Artikel Ilmiah an. Arif Senja Fitrani

Artikel Ilmiah an. Arif Senja Fitran

An integrative review of computational methods for vocational curriculum, apprenticeship, labor market, and enrollment problems

Computational methods have been used extensively to solve problems in the education sector. This paper aims to explore the computational method's recent implementation in solving global Vocational education and training (VET) problems. The study used a systematic literature review to answer specific research questions by identifying, assessing, and interpreting all available research shreds of evidence. The result shows that researchers use the computational method to predict various cases in VET. The most popular methods are ANN and Naïve Bayes. It has significant potential to develop because VET has a very complex problem of (a) curriculum, (b) apprenticeship, (c) matching labor market, and (d) attracting enrollment. In the future, academics may have broad overviews of the use of the computational method in VET. A computer scientist may use this study to find more efficient and intelligent solutions for VET issues

Forecasting bus passenger flows by using a clustering-based support vector regression approach

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio

Class Attendance System in Education with Deep Learning Method

With the advancing technology, the hardware gain of computers and the increase in the processing capacity of processors have facilitated the processing of instantaneous and real-time images. Face recognition processes are also studies in the field of image processing. Facial recognition processes are frequently used in security applications and commercial applications. Especially in the last 20 years, the high performances of artificial intelligence (AI) studies have contributed to the spread of these studies in many different fields. Education is one of them. The potential and advantages of using AI in education; can be grouped under three headings: student, teacher, and institution. One of the institutional studies may be the security of educational environments and the contribution of automation to education and training processes. From this point of view, deep learning methods, one of the sub-branches of AI, were used in this study. For object detection from images, a pioneering study has been designed and successfully implemented to keep records of students' entrance to the educational institution and to perform class attendance with images taken from the camera using image processing algorithms. The application of the study to real-life problems will be carried out in a school determined in the 2022-2023 academic year.Comment: International LET-IN 2022 Conference Proceedings Book, October 06-08, 2022, Ankara, T\"urkiye. ISBN: 978-605-71971-1-

Development of Machine Learning based approach to predict fuel consumption and maintenance cost of Heavy-Duty Vehicles using diesel and alternative fuels

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and effective methods to predict fuel consumption, maintenance costs, and total cost of ownership for heavy-duty vehicles. Every improvement so achieved in this direction is a direct contributor to driving the reduction in the total cost of ownership for a fleet owner, thereby bringing economic prosperity and reducing oil imports for the economy. Motivated by these crucial goals, the present research considers integrating data-driven techniques using machine learning algorithms on the historical data collected from medium- and heavy-duty vehicles. The primary motivation for this research is to address the challenges faced by the medium- and heavy-duty transportation industry in reducing emissions and operating costs. The development of a machine learning-based approach can provide a more accurate and reliable prediction of fuel consumption and maintenance costs for medium- and heavy-duty vehicles. This, in turn, can help fleet owners and operators to make informed decisions related to fuel type, route planning, and vehicle maintenance, leading to reduced emissions and lower operating costs. Artificial Intelligence (AI) in the automotive industry has witnessed massive growth in the last few years. Heavy-duty transportation research and commercial fleets are adopting machine learning (ML) techniques for applications such as autonomous driving, fuel economy/emissions, predictive maintenance, etc. However, to perform well, modern AI methods require a large amount of high-quality, diverse, and well-balanced data, something which is still not widely available in the automotive industry, especially in the division of medium- and heavy-duty trucks. The research methodology involves the collection of data at the West Virginia University (WVU) Center for Alternative Fuels, Engines, and Emissions (CAFEE) lab in collaboration with fleet management companies operating medium- and heavy-duty vehicles on diesel and alternative fuels, including compressed natural gas, liquefied propane gas, hydrogen fuel cells, and electric vehicles. The data collected is used to develop machine learning models that can accurately predict fuel consumption and maintenance costs based on various parameters such as vehicle weight, speed, route, fuel type, and engine type. The expected outcomes of this research include 1) the development of a neural network model 3 that can accurately predict the fuel consumed by a vehicle per trip given the parameters such as vehicle speed, engine speed, and engine load, and 2) the development of machine learning models for estimating the average cost-per-mile based on the historical maintenance data of goods movement trucks, delivery trucks, school buses, transit buses, refuse trucks, and vocational trucks using fuels such as diesel, natural gas, and propane. Due to large variations in maintenance data for vehicles performing various activities and using different fuel types, the regular machine learning or ensemble models do not generalize well. Hence, a mixed-effect random forest (MERF) is developed to capture the fixed and random effects that occur due to varying duty-cycle of vocational heavy-duty trucks that perform different tasks. The developed model helps in predicting the average maintenance cost given the vocation, fuel type, and region of operation, making it easy for fleet companies to make procurement decisions based on their requirement and total cost of ownership. Both the models can provide insights into the impact of various parameters and route planning on the total cost of ownership affected by the fuel cost and the maintenance and repairs cost. In conclusion, the development of a machine learning-based approach can provide a reliable and efficient solution to predict fuel consumption and maintenance costs impacting the total cost of ownership for heavy-duty vehicles. This, in turn, can help the transportation industry reduce emissions and operating costs, contributing to a more sustainable and efficient transportation system. These models can be optimized with more training data and deployed in a real-time environment such as cloud service or an onboard vehicle system as per the requirement of companies

