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Bulletin of Engineering Science, Technology and Industry
Published by PT. RADJA INTERCONTINENTAL PUBLISHING
ISSN : -     EISSN : 30255821     DOI : https://doi.org/10.59733/besti
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering
Bulletin of Engineering Science, Technology and Industry | ISSN: 3025-5821 is a peer-reviewed journal that publishes popular articles in the fields of Engineering, Technology and Industrial Science. This journal is published 4 times a year, namely in March, June, September and December. We invite scientists, practitioners, researchers, lecturers and students from various countries and institutions to contribute to publishing their work and research results in the fields of Engineering, Technology and Industry.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 15 Documents
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Search results for , issue "Vol. 2 No. 3 (2024): September" : 15 Documents clear
ARTIFICIAL INTELLIGENCE IN COMPUTER ENGINEERING: PSYCHOLOGICAL APPROACHES TO UNDERSTANDING HUMAN BEHAVIOR Shreyasa Rani Dubey; Diwakar Pandey
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.49

Abstract

The integration of Artificial Intelligence (AI) in computer engineering has significantly advanced the field's ability to understand and predict human behavior. This research article explores the intersection of AI and psychological approaches, examining how computational models can simulate cognitive processes and emotional responses. By leveraging machine learning algorithms and neural networks, the study demonstrates how AI systems can analyze vast datasets to identify patterns in human behavior, providing insights into decision-making, social interactions, and mental health. The article also discusses the ethical implications of AI-driven behavioral analysis and the potential for enhancing human-computer interactions. Through a comprehensive review of current methodologies and case studies, this research highlights the transformative impact of AI on understanding human behavior and proposes future directions for integrating psychological theories with AI technologies to further enhance the accuracy and applicability of behavioral predictions in computer engineering.
COLLABORATIVE LEARNING IN ENGINEERING: DEVELOPING TEAMWORK AND PROBLEM-SOLVING SKILLS Renuka Patel
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.50

Abstract

Engineering education, emphasizing the development of teamwork and problem-solving skills essential for modern engineering practice. This study explores the impact of collaborative learning environments on engineering students' ability to work effectively in teams and tackle complex problems. Through a combination of qualitative and quantitative research methods, including surveys, interviews, and performance assessments, the study evaluates students' skill development over a semester-long course designed with collaborative projects. Findings indicate significant improvements in communication, coordination, and conflict resolution abilities, as well as enhanced problem-solving proficiency. The research highlights the importance of structured teamwork activities and reflective practices in fostering these essential skills, suggesting that collaborative learning should be an integral component of engineering curricula to better prepare students for the demands of the professional engineering environment.
THE ROLE OF VIRTUAL REALITY IN COMPUTER ENGINEERING: IMPACTS ON COGNITIVE AND EMOTIONAL PROCESSES Vikrant Sharma
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.51

Abstract

Virtual Reality (VR) technology has emerged as a transformative tool in computer engineering, offering immersive experiences that profoundly influence cognitive and emotional processes. This research explores the multifaceted impacts of VR on human cognition and emotion, examining its applications across various domains such as education, healthcare, and entertainment. By simulating interactive environments, VR enhances spatial awareness, memory retention, and decision-making abilities through realistic simulations and interactive learning experiences. Furthermore, VR's ability to evoke emotions and empathy fosters deeper engagement and emotional resonance in users. This paper reviews empirical studies and theoretical frameworks that elucidate the mechanisms through which VR influences cognitive processes, including attention, perception, and learning, as well as emotional responses such as presence and immersion. Insights gained from this research contribute to understanding VR's potential in enhancing human-computer interaction and shaping future advancements in computer engineering.
NEURAL NETWORKS IN COMPUTER ENGINEERING: INSIGHTS FROM COGNITIVE PSYCHOLOGY Diwakar Pandey
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.52

Abstract

Neural networks have become instrumental in advancing computer engineering by drawing insights from cognitive psychology. This research article explores the synergy between neural network models and cognitive psychology theories, highlighting how computational models simulate human cognitive processes. By integrating principles of memory, learning, and decision-making from cognitive psychology, neural networks emulate complex human behaviors and intelligence. The article reviews current methodologies and case studies to illustrate the application of neural networks in solving engineering challenges, such as pattern recognition, natural language processing, and autonomous systems. Ethical considerations and future directions for enhancing neural network capabilities through cognitive psychology are also discussed, emphasizing the transformative impact of this interdisciplinary approach on computer engineering and cognitive science research.
ASSESSMENT OF RIGID PAVEMENT CONDITION USING PAVEMENT CONDITION INDEX METHOD ON THE DENAI-MANDALA BYPASS ROAD SECTION IN MEDAN CITY Aldyoki Firmansyah Matondang; Defry Basrin; Haikal Fajri
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.53

