Indexing: Vertex (ISSN 2089-385X (Print) | 2829-6761 (Online)) at https://pub.iocscience.org/index.php/Vertex/index) is a peer-reviewed journal that publishes scientific articles from various engineering disciplines (Preferably from the Nuclear Field). Here are the fields that we highly prioritize to publish:. Computer Science AI Quantum Computation Instrumentation Electronics Electrical Engineering all fields of science that are relevant to the field of technology Articles published in Vertex include original scientific research results (top priority), new scientific review articles (non-priority), or comments or criticisms on scientific papers published by Vertex. The journal accepts manuscripts or articles in the field of engineering from various academics and researchers both nationally and internationally. The journal is published every June and December (2 times a year). Articles published in Vertex are those that have been reviewed by Peer-Reviewers. The decision to accept a scientific article in this journal is the right of the Board of Editors based on recommendations from the Peer-Reviewers. Since 2011, Vertex only accepts articles derived from original research (top priority), and new scientific review articles (non-priority).
Unsupervised Machine Learning Based DSS for Land Profiling and Disease Risk Mitigation in Smart Farming
Decision Support Systems (DSS) in smart farming require methodologies capable of representing the holistic complexity of agricultural ecosystems. This study proposes a DSS framework based on unsupervised machine learning, specifically K-Means clustering, to automatically segment land profiles using IoT sensor records. The dataset consists of 500 global sensor data points covering seven essential environmental variables: soil moisture, pH, temperature, rainfall, humidity, sunlight duration, and the NDVI index. Through Principal Component Analysis (PCA) for dimensionality reduction and Silhouette Score evaluation, the system successfully identified and mapped seven land profiles with distinct microclimatic characteristics. Cross-tabulation analysis further demonstrates the principal novelty of this DSS, namely its ability to classify land into "Safe Zones" (Clusters 0, 3, and 4), which are characterized by Mild disease status and are suitable for Soybean, Cotton, and Maize, as well as "High-Risk Zones" (Clusters 1, 2, 5, and 6), which consistently correspond to Severe disease status. These findings indicate that a DSS based on environmental clustering is substantially more effective for crop selection recommendations and disease prevention than conventional predictive approaches. Ultimately, this framework provides farmers with actionable insights to optimize productivity and minimize agricultural risk
Copyright (c) 2026 Embun Fajar Wati (Author); Elvi Sunita (Translator); Andi Diah Kuswanto (Author) https://creativecommons.org/licenses/by/4.0
Simulation Of Rectangular Patch Microstrip Antenna For Wimax At 3.5 GHz Frequency
Microstrip antenna is one of the compact antenna types that can be implemented using PCB material and consists of three main parts, namely the radiating element, substrate, and ground plane. This antenna is widely used in wireless communication systems because it has a simple structure, small size, and is easy to integrate with electronic devices. One of its applications is in WiMAX systems operating at 3.5 GHz to support more efficient data transmission. In this study, a rectangular patch microstrip antenna was designed as an alternative antenna for WiMAX systems. The design process was carried out using AWR software to obtain antenna characteristics that meet the required specifications. The evaluated performance parameters included return loss, VSWR, and bandwidth. Based on the simulation results, the designed antenna achieved a return loss of -14.21 dB, a VSWR of 1.484, and a bandwidth of 152 MHz. After optimization, the final design achieved an improved return loss of -26.93 dB and a VSWR of 1.09. These results indicate that the proposed antenna has good performance and is suitable for supporting WiMAX communication systems
Copyright (c) 2026 Muhammad Iqbal, Aprima A Matondang, Stephanie Pardede, The Fitri Astarani, Morlan Pardede (Author) https://creativecommons.org/licenses/by/4.0
Analyzing Consumer Purchasing Behavior in Electrical Supply Stores Using Association Rules
This study aims to improve inventory management and to analyze consumer purchasing behavior through data exploration and the application of association rule mining. The dataset used in this research consists of sales transaction records of electrical products collected over a one-year period. Due to the wide variety of items sold, product categorization is conducted to support more effective analysis and interpretation of purchasing patterns. The method applied in this study is association rule mining using the Apriori algorithm. This method is employed to discover relationships and co-occurrence patterns among items in transaction data. The minimum thresholds used in this study are support ≥ 10% and confidence ≥ 30%, ensuring that only significant and reliable association rules are generated. The results of the analysis reveal several important patterns, with the strongest rule identified as: “Lakban, Switch, and Socket → Cable,” which has a confidence value of 46%. This indicates that customers who purchase Lakban, switches, and sockets have a 46% likelihood of also purchasing cables. The findings provide insights into customer purchasing behavior that can be utilized to optimize inventory control, improve product arrangement, and develop effective cross-selling strategies. Furthermore, this study demonstrates that the application of association rule mining can support data-driven decision-making, enhance operational efficiency, and contribute to increased sales performance and customer satisfaction
Copyright (c) 2026 Yunas Akbar, Tiwuk Wahyuli Prihandayani, Novianti Madhona Faizah, Luky Fabrianto, Ryan Rakryan (Author) https://creativecommons.org/licenses/by/4.0
IMDb Movie Rating Prediction Using a Random Forest Classification Approach
Accurate movie rating prediction is essential for supporting audience preferences and analytical decision-making in the digital film industry. The availability of large-scale metadata from IMDb provides valuable opportunities for applying machine learning techniques to analyze rating patterns. This study investigates the effectiveness of a Random Forest classification model for predicting IMDb movie rating categories based on structured attributes, including genre, movie duration, content rating, actor popularity, and user review statistics. Data preprocessing involved handling missing values, removing duplicates, encoding categorical variables, normalizing numerical features, and partitioning the dataset into training and testing subsets. To mitigate class imbalance among rating categories, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training data. Experimental evaluation demonstrates that the proposed model achieves an overall accuracy of 0.78, accompanied by balanced precision, recall, and F1-score values across all classes. Confusion matrix analysis shows that classification errors predominantly occur between neighboring rating categories, reflecting the inherent subjectivity of movie ratings. Furthermore, feature importance analysis highlights genre, duration, content rating, and user engagement indicators as the most influential predictors. These results indicate that Random Forest offers a robust and interpretable baseline model for IMDb rating prediction and provides meaningful insights for future movie analytics and recommendation research.