Практико-орієнтований підхід у навчанні фізики студентів нефізичних спеціальностей

The article deals with the main ways of applying the practice-oriented approach in the study of general physics of students in the areas of training 015.01 "Vocational education. Construction” and 014.10 “Secondary education. Labor training and technology": situational tasks and practical-oriented laboratory work. Differences in the subject of tasks and laboratory work are determined, which is connected with the specifics of future professional activity of students. For students the direction of preparation 015.01 "Professional education. Construction "professionally-oriented tasks are static tasks, problems on equinoxpressed motion, tasks on heat conductivity, heat exchange, calculations of electric circuits of alternating current, determination of illumination in the room. For students the direction of preparation is 014.10 "Secondary education. Labor studies and technologies »such tasks are tasks on tension-compression, tasks on the dynamics of rotational motion, the problem of changing the aggregate state of matter, the problem of calculating the circuits of constant and alternating current, the task of determining the illumination of the room. Practical-oriented laboratory work also takes into account the specifics offuture professional activities of students. The application of a practice-oriented approach to learning contributes to the growth of student motivation and, accordingly, their success in the process of studying general physics.У статті розглядаються основні шляхи застосування практико-орієнтованого підходу у навчанні загальної фізики студентів напрямів підготовки 015.01 «Професійна освіта. Будівництво» і 014.10 «Середня освіта. Трудове навчання та технології»: ситуаційні задачі і практико-орієнтовані лабораторні роботи. Визначено відмінності у тематиці задач і лабораторних робіт, що пов’язано зі специфікою майбутньої професійної діяльності студентів. Для студентів напряму підготовки 015.01 «Професійна освіта. Будівництво» професійно-орієнтованими задачами є задачі зі статики, задачі на рівноприскорений рух, задачі на теплопровідність, теплообмін, розрахунки електричних кіл змінного струму, визначення освітленості у приміщенні. Для студентів напряму підготовки 014.10 «Середня освіта. Трудове навчання та технології» такими задачами є задачі на розтяг-стиск, задачі з динаміки обертального руху, задачі про зміну агрегатних станів речовини, задачі на розрахунок кіл постійного та змінного струму, задачі на визначення освітленості приміщення. Практико-орієнтовані лабораторні роботи теж враховують специфіку майбутньої професійної діяльності студентів. Застосування практико-орієнтованого підходу у навчанні сприяє зростанню мотивації студентів і, відповідно, їх успішності у процесі вивчення загальної фізики

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 341)

This bibliography lