDR) technology, which offers a cost-effective and adaptable approach for real-time signal processing. An SDR-based V2V communication system was developed using GNU Radio and HackRF One, with signal power calibration conducted through comparative measurements involving a Spectrum Analyzer across varying distances (3-15 meters) and environmental conditions. Performance evaluation focused on Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) under different vehicle speeds (20-40 km/h). Results indicate that increasing distance leads to signal degradation, with BER reaching 36.83% and SNR dropping to -3.17 dB, emphasizing the need for adaptive signal optimization techniques. While SDR-enabled calibration provided accuracy in signal measurements, environmental factors such as multipath interference and atmospheric attenuation significantly impacted communication reliability. Despite its flexibility, the system exhibited high BER and limited communication range, necessitating further enhancements through adaptive modulation schemes, machine learning-based power control, and hybrid 5G-DSRC integration. The study highlights SDR's potential for improving V2V communication while addressing key limitations in urban mobility networks. Future research should focus on enhancing scalability, security, and energy efficiency through advanced signal processing techniques. This study contributes to developing next-generation STS by providing empirical insights into SDR-based V2V communication optimization, supporting safer and more efficient transportation systems.
Automated Printed Circuit Board Inspection System using NI Vision Builder and NI MyRio
To improve flaw identification in contemporary electronics manufacturing, this study introduces an automated printed circuit board (PCB) inspection system that integrates NI Vision Builder with NI MyRIO. The device effectively detects flaws like missing parts, open circuits, and over-etched traces by utilizing a high-resolution camera and sophisticated methods including color plane extraction and pattern matching. Real-time visualization, classification, and automated data recording are made possible via a LabVIEW-based interface, which makes the inspection process easy to use. A 92% accuracy rate was attained during testing on both bare PCBs and PCB assemblies, indicating better performance than conventional techniques. Although multi-layer and subsurface defect detection still presents difficulties, the system provides a scalable and affordable solution with the possibility to incorporate machine learning and sophisticated imaging in the future for increased adaptability.
Design of an Automatic Slat Conveyor Prototype Based on PLC
The transfer of goods, commonly known as pick and place, which still relies on manual labor, poses challenges for industrial production companies. In automotive quality inspection, the manual transfer of vehicles by drivers between inspection stations often leads to inefficiencies and missed production targets. This research aims to implement an automated slat conveyor system controlled by PLC to optimize vehicle quality inspection. With this system, vehicles are moved automatically between stations, allowing inspections to be conducted during transit. The automation aligns with the Kaizen philosophy, emphasizing continuous improvement in production processes. The results of testing the slat conveyor automation system indicate that the system successfully achieves a maximum response time of 0.8 seconds, with a speed of 0.6 meters per second.
Implementing a Dual Payment Method for Mobile Charging Stations in Urban Areas
The mobile phone has become the communication device of choice for most people due to its practicality compared to other communication devices. A mobile phone requires electrical energy from its battery to function. Over time, the electrical energy stored in the battery depletes, and the phone needs to be recharged. A frequent problem arises when users are outside without a charger or have difficulty finding a safe power source. To address this issue, this final project proposes a solution: a secure mobile charging station that can be placed in public locations such as stations, hospitals, banks, hotels, malls, restaurants, and other venues. The paid mobile charging station consists of six charging lockers. Each locker is secured with a solenoid lock controlled by a Node MCU ESP32 microcontroller. Payment can be made using either coins or an RFID card. To charge their mobile phones, users must insert coins or swipe an RFID card. Once payment is made, users can charge their phones in the provided lockers for a specified period. System testing demonstrates high reliability, with a 100% success rate in coin validation, accurate RFID balance deductions, and seamless data transmission between the ESP32 and the database. The results indicate that the proposed system is a cost-effective, scalable, and secure solution for providing public mobile charging services in locations such as malls, hospitals, and transportation hubs.