Abstract

Road pavement is a critical component of the transportation system that requires maintenance to ensure the pavement remains safe and comfortable for traffic users. Assessing road conditions is crucial for identifying various types of potential damage, thus necessitating specific methods for evaluating road damage conditions. The Pavement Condition Index (PCI) method is commonly used to assess pavement conditions. The aim of this research is to evaluate rigid pavement damage on the Denai-Mandala Bypass road section in Medan City using the Pavement Condition Index (PCI) method. The methodology used in this research involves field surveys for collecting pavement condition data and analyzing the data using the PCI method to determine pavement conditions and types of damage. This research indicates that the rigid pavement on the Denai-Mandala Bypass road section in Medan City experiences various types of damage, including corner break (2 occurrences), divided slab (1 occurrence), durability (“D”) cracking (2 occurrences), faulting (6 occurrences), joint seal damage (10 occurrences), linear cracks (78 occurrences), patching large (11 occurrences), patching small (1 occurrence), polished aggregate (5 occurrences), punchout (2 occurrences), scalling (4 occurrences), shrinkage cracks (10 occurrences), spalling corner (5 occurrences), and spalling joint (30 occurrences). The average PCI value on the Denai road section examined for approximately ±1.4 km is 78.53, categorized as satisfactory with periodic maintenance as the priority for handling, while on the Mandala Bypass road section examined for approximately ±800 m, the average PCI value is 89.93, categorized as very good with routine maintenance as the priority for handling. Assessing pavement conditions using the PCI Method aids in identifying occurring damage and provides necessary information for effective pavement repair planning.
COMPARISON OF K-NEAREST NEIGHBOR ALGORITHM AND SUPPORT VECTOR MACHINE IN CLASSIFICATION ARRHYTHMIA IN ECG SIGNALS Yennimar; Julfan Farman Zebua; Wikesyah Putri Sitorus; Yans YMP Situmorang; Juilo Diamond Sitepu
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.56

Abstract

Heart disease is one of the biggest causes of death in Indonesia, one of which is arrhythmia, which is a heart rhythm disturbance or a pattern of rapid changes in normal heart rate. Early detection of arrhythmias is very important in reducing the risk of death. In this study, a machine learning approach was used to classify arrhythmias through ECG signal analysis using the K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) algorithms. The research results show that the K-NN algorithm managed to achieve an accuracy of 82.61%, while the SVM algorithm achieved an accuracy of 79.35%. This shows that the K-NN algorithm has better performance than SVM in the classification of arrhythmia diseases from ECG signals. With higher accuracy, K-NN can identify arrhythmias more precisely, which is very important in the context of early detection. Accurate early detection allows for quicker and more appropriate medical intervention, thereby reducing the risk of serious complications and death. Implementing a K-NN-based early arrhythmia detection system can be an effective solution to be implemented on a wider scale, such as in hospitals or clinics. With this technology, medical personnel can more quickly and accurately diagnose arrhythmias, so that treatment can be carried out earlier. This is very important considering the high death rate due to heart disease in Indonesia. Overall, this study makes an important contribution to the development of an effective early detection system for arrhythmias. By using the K-NN algorithm which has proven to be more accurate, it is hoped that this system can help reduce the death rate due to heart disease in Indonesia. Additionally, further research is needed to continue improving the accuracy and effectiveness of these systems, as well as to explore the potential use of other machine learning algorithms in the medical field.
STUDY OF RECOMMENDATIONS FOR SUSTAINABLE FOOD AGRICULTURAL LAND PROTECTION IN DETERMINING REGIONAL SPATIAL PLANNING SOUTHEAST ACEH DISTRICT CASE STUDY Mas Pratono; Feby Milanie
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.58

Abstract

Protecting sustainable food agricultural land is an important component as a forerunner in recommendations for the demolition of space as a food crop area plan, especially in Southeast Aceh Regency which has great potential in the agricultural sector. This research aims to analyze the challenges and opportunities in integrating urban development and agricultural land conservation, as well as providing strategic recommendations in determining the Regional Spatial Planning (RTRW) in Southeast Aceh Regency. The research method used is a qualitative approach with a case study in Southeast Aceh Regency. This research involves agricultural extension workers as executors in collecting data and filling in agricultural attributes based on actual conditions in the field which will later be processed into spatial data to become spatial data on potential land in Southeast Aceh Regency. The data analysis method was carried out using content analysis techniques to identify relevant main themes. This research involves the working group (Pokja) on sustainable food agricultural land (LP2B) as stakeholders who provide input regarding policies, directions and technical provisions in recommendations for the protection of sustainable food agricultural land. Current zoning policies and Regional Spatial Planning (RTRW) are not yet fully effective in protecting agricultural land. The government needs to strengthen regulations and introduce economic incentives to encourage farmers to defend their land. The establishment of conservation areas is also important to maintain biodiversity and important ecosystems. A sustainable development approach through Smart Growth strategies and green infrastructure can reduce urban land expansion and improve environmental quality. Community participation in the spatial planning process is also very important. Public involvement through consultation forums and environmental education programs can increase awareness of the importance of protecting agricultural land. The use of technology such as geographic information systems and remote sensing is very helpful in monitoring land use changes and planning sustainable urban development. This technology enables accurate spatial data management to support decision making. The strategic recommendations provided include strengthening agricultural land protection regulations, providing economic incentives to farmers, adopting sustainable development principles in spatial planning, and increasing environmental education programs. In addition, the use of geographic information system technology must be optimized for effective land mapping and monitoring. In conclusion, protecting food agricultural land in determining RTRW in Southeast Aceh Regency requires a holistic approach that includes appropriate policies, good planning, community participation and use of technology.With this approach, urban development can take place in a sustainable manner without sacrificing land which is important for food security and environmental sustainability.
EVALUATION OF INFORMATION TECHNOLOGY GOVERNANCE E-KINERJA SYSTEMS IN ASSESSING EMPLOYEE PERFORMANCE USING THE MODEL COBIT 2019 AT THE DISTRICT COMMINFO OFFICE WAS REALLY FUN Eswin Syahputra; Khairul; Muhammad Iqbal; Rian Farta Wijaya; Darmeli Nasution
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.60