Copyright (c) 2026 Rifqy Rosyidah Ilmi (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Rice Disease Identification using Hybrid GLCM-XGBoost with SMOTE Imbalance Handling
Rice (Oryza sativa ) is a major food staple, which is prone to multiple diseases that will dramatically decrease the harvest yield. Disease identification is time consuming and is usually subject to subjective errors in a manual approach. The following research will seek to increase the level of precision of automatic rice plant disease detection, namely the Brown Spot, Hispa, and Leaf Blast classes. The suggested method combines both the Gray Level Co-occurrence Matrix (GLCM) to extract texture features and the Extreme Gradient Boosting (XGBoost) classification algorithm. Furthermore, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to address class imbalance within the dataset of 5,548 images. Preprocessing steps include resizing, grayscale conversion, and Min-Max normalization. Experimental results demonstrate that the model trained on SMOTE-balanced data with optimized XGBoost parameters achieved a superior accuracy of 98%, outperforming the imbalanced scenario (97%) and previous studies. This research confirms that the combination of GLCM, SMOTE, and XGBoost constitutes a robust and high-precision method for rice disease identification
Copyright (c) 2026 Anwar Sadad (Author) https://creativecommons.org/licenses/by/4.0
Analysis of the Technology Acceptance Model (TAM) in the Acceptance of Smartboards in Elementary Schools
The use of Smartboards in primary school learning has become an essential part of digital transformation in Indonesian education. However, the success of this implementation strongly depends on user acceptance. This study aims to analyze the factors influencing Smartboard acceptance using the Technology Acceptance Model (TAM), which includes Perceived Usefulness (PU), Attitude Toward Using (ATU), and Intention to Use (IU). A quantitative explanatory correlational design was employed, involving primary school teachers in West Jakarta who have used Smartboards in classroom instruction. Data were analyzed using Structural Equation Modeling (SEM). The findings indicate that all indicators of PU, ATU, and IU demonstrate valid and reliable loading factor values. Structurally, PU has a positive and significant effect on IU, and ATU also shows a significant positive influence on IU. These results highlight that perceived usefulness and positive attitudes are the primary determinants shaping teachers’ intention to use Smartboard technology. The study implies that enhancing digital competencies, providing structured Smartboard training, and ensuring technical support are crucial to optimizing the implementation of Smartboard-based learning
Copyright (c) 2026 Ririn Restu Aria, Helina Apriyani, Enok Tuti Alawiah (Author) https://creativecommons.org/licenses/by/4.0
Obesity risk estimation using ensemble learning and synthetic data augmentation techniques
Obesity has become a primary global health concern due to its strong association with various chronic diseases such as diabetes, cardiovascular disorders, and certain types of cancer. Accurate and early risk prediction of obesity is essential for effective prevention and intervention strategies. However, predictive modeling in this domain often encounters two critical challenges: the presence of imbalanced datasets and the complex, nonlinear nature of behavioral and anthropometric features. This study aims to address these challenges by developing a robust classification model that integrates ensemble learning with synthetic data augmentation techniques. The research utilizes the Obesity Dataset from Kaggle, which comprises 2,111 records labeled into seven obesity levels, reflecting a realistic class distribution imbalance. Preprocessing steps included data cleaning, encoding, and stratified splitting. To enhance class representation, two augmentation methods were applied: SMOTE for synthetic oversampling and Generative Adversarial Networks (GANs) for generating realistic minority samples. A stacking ensemble model was constructed using Random Forest and XGBoost as base learners, with Logistic Regression serving as the meta-learner. Hyperparameter optimization was conducted using both grid and randomized search methods. Evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess performance. The proposed model achieved a 91% accuracy and an F1-score of 0.89, significantly outperforming models from previous studies. These findings suggest that combining ensemble learning with hybrid augmentation strategies effectively addresses class imbalance and improves predictive reliability in obesity risk estimation. The developed model holds practical value as a decision-support tool for early screening and targeted intervention in obesity prevention programs.