Development of a Remote Three-Phase Motor Wiring Practice Tool Using ESP32, LabVIEW, Wokwi, and Adafruit IO
The practical application of three-phase induction motor wiring is an essential part of mechatronic engineering education. The integration of IoT-based tools and simulation software such as Wokwi, LabVIEW, and Adafruit IO enables remote monitoring and control of motor wiring practices. This article discusses the development of a remote three-phase induction motor wiring tool utilizing the ESP32 microcontroller, LabVIEW for real-time monitoring and control, Wokwi for circuit wiring simulation, and Adafruit IO for cloud-based data communication. Testing shows that the system effectively facilitates remote motor control and wiring simulation, providing a flexible and accessible learning environment.
Model of Integrated System for Feeding Catfish and Monitoring Pond Temperature Based on IoT
Indonesia is renowned for its rich biodiversity, including a diverse array of ornamental and consumable fish species. These fish are widely cultivated in aquariums, ponds, and cages, with prices varying greatly depending on the species. However, the current method of manual fish feeding remains inefficient and time-consuming. To address this challenge, an automated fish feeding system has been developed to streamline and enhance the feeding process.This research project was conducted using a simulation approach to lay the groundwork for a future prototype. The simulation involves an IoT-based model capable of providing automated feeding for catfish and monitoring the temperature of their pond. The system is designed for future implementation at Botani UPI, where traditional manual feeding methods are still prevalent.The primary objectives of this research are to automate catfish feeding and monitor the temperature of their pond. Feed will be dispensed according to a predetermined schedule stored in a database, allowing for flexible adjustments via the internet.Testing has demonstrated the successful operation of the website, fulfilling its core functions: adjusting pond water temperature as needed, automatically providing catfish feed, and scheduling regular feeding intervals. Users can control these functions remotely, enabling effective pond monitoring and management without physically being present at the site.The success of this testing underscores the immense potential of the integrated system to elevate catfish farming productivity and efficiency. By maintaining optimal pond temperatures, the health and growth of catfish can be better sustained, while scheduled and accurate feeding helps minimize feed wastage and ensures adequate nutrition for the fish. Additionally, the system reduces the manual workload for farmers, allowing them to focus on other aspects of pond management. Overall, the implementation of this technology not only offers economic benefits through increased production yield but also promotes more sustainable and modern fish farming practices.
IoT-Integrated Smart Attendance and Attention Monitoring System For Primary and Secondary School Classroom Management
The monitoring of student attendance is a crucial aspect of the assessment of academic performance. The conventional methods for monitoring student attendance have inherent limitations in terms of both time efficiency and accuracy. Consequently, there is a clear need for a more expedient and precise attendance system. The objective of this research is to present the design of a real-time attendance recording and monitoring system for students from elementary school to senior high school, which will be implemented using the concept of the Internet of Things (IoT). The proposed system employs biometric technology in the form of face recognition. The methodology commences with the capture of images of students who leave the classroom during the instructional period via an active camera positioned on the classroom door. The system employs a Convolutional Neural Network (CNN) algorithm and a powerful computer vision tool, OpenCV, to perform real-time face recognition. Teachers will be informed of student absences and returns, as well as at the 15th and 30th minutes. An absence exceeding 30 minutes is classified as truancy. The integration of sophisticated technologies, such as machine learning and image processing, not only enhances the precision of attendance records but also equips educators with an efficient and automated system for streamlining classroom attendance management. This not only optimizes the learning environment but also facilitates more advanced and efficient pedagogical practices.
Авторське право (c) 2024 Journal of Electronics Technology Exploration https://creativecommons.org/licenses/by-sa/4.0
Near Field Communication (NFC)-Based Prepaid Smart Power Meter
An essential part of an electric energy service is an electrical power meter. Numerous customers have already expressed dissatisfaction over the electric meter's faulty readings. The development of an electrical power meter with an NFC reader is presented in this study. When an NFC card is successfully scanned by the NFC reader, the power meter is turned on and ready to deliver electricity. Before the electricity is automatically turned off, or when the credit is almost low, the display will show the alert. By experiment, PZEM 004T sensor use 3 loads, namely 60 W, 75 W and 100 W for testing current, voltage and cos phi and have error of 0 – 1.20%. In balance reduction with loads of 60 W, 75 W, and 100 W with a time gap of 5 and 10 minutes, there is an error reduction of 11.5 – 17.4%. Thus, this electricity meter with NFC reader is suitable for monitoring and controlling electricity usage.
