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Vertex
Published by Institute of Computer Science
ISSN : 2089385X     EISSN : 28296761     DOI : https://doi.org/10.35335/Vertex
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
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).
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 36 Documents
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Optimization-based decision support system for accurate earthquake epicenter determination Roma Sinta Simbolon; Methodius Tigor
Vertex Vol. 11 No. 1 (2021): December: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/hd5are53

Abstract

Accurate determination of earthquake epicenters is vital for effective disaster response and risk mitigation. This research proposes a Decision Support System (DSS) that leverages optimization techniques to enhance earthquake epicenter determination accuracy. The DSS combines seismic data processing, geospatial analysis, and advanced optimization algorithms to pinpoint epicenters with high precision. The study presents a mathematical formulation to minimize the misfit between observed and theoretical travel-time data using optimization algorithms. A numerical example showcases the DSS's effectiveness in accurately localizing seismic events. The research demonstrates that the DSS outperforms traditional methods, providing valuable insights for seismic monitoring agencies and disaster response teams. The DSS offers a promising solution for real-world applications, contributing to community safety and disaster preparedness in seismic-prone regions
Web-Based on-line learning (e-learning) decision support system Firta Sari Panjaitan; Sonya Enjelina Gorat
Vertex Vol. 11 No. 1 (2021): December: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/kpwbk147

Abstract

The rapid advancement of technology has revolutionized education, paving the way for innovative learning methods such as E-Learning. However, optimizing the effectiveness of online education poses challenges in data management and decision-making processes. This research investigates the integration of Web-Based Decision Support Systems (DSS) in E-Learning to enhance learning outcomes. The study develops a mathematical formulation that quantifies the impact of DSS by considering student engagement, knowledge retention, and academic achievement. A numerical example is presented to demonstrate the application of the formulation, showcasing the positive influence of the DSS on individual students and the overall cohort. The results emphasize the potential benefits of personalized learning experiences, data-driven insights, and informed decision-making facilitated by the DSS. Nonetheless, the limitations of the study are acknowledged, warranting further research with larger and more diverse samples. Overall, this research contributes to the discourse surrounding the role of Web-Based DSS in shaping the future of online education, empowering educators and learners to unlock the full potential of E-Learning in the digital age
Decision support system for selection of major concentration using fuzzy logic Heo Wang Jee
Vertex Vol. 11 No. 1 (2021): December: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/f1j7ys60

Abstract

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
Advancing air navigation engineering for autonomous and sustainable aviation: navigating technological innovation and regulatory frameworks for sustainable flight Zhaoxuan Ma Yang; Feng Chao Jung
Vertex Vol. 11 No. 1 (2021): December: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/716k5h38

Abstract

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
Autonomous and sustainable air navigation technologies shaping the future of aeronautical engineering Ribeiro Karanjikar Rae; Murashov Sutton Görür; Babiceanu Babiceanu; Sándor Sándor
Vertex Vol. 11 No. 1 (2021): December: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/m44pf111

Abstract

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
Adaptive traffic control at complex intersections using fuzzy logic multi-agent approach Zainal Yang Xu; Rivera Smith Ager; Niu Wylie Sjödin; Mubashar Wylie Pintrich
Vertex Vol. 11 No. 2 (2022): June: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/0meps156

Abstract

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.
Optimizing face sensor-based attendance system using wavelet method for enhanced security and efficiency Priya Shimizu; Hagihara Kobatake; Subasi Tziritas Ezema; Maiti Eneh Bhanot; Das Junior
Vertex Vol. 11 No. 2 (2022): June: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/629k6b98

Abstract

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.
Design of an expert system for diagnosing eye disease using the certainty factor method Birch Shah; Zeeshan; Asghar Sadiq; Übeyli Ung; Shanbhag Gilmore
Vertex Vol. 11 No. 2 (2022): June: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/vw11e535

Abstract

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.
Fake news detection using naive bayes classifier and forward selection in the digital era Traore Lei Ogilvie; Monti Sharma; Zhou Xei Huu
Vertex Vol. 11 No. 2 (2022): June: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/rxg67q35

Abstract

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.
Men's facial foam selection decision support system based on skin type Jonhariono Sihotang; Roma Sinta Simbolon; Amran Manalu
Vertex Vol. 11 No. 2 (2022): June: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/vxydbw52

Abstract

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.

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