Copyright (c) 2023 Nur Tulus Ujianto, Gunawan Gunawan, Wresti Andriani, Ivan Rizky Ramadhani, Nasichatun Nasichatun (Author) https://creativecommons.org/licenses/by/4.0
Anomaly Detection of Parasitic Plankton in Brebes Eco-Waters Using Vision-Based Autoencoder AI
The escalating impact of environmental stress on coastal ecosystems necessitates reliable, scalable tools for monitoring marine biodiversity. This study proposes an unsupervised anomaly detection framework to identify parasitic and morphologically abnormal plankton in the waters of Brebes, Indonesia. The primary aim is to develop an interpretable, vision-based system capable of detecting visual anomalies without relying on labeled anomaly data. The research integrates convolutional autoencoders for reconstructing normal plankton images, Principal Component Analysis (PCA) for feature extraction, and One-Class Support Vector Machines (OC-SVM) for classification. Monthly microscopic images were obtained from selected mangrove and aquaculture pond sites in Brebes, Central Java, using portable digital microscopy under standardized field conditions. Images that exceeded a dynamic reconstruction threshold were flagged as anomalous and validated by marine biology experts. The system achieved an F1-score of 86.1%, a precision of 85.3%, and an AUC of 0.94, demonstrating high effectiveness in distinguishing between normal and anomalous plankton. With an average inference time of 0.37 seconds per image, the system supports near real-time monitoring. These results confirm the potential of the proposed method as a low-latency, field-deployable solution for aquatic ecosystem surveillance. By integrating AI-based detection with ecological expert validation, this research offers a scalable approach for marine biodiversity assessment and establishes a foundation for future adaptive environmental monitoring systems.
Copyright (c) 2025 Gunawan Gunawan, Wresti Andriani, Sesilia Putri Maryanto, Restu Abi Mustaqiim (Author) https://creativecommons.org/licenses/by/4.0
Implementation of UI/UX Design in the Development of Profile Website Using the User Centered Design Method
The evolution of UI/UX design on the profile webpage is covered in this study. A community called Omah Cikal Kreatif aims to empower housewives by fostering their creativity via handicrafts made from secondhand items. The primary obstacle is the limitations of digital platforms' user interface (UI) design and user experience (UX), which affect user engagement and low exposure. This research aims to design and develop an attractive, informative and easy-to-use website by applying the UCD approach. The development process includes user needs analysis, prototype design, and design evaluation using the System Usability Scale (SUS) method. The results of this research provide strategic insights in improving the quality of website design and strengthening Omah Cikal Kreatif's identity in the digital realm
Copyright (c) 2025 Savanda Harianty, Sri Wahyuni, Ruly Dwi Arista (Author) https://creativecommons.org/licenses/by/4.0
Implementation of the Topsis Method in Determining Online Shopping Options in the Marketplace
The development of information technology changes the way customers view shopping. Currently, customers are more likely to make online shopping transactions through the marketplace. Significantly increased internet penetration, ease of transactions, seller reputation, speed of service, ease of access are factors that support customers in making shopping transactions in the marketplace. However, customers need to decide wisely before making a shopping transaction so that the products obtained are in accordance with expectations. This study uses the TOPSIS method, a method in the decision-making process to choose an ideal solution based on the criteria offered. The results of the study obtained a result of 0.85 for product reviews as an alternative preference in shopping online through the marketplace.
Copyright (c) 2025 Tuslaela Tuslaela, Enok Tuti Alawiah, Tuslaela (Author) https://creativecommons.org/licenses/by/4.0
Development of distance measurement accuracy technology in physical activity tracking applications with a reward point system
In the digital era, physical activity tracking applications have become increasingly popular as tools to monitor body health and encourage healthy habits. However, the accuracy of distance measurements used by many of these applications still faces challenges, especially in environments with GPS signal interference. This research aims to develop a system that integrates GPS technology and accelerometer sensors to improve the accuracy of distance measurements in physical activity tracking applications. The developed system was tested through a prototype to evaluate the effectiveness of combining these two technologies in improving measurement results. Additionally, this research also designs a database system for efficient physical activity data management, enabling real-time monitoring. To enhance user motivation, a reward point system was applied as a gamification element to encourage further engagement in physical activities. The results of this research show that the combined use of GPS and accelerometers was able to improve measurement accuracy, with errors ranging from 2.4% to 4.2%, depending on the type of activity performed. Walking activities demonstrated higher accuracy compared to running. The reward point system was also proven to be effective in motivating users to be more active. This research provides an important contribution to the development of more accurate, efficient health applications that can improve both physical and mental well-being
Copyright (c) 2025 Oki Setiono, Nurjanah Nurjanah, Kismi Mubarokah, Haikal Haikal, Muhammad Iqbal (Author) https://creativecommons.org/licenses/by/4.0
Performance Analysis of Wet Gas Flow in Up and Down Transmission Pipelines
This research examines the performance of wet gas flow in up and down transmission pipelines, addressing the critical challenges and dynamics associated with fluid behavior in varying orientations. Through extensive empirical analysis and modeling, the study identifies key factors influencing slugging, liquid accumulation, and flow efficiency. Findings reveal that upward transmission systems are particularly prone to slugging, leading to operational instability and increased energy demands, while downward flow systems benefit from gravitational assistance, resulting in enhanced reliability and reduced maintenance needs. Additionally, effective liquid management strategies and advanced monitoring technologies are essential for mitigating adverse effects and optimizing system performance. The research also addresses gaps in existing literature by providing new insights into flow management and environmental considerations, offering practical recommendations for pipeline design and operation. Ultimately, this study underscores the importance of ongoing research to refine understanding and improve practices in the transport of wet gas, contributing to the development of sustainable energy solutions.
Copyright (c) 2024 Silvester Adi Surya Herjuna, Andrieu Labriet, Bima Akbar, Johannes Dimara, Frans Kaisiepo (Author) https://creativecommons.org/licenses/by/4.0
Analysis of the Effect of Pretreatment of Empty Palm Oil Bunches (TKKS) on Biogas Production
This study investigates the effect of different pretreatment methods mechanical, chemical, and biological on biogas yield and quality from TKKS. Mechanical pretreatment involves shredding and grinding TKKS to increase surface area and enhance enzymatic accessibility, facilitating microbial degradation of cellulose and hemicellulose. Chemical pretreatment employs acid hydrolysis to disrupt lignocellulosic bonds, releasing fermentable sugars for improved substrate availability in anaerobic digestion. Biological pretreatment utilizes enzymatic or microbial processes to enhance biomass deconstruction and accelerate methane production. Experimental results demonstrate that all pretreatment methods enhance biogas production compared to untreated TKKS. Mechanical pretreatment yields significant improvements in biogas yield and methane content, owing to enhanced substrate accessibility. Chemical pretreatment shows comparable efficacy, albeit with considerations for optimal acid concentration and microbial inhibition. Biological pretreatment exhibits superior methane production rates, underscoring its potential in maximizing biogas recovery from TKKS. The implications of these findings extend to sustainability benefits, including reduced greenhouse gas emissions and enhanced waste management practices within the palm oil industry. Techno-economic feasibility and scalability considerations highlight mechanical and biological pretreatment methods as viable options for industrial-scale biogas production from TKKS.
Copyright (c) 2024 David Manuel, Nasihin Rahman (Author) https://creativecommons.org/licenses/by/4.0
Analysis of the Impact of Paving Block Material Variations on Urban Traffic Flow, Safety, and Sustainability
This study investigates the impact of varying paving block materials on traffic flow, safety, durability, and environmental sustainability in urban settings. By analyzing data from multiple field studies, laboratory experiments, and performance models, we compared traditional materials such as asphalt and concrete with innovative options like porous asphalt, permeable pavers, and recycled composites. Our findings reveal that smoother materials, including asphalt and certain concrete types, enhance traffic flow by reducing rolling resistance, while textured surfaces offer superior skid resistance, thereby improving safety. Concrete demonstrated notable durability and lower long-term maintenance costs compared to asphalt, while sustainable materials like recycled composites and permeable pavers exhibited reduced carbon footprints and enhanced stormwater management capabilities. The implications of this research suggest that informed material selection can significantly improve urban traffic efficiency, safety, and sustainability, providing a roadmap for urban planners and policymakers aiming to develop resilient and environmentally friendly infrastructure.
Copyright (c) 2024 Rivaldi Miwa, Aswinda Aswinda (Author) https://creativecommons.org/licenses/by/4.0
Performance Analysis of Insulation Materials in Earthquake Resistant Buildings
This research investigates the performance of insulation materials in enhancing the seismic resilience of buildings against earthquake-induced forces. The study focuses on evaluating various insulation materials including fiberglass, mineral wool, and foam boards (EPS and XPS) through rigorous experimental testing under simulated seismic conditions. Findings reveal significant differences in the seismic response of insulation materials based on their damping capacities, stiffness characteristics, and resilience post-seismic event. Mineral wool demonstrates superior energy dissipation properties, effectively reducing structural vibrations and enhancing building stability. Conversely, certain foam board insulations exhibit high compressive strength, maintaining structural integrity and controlling deformations under dynamic loading conditions. The study's results align with theoretical expectations in earthquake engineering, validating the importance of material properties in enhancing building performance under seismic hazards. The implications of this research extend to informing building codes, standards, and design practices aimed at promoting sustainable and resilient urban infrastructure in earthquake-prone regions.