Application of Passive Infrared Sensor to Improve the Quality of CCTV in Maintaining Home Security
Artificial intelligence, or AI, is a simulation technology that runs through human intelligence demonstrated by machines or tools. Artificial intelligence can overcome and provide a sense of comfort, especially in the application of CCTV devices that use this passive infrared sensor method. This method can detect thieves or people moving in the area of the house where CCTV is installed, by detecting human objects using IR filters. If it detects an object that has the minimum temperature possessed by humans, it will immediately direct the alarm indicator. With the existence of CCTV that applies AI, it is hoped that human life will be safe, and crime will be reduced in an area, especially quiet areas with high crime rates. The application of Passive Infrared Sensor (PIR Sensor) in this anti-theft CCTV tool can detect and be able to work with a high level of accuracy.
Air Humidity Measurement Through Solar Panel
In this work, we will examine the placement of solar panels at certain heights to obtain optimal efficiency. To prove the effect of altitude and humidity, the authors conducted tests in areas with different altitudes and different temperatures, namely in the areas of Medan and Berastagi cities, where the city of Medan is located at an altitude of 2.5-7.5 meters above sea level, and the city area of Berastagi is higher, namely at an altitude of 1220 meters above sea level. The distance between the two areas is around 70 km and can be reached in approximately 2 hours of travel. With different altitudes and different climates, the authors are very interested in studying and researching how the effectiveness of solar panels is at different altitudes and humidity level and how it affects the Medan area, which has a tropical climate, and Berastagi, which has a cold climate.
Off-Grid Emergency Tent Solutions with a Compact Solar Photovoltaic System
In recent years, solar photovoltaic (PV) systems have seen increased utilization for powering a diverse range of applications, from residentials and buildings to off-grid installations, highlighting their growing importance in the global transition towards sustainable development using renewable energy sources. This paper proposes the development of a compact solar PV system for emergency tent systems to aid preparations and rapid response efforts during disasters and unforeseen events. The system aims to support local authorities and communities in mitigating disaster risks due to power outages. The design and implementation of an emergency tent prototype measuring 3´3 m2, with a power consumption of 0.36 kW/day using a 30 W solar PV panel and battery storage capacity of 40 Ah. The system is sufficient for powering the basic functions of dc bulbs and fans as the primary load.
Analysis of Micro Structure, Porosity Disability and Wear Resistance with Volume Variation from Riser On the Engine Cover of Electric Motors
This research to analyze the microstructure, porosity defects, and wear resistance of aluminum casting in the presence of variations in the volume of risers. The method used is the pre-experimental method of the One-Shot Case Study type, because in this study a treatment will be carried out and the results will be observed. The treatment that will be carried out is the addition of variations not using risers, variations of riser cylinders with a volume of 2826mm3, variations of riser cylinders with a volume of 4710mm3, variations of riser cylinders with a volume of 6594mm3. Microstructure testing used the Meji Techno IM 7200 test tool. Wear testing used the Ogoshi High Speed Universal Wear Testing Machine (Type OAT-U). The data analysis used is descriptive analysis to provide an overview of the research subject based on data from the variables obtained from the group of subjects studied. The best microstructure is shown by the cast specimens with the riser volume variation of 6594mm3 as evidenced by the formation of a more dense and even structural phase. The best porosity results were shown by the specimens that were cast with a volume variation of the riser 6594mm3 of 37.97%. The best wear resistance results in variations of the volume riser 6594mm3 with wear values of 0.51x10-7mm2/kg.
Авторське право (c) 2023 Journal of Electronics Technology Exploration https://creativecommons.org/licenses/by-sa/4.0
Performance Analysis of Wi-Fi Wireless Networks In A Vortex Media Access And Reseller Broadband
The research aims to analyze network performance in an office and also a broadband reseller, to determine network quality through monitoring trials on servers and clients. The research uses data collection methods by conducting interviews. The second stage is needs analysis as a benchmark for network performance analysis. The next stage is monitoring the server and client to determine network speed. From research that has been carried out related to the analysis of WiFi wireless networks in small and medium office environments using custom network topology to suit needs, as well as broadband resellers also using custom network topology. The distribution of bandwidth from each place adjusts to their respective needs to get quality network connections The good thing is, that there are no specific standards for dividing bandwidth, it all depends on needs. The results of the data obtained from the analysis of the two places, of course from the analysis that the author has carried out, we can know the quality of the network connection from the two places, apart from that the topology design used by the office environment and also broadband resellers can increase knowledge about WiFi wireless networks and can be an example to open a business in the field of WiFi wireless networks that can provide faster WiFi performance with wider coverage.
Flying Trap (Fly-T): An Automatic Termite Trapping Based on IoT and Hybrid Energy System using NodeMCU
This paper proposes an automated productive caste termite trap device based on a hybrid energy system and the Internet of Things called Flying Trap (Fly-T). This tool is equipped with ultraviolet light with a frequency of 365 nm which is used to attract termites to enter and trap into Fly-T storage tank until they die. Dead termites will be detected by an ultrasonic sensor with a certain limit value then the relay cuts off electric current so that the light turns off and the tank door automatically opens to expel dead termites. The automatic control system on Fly-T is built using the NodeMCU ESP32 microcontroller to optimize the performance of sensors, relays, servo, and wifi connections in recording data to an IoT-based cloud database. The Fly-T is also controlled by command via a Telegram Bot equipped with solar panels and a windmill turbine generator. The results show that Fly-T can run automatically, easily, and save time efficiently, and is environmentally friendly.
Early Detection of Microsleep in Motorcycle Helmet Based on Pulse Sensor
Microsleep can be defined as a brief condition in which someone unintentionally falls asleep for a few seconds to several minutes. This condition can occur in anyone and poses a high potential risk, especially when engaged in activities that require high concentration, such as driving. To detect and address the potential dangers of microsleep while driving, this research has designed a smart helmet capable of early detection of signs of microsleep and taking actions to awaken the rider. This system uses a pulse sensor connected to an Arduino and placed on the backside of the helmet. Detection of beats per minute (bpm) is crucial to determine whether the rider is drowsy or not. This is essential for providing early warnings to the rider. If the rider's bpm reading is <60, indicating drowsiness, the system activates a vibrator to shake the helmet. If this condition persists for more than 7 seconds, the speaker also activates to play music, which will only stop when the bpm reading is >60. Testing was conducted on 5 test subjects, with each subject undergoing 5 trial tests, resulting in a total of 25 test runs. The results indicate that the designed system is capable of reading microsleep conditions and activating the vibrator and music according to the configured settings.
Enhancing IoT Network Reliability: Evaluating LoRa Module Susceptibility to Interference
The Internet of Things (IoT) is gaining popularity, leading to the widespread use of remote communication modules (LoRa), known for their energy efficiency and wide coverage range. However, as the number of LoRa modules used in IoT networks grows, the possibility of interference from third-party devices operating at the same frequency becomes a concern. This study aimed to examine the vulnerability of LoRa modules to electromagnetic interference (EMI) when transmitting text messages and images. Radiation emission conditions were measured in the test area for evaluating LoRa module performance, and susceptibility to interference was assessed under non-line-of-sight (NLOS) conditions. The study's outcomes reveal that interference with LoRa transmitters has no noticeable effect on the range within a distance of up to 50 meters. In contrast, the interference power required to disrupt the LoRa receiver decreases with increasing distance. Additionally, interference from frequencies outside the designated LoRa working frequency (915 MHz) has no discernible impact on module performance. Introducing a delivery delay check demonstrates consistent performance even in interference. These findings deepen our understanding of the susceptibility of LoRa modules to tampering, emphasizing the importance of implementing effective disruption management strategies in IoT deployments. By considering the potential impact of electromagnetic interference (EMI) on LoRa modules, developers can design more robust IoT networks, ensuring reliable communication and improved system performance. Overall, the research focuses on the interference characteristics of LoRa modules, providing insights for developing resilient and interference-resistant IoT solutions. It underscores the necessity of addressing interference issues to ensure the reliable operation of IoT devices across diverse environments.