Abstract

The Department of Communication and Information Technology (Kominfo) in developing an e-performance information system needs to carry out a governance evaluation. The aim of this research is to evaluate information technology governance in the E-Kinerja system in assessing employee performance at the Bener Meriah Regency Communication and Information Service using the COBIT 2019 model using the Action Research method. Data collection techniques use two sources of primary data and secondary data. The results of this research are three main domains in COBIT 2019, namely EDM02 (Ensured Benefits Delivery), APO10 (Managed Vendors), and BAI11 (Managed Projects). This evaluation is carried out to measure the targeted capability level (to-be), the current capability level (as-is), as well as the gaps (GAP) that exist between the two. So the capability level in the EDM02 domain is at level 4, the APO10 domain is at level 2 and the BAI11 domain is at level 2. These findings provide an overview of areas that require further improvement and development to achieve more effective and efficient information technology governance. Thus, it is hoped that this research can contribute to improving the quality of information technology governance at the Bener Meriah Regency Communication and Information Service, as well as becoming a reference for other government agencies in implementing the COBIT 2019 model for evaluating information technology systems.
ANALYSIS OF THE LEVEL OF EFFECTIVENESS OF THE INDEPENDENT CAMPUS MERDEKA LEARNING PROGRAM (MBKM) USING METHODSPREFERENCE SELECTION INDEX (PSI) AND VIKOR METHOD Kiki Artika; Muhammad Iqbal; Zulham Sitorus; Andysah Putera Utama Siahaan; Rian Farta Wijaya
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.61

Abstract

This research aims to analyze the level of effectiveness of the Independent Campus Learning Program (MBKM) using the Preference Selection Index (PSI) Method and VIKOR Method. The MBKM program is an initiative of the Ministry of Education and Culture of the Republic of Indonesia which aims to provide more flexibility and learning opportunities for students through various off-campus activities. This research was conducted to measure the extent to which the program succeeded in achieving its goals. The PSI method is used to determine preferences for various aspects of the program based on assessments from students and academic staff, while the VIKOR method is used to identify the best compromise solution that can maximize stakeholder satisfaction. Analysis was carried out to assess the effectiveness of the program based on several criteria, including the quality of the learning experience, relevance to the world of work, and contribution to student skills development. This research suggests that to further increase the effectiveness of the MBKM Program, there needs to be an emphasis on developing a curriculum that is more responsive to industry needs and improving supporting facilities for students. The implications of the results of this research are important for policy makers in designing educational strategies that are more adaptive and oriented to labor market needs.
MACHINE LEARNING ANALYSIS IN IMPROVING THE EFFICIENCY OF THE STUDENT ADMISSION DECISION MAKING PROCESS NEW AT PANCA BUDI MEDAN DEVELOPMENT UNIVERSITY M. Rasyid; Zulham Sitorus; Rian Farta Wijaya; Muhammad Iqbal; Khairul
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.62

Abstract

The decision-making process in admitting new students is a crucial aspect that can influence the quality and efficiency of academic administration in higher education. This research aims to analyze the role of Machine Learning methods, especially Support Vector Machines (SVM), in increasing the efficiency of the decision-making process for new student admissions at the Panca Budi Development University, Medan. The data used in this research includes information from the student admissions process for the odd semester of the 2022/2023 academic year, which includes various variables such as Registration Number, School of Origin, Registration Payment, and others. The data is divided into a training set (70%) and a testing set (30%). The Support Vector Machine (SVM) model that was built was evaluated using metrics such as accuracy, precision, recall, and F1-Score. The research results show that the SVM model achieves an accuracy of 100%, with high precision and recall for both classes. Precision for both classes reached 1.00, while recall for the minority class (class 1) reached 0.91, indicating excellent model performance in classification. The conclusion of this research is that the Support Vector Machine (SVM) model can significantly increase efficiency and accuracy in the decision-making process for new student admissions at the Panca Budi Development University in Medan compared to conventional methods. These findings indicate that the application of Machine Learning methods can provide substantial benefits in the context of academic administration.

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