Copyright (c) 2024 Fabrio Situmorang, Rifanro Situmorang (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Picohydro Power Plant Design on Water Flow and its Utilization
Picohydro power plants represent a promising solution for decentralized energy generation in remote and underserved regions, leveraging the natural energy potential of small water flows. This research investigates the design principles, efficiency considerations, and socio-economic impacts of picohydro systems to assess their viability and potential contributions to sustainable development. The study begins with a comprehensive review of existing literature to establish foundational knowledge on picohydro technology and its applications. It then proceeds with empirical analyses, including case studies and field surveys, to gather firsthand data on system performance, water flow dynamics, and community perceptions. Computational simulations further optimize design parameters, such as turbine selection and system configuration, to maximize energy extraction efficiency under varying operational conditions. Key findings highlight the critical role of tailored engineering solutions in enhancing picohydro system performance and reliability. Socio-economic analyses underscore the transformative impact of picohydro installations on improving energy access, supporting local livelihoods, and stimulating economic growth in rural areas. Environmental assessments emphasize the importance of eco-friendly design practices to minimize ecological impacts and ensure sustainable operation.
Copyright (c) 2024 Gv Rian Simare-mare, Bintra Rotua Simbolon (Author) https://creativecommons.org/licenses/by/4.0
Optimizing Evacuation Routes During Volcanic Eruptions: A Comparative Analysis of Pathfinding Algorithms around Mount Sinabung
This research explores the optimization of evacuation routes amid volcanic eruptions around Mount Sinabung, employing sophisticated pathfinding algorithms the Bellman Ford and Floyd Warshall algorithms. Leveraging diverse datasets encompassing geographical, demographic, and historical information, the study aims to identify optimal evacuation paths considering factors such as terrain conditions, population distribution, and real-time adaptability. The study's methodology involves the integration of geospatial data, historical eruption records, and infrastructure details into structured graph representations, enabling algorithmic computations to determine efficient evacuation routes. Comparative analyses of the Bellman Ford and Floyd Warshall algorithms highlight their strengths, limitations, and applicability in dynamic volcanic scenarios, offering nuanced insights into their performance. The findings reveal optimized evacuation routes that prioritize safety, efficiency, and inclusivity, catering to diverse demographic needs. Additionally, future research directions outlined for refining pathfinding algorithms stress the importance of interdisciplinary collaboration, technological advancements, and community-centric approaches in enhancing disaster preparedness and response strategies. This research contributes to the evolving landscape of disaster management by offering evidence-based insights, actionable recommendations, and a roadmap for policymakers, emergency responders, and local authorities.
Copyright (c) 2024 Erikson Sinukaban, Franky Sotar Sitohang (Author) https://creativecommons.org/licenses/by/4.0
Optimizing Wind Energy Generation: Wind Speed Forecasting Using Elman Recurrent Neural Networks for Enhanced Power Generation in Turbines
This research delves into the realm of wind energy by exploring the accuracy of wind speed forecasting using the Elman Recurrent Neural Network (RNN) and its direct influence on the power generation of wind turbines. Leveraging historical wind speed data and employing the Elman RNN, this study demonstrates the model's precision in forecasting wind speeds, capturing temporal dependencies, and elucidating their impact on electricity output. Correlating these forecasts with actual power generation records, the research establishes a profound relationship, showcasing how even minor variations in predicted wind speeds significantly influence the amount of electricity produced by wind turbines. The study's findings underscore the critical role of accurate wind speed predictions in optimizing wind farm operations, enhancing energy capture efficiency, and contributing to grid stability. Furthermore, the research sets the stage for practical applications in renewable energy planning and policy-making, offering insights that shape the future trajectory of wind energy utilization. The research concludes by proposing avenues for further refinement in predictive models, real-time integration strategies, and long-term forecasting, guiding the path towards a sustainable and resilient energy landscape.
Copyright (c) 2023 Anthony Brandon, Richrad Xavier, Cley Valentine Ludwig (Author) https://creativecommons.org/licenses/by/4.0
Optimizing Anthurium Cultivation: Advancements in Greenhouse Design for Enhanced Plant Quality in Controlled Environments
Anthuriums, renowned for their captivating blooms and lush foliage, thrive in controlled environments. This research delves into the nuanced intricacies of greenhouse design, aiming to optimize growth conditions and enhance the quality of anthurium plants. Through meticulous experimentation and comparative analysis of various greenhouse configurations, the study explores the impact of structural elements, environmental variables, lighting strategies, and soil management on anthurium growth and flowering patterns. Distinct greenhouse structures, including glass, polycarbonate, and advanced plastics, were scrutinized for their effects on anthurium health and quality. Findings reveal the significance of elements such as insulation, humidity regulation, ventilation, and balanced lighting strategies in fostering superior plant vitality. Polycarbonate or advanced plastic structures emerged as optimal environments, ensuring stable conditions conducive to vibrant foliage, prolific blooms, and overall plant vigor. The research extends beyond plant quality, emphasizing economic viability and sustainability. While aesthetics traditionally favored glass structures, the evidence supporting energy efficiency and reduced maintenance costs of alternative materials underscores their practicality in commercial anthurium cultivation. These insights not only redefine greenhouse design paradigms but also serve as a guiding beacon for future research and industry practices. They pave the way for innovative approaches and sustainable cultivation methods, heralding a new era of precision and excellence in anthurium cultivation within controlled greenhouse environments.
Copyright (c) 2023 Albertus Bahri, Chairi Kamoga, Kinza Adiguna Pallawarukka (Author) https://creativecommons.org/licenses/by/4.0
Analyzing the Impact of Occupational Safety Programs on Work Productivity Using Fault Tree Analysis
This research delves into the intricate relationship between the implementation of an occupational safety program and its influence on work productivity within organizational settings. Employing fault tree analysis as a primary methodology, the study aims to scrutinize the efficacy of safety initiatives and delineate their direct correlation with productivity outcomes. Through a mixed-method approach integrating qualitative and quantitative analyses, the research examines the effectiveness of safety programs in mitigating workplace hazards and fostering a culture of safety. The fault tree analysis dissects causal factors, identifying critical pathways and root causes impacting work productivity. Findings reveal a symbiotic relationship between safety measures and productivity metrics. A well-executed safety program emerges as a catalyst for improved operational efficiency, employee engagement, and quality of work. The implications drawn from this study advocate for a paradigm shift, where safety initiatives are integrated strategically, nurturing an environment where safety and productivity coalesce seamlessly. This research contributes actionable insights for organizations to optimize safety measures, bolster productivity, and foster a culture where safety isn't just a priority but a fundamental value. Ultimately, the findings offer a compass guiding organizations towards a future where safety and productivity converge synergistically, shaping a landscape of sustained success and employee well-being.
Copyright (c) 2023 Alex Christhoper, Dareen Dominic (Author) https://creativecommons.org/licenses/by/4.0
Advancing Fault Diagnosis in BLDC Motors: Surge Test-Based Diagnostic Equipment for Phase to Ground Faults
This research introduces a groundbreaking approach to fault diagnosis in Brushless DC (BLDC) motors through the design and validation of specialized diagnostic equipment utilizing surge tests for phase to ground faults. The study focuses on identifying, localizing, and classifying various fault types, including insulation breakdowns, erosion, penetration, and partial grounding within BLDC motors. The research methodology encompasses theoretical frameworks, experimental validations, and comparative analyses with existing diagnostic methods. Surge tests conducted on BLDC motors with induced faults revealed distinct fault signatures, providing precise fault localization and aiding in the establishment of diagnostic criteria and thresholds. The findings showcased the developed equipment's precision, reliability, and automated fault classification capabilities, surpassing the limitations of traditional diagnostic methods. Comparisons with conventional techniques highlighted the advantages of the developed approach, emphasizing its heightened sensitivity, objectivity, and potential for predictive maintenance strategies. The equipment's ability to offer quantifiable fault parameters and establish diagnostic thresholds presents a transformative potential for proactive maintenance, minimizing downtime, and enhancing operational efficiency in BLDC motor-driven systems. The research findings underline the significance of surge tests and specialized diagnostic equipment in revolutionizing fault diagnosis practices for BLDC motors. The implications extend to industry-wide adoption, offering a pathway for enhanced reliability, safety, and operational continuity in various industrial applications.
Copyright (c) 2023 Stephen Larigaduelle, Jeseph Patrick William (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Hydrogen Production Efficiency in Water Electrolysis through Polycarbonate Integration from CD-R Waste
This research explores the integration of polycarbonate derived from CD-R (Compact Disc-Recordable) waste into water electrolysis for hydrogen production, aiming to assess its catalytic potential and influence on electrolysis efficiency. The study investigates the effects of waste-derived polycarbonate on hydrogen evolution rates, electrolysis kinetics, and operational parameters. Experimental trials reveal a notable enhancement in hydrogen production rates with the incorporation of waste-derived polycarbonate, suggesting its potential catalytic influence within the electrolysis process. However, observed variations and nonlinear concentration-efficiency relationships underscore the complexities and challenges in achieving consistent performance. Limitations related to material dispersion and compatibility highlight critical areas requiring optimization for practical implementation. The findings offer insights into the nuances of waste-derived material integration in electrochemical systems, emphasizing the need for further research to address challenges and optimize the utilization of waste-derived materials in advancing clean energy technologies.
Copyright (c) 2023 Matthews Romagnoli, Boiral Lymberopoulos Sevastya, Vanechka Valery (Author) https://creativecommons.org/licenses/by/4.0
Men's facial foam selection decision support system based on skin type
This research presents the development of a Decision Support System (DSS) aimed at assisting men in selecting facial foam products based on their skin types. The growing interest in skincare and grooming among men has led to an abundance of facial care products in the market, making it challenging for consumers to choose the most suitable option for their individual needs. The WDSS addresses this predicament by intelligently analyzing user input, classifying skin types, and generating personalized product recommendations. The conceptual framework of the WDSS combines content-based filtering and collaborative filtering techniques to ensure accuracy and relevance in recommending facial foam products. The Decision Support System offers a valuable tool for men seeking the most suitable facial foam products based on their individual skin types. The system's ability to provide personalized recommendations contributes to improved self-confidence and promotes proactive self-care practices among users. Continuous efforts in refining algorithms and updating the product database are essential to ensure the DSS's accuracy and relevancy as the skincare industry continues to evolve. The research seeks to empower men in their skincare journey, fostering a positive impact on their overall well-being and self-image.
Copyright (c) 2022 Jonhariono Sihotang, Roma Sinta Simbolon, Amran Manalu (Author) https://creativecommons.org/licenses/by/4.0
Fake news detection using naive bayes classifier and forward selection in the digital era
This study investigates the application of the Naive Bayes Classifier Method with the Forward Selection Technique in an effort to detect hoax news. Through a hypothetical numerical example, this study illustrates the basic steps involved in this approach. The Naive Bayes method is used to estimate class probabilities based on the selected text features, while the Forward Selection technique is used to select the most informative features. The results and implications of this approach are discussed in the context of potential real-world applications. This research provides an initial understanding of the use of these techniques in dealing with the challenge of detecting fake news in the digital age. While this research does not describe the more complex aspects of real-world fake news detection, it highlights a foundation that can be developed for further efforts to improve information integrity in an increasingly complex digital environment.
Copyright (c) 2022 Traore Lei Ogilvie, Monti Sharma, Zhou Xei Huu (Author) https://creativecommons.org/licenses/by/4.0
Design of an expert system for diagnosing eye disease using the certainty factor method
Accurate eye disease diagnosis is essential for therapy and vision preservation. Complexity and diversity of symptoms often confound established diagnostic methods. We suggest designing an expert system for eye illness diagnosis utilizing the certainty factor method, which handles ambiguous and imprecise medical diagnoses. A systematic technique to improve diagnostic accuracy is proposed in this research to bridge medical experience and computational reasoning. The research begins with a mathematical framework for symptoms, test data, and diagnostic conclusions. The system uses medical-inspired rule-based inference to aid evidence-based reasoning. The certainty factor technique quantifies diagnosis confidence, ensuring transparency and justifiability. A numerical example shows how to apply the strategy. The simple example shows how the expert system can analyze various criteria and make well-supported diagnoses. It emphasizes symptom analysis, test results, and the certainty factor method's capacity to handle uncertainty. This research emphasizes the interaction between artificial intelligence and medical competence and is conceptual. Real-world application involves medical practitioner participation, intensive testing, and patient data validation. This research advances medical diagnostic tools by combining computational and clinical knowledge. It symbolizes a shift toward efficient, accurate, and transparent diagnostic methods to improve patient care and healthcare.
Copyright (c) 2022 Birch Shah , Zeeshan , Asghar Sadiq, Übeyli Ung , Shanbhag Gilmore (Author) https://creativecommons.org/licenses/by/4.0
Optimizing face sensor-based attendance system using wavelet method for enhanced security and efficiency
In today’s digital era, efficient and accurate attendance management is essential in various sector. This research presents an innovative approach in the development of facial sensor-based attendance systems using the wavelet method. This approach aims to address the challenges of recognizing faces under varying lighting conditions, rotations, and facial details. Image pre-processing is used to improve the quality of the face image, followed by a discrete wavelet transform to decompose the image into wavelet coefficients at various scales. This model is strengthened by strong security techniques, considering the importance of individual data privacy. This research offers a modern alternative in attendance management by utilizing facial recognition technology and the wavelet method. Although the mathematically formulated model provides a clear framework, practical implementation requires advanced techniques in image processing and artificial intelligence.
Copyright (c) 2022 Priya Shimizu , Hagihara Kobatake, Subasi Tziritas Ezema, Maiti Eneh Bhanot, Das Junior (Author) https://creativecommons.org/licenses/by/4.0
Adaptive traffic control at complex intersections using fuzzy logic multi-agent approach
In an increasingly dense urban environment, efficient and adaptive traffic management is essential to maintain smooth mobility and reduce congestion. In this effort, the Multi-Agent Fuzzy Logic method emerges as a promising approach to overcome the complexity and fluctuation of traffic conditions. This study aims to investigate the potential application of the Multi-Agent Fuzzy Logic method in controlling traffic lights at complex crossroads. Within this conceptual framework, a mathematical formulation model and a programming algorithm are developed that enable the simulation of traffic light settings with the Multi-Agent Fuzzy Logic approach. By fuzzifying input variables to convert numeric data into linguistic variables. Furthermore, applying fuzzy rules to make adaptive decisions based on traffic conditions and coordination between agents in a multi-agent system. The results of this system are then validated through simulations, with evaluation metrics such as average waiting time, energy efficiency, and traffic smoothness. The results of this study indicate that the Multi-Agent Fuzzy Logic method is capable of producing traffic light settings that are responsive to changes in traffic conditions on each lane. By coordinating between agents, the average waiting time can be reduced, energy efficiency can be increased, and traffic flow can be improved.
Copyright (c) 2022 Zainal Yang Xu, Rivera Smith Ager, Niu Wylie Sjödin, Mubashar Wylie Pintrich (Author) https://creativecommons.org/licenses/by/4.0
Autonomous and sustainable air navigation technologies shaping the future of aeronautical engineering
Technology, regulation, operations, and environmental awareness are transforming the aviation business. This study examines aeronautical technologies, focusing on paradigm-shifting autonomous and sustainable air navigation. A multidimensional conceptual framework encompassing unmanned aerial vehicles (UAVs), electric and hybrid-electric propulsion, Urban Air Mobility (UAM), sustainable aviation fuels (SAFs), advanced avionics, AI, noise reduction technologies, and collaborative stakeholder engagement emerges from a comprehensive review of existing research. In a numerical route planning scenario, optimal route optimization reduces fuel consumption, emissions, and operational costs while meeting energy restrictions. This shows how mathematics helps solve air navigation's modern problems. A holistic, interdisciplinary strategy is needed to address the numerous, interwoven aspects of autonomous and sustainable air navigation. Technology and regulation must work together to integrate autonomous and sustainable technology. The research shows how air traffic management systems must balance safety, efficiency, and environmental stewardship. Aviation engineers, environmental specialists, regulatory authorities, lawmakers, and industry stakeholders must work together to make skies safer, more accessible, and environmentally friendly. This research helps aviation engineering enter a new era of autonomous and sustainable flight navigation. As the aviation industry moves toward this transformative horizon, it promises improved operational efficiency, environmental sustainability, and societal advancement, ushering in an age of aviation that harmonizes human aspirations with the vast sky
Copyright (c) 2021 Ribeiro Karanjikar Rae, Murashov Sutton Görür, Babiceanu Babiceanu, Sándor Sándor (Author) https://creativecommons.org/licenses/by/4.0
Advancing air navigation engineering for autonomous and sustainable aviation: navigating technological innovation and regulatory frameworks for sustainable flight
Autonomous flying and sustainability are transforming the aviation business. This study examines how technology, policy, and society affect air navigation engineering for autonomous and sustainable aviation. A thorough literature analysis shows how navigation algorithms, sensor systems, and communication networks enable safe and efficient autonomous operations. To address autonomous flight's specific problems, aviation authorities, stakeholders, and researchers must collaborate on regulatory evolution. To gain public trust, safety, privacy, and ethics must be addressed. These observations suggest integrating technical innovation, regulatory frameworks, and public education. Sustainable aviation techniques emphasize aircraft route optimization and emissions reduction. The research highlights the collaborative efforts needed to negotiate the complicated landscape of autonomous and sustainable aviation, resulting in an efficient, safe, and environmentally responsible sector. This study guides industry stakeholders, politicians, and researchers toward reimagined flight operations and aeronautical advancement as the aviation sector navigates autonomy and sustainability
Copyright (c) 2021 Zhaoxuan Ma Yang, Feng Chao Jung (Author) https://creativecommons.org/licenses/by/4.0
Decision support system for selection of major concentration using fuzzy logic
The choice of major concentration at the tertiary level is an important stage in one's educational development, involving various subjective factors such as interests, abilities, career goals, and subject preferences. To overcome the complexity of this process, we propose the development of a Fuzzy Logic-based Decision Support System (SPK) that can provide recommendations for major concentrations that are more in line with student profiles. In this study, we designed and implemented a DSS model that uses the Fuzzy Logic method to overcome uncertainty and ambiguity in concentration selection. The membership function that has been defined describes the degree of membership in each relevant set. Fuzzy rules are formed based on domain knowledge and historical data, and are applied in the inference process to produce recommendations for major concentrations. Fuzzy Logic in building a Decision Support System that can improve the process of selecting major concentrations
Copyright (c) 2021 Heo Wang Jee (Author) https://creativecommons.org/licenses/by/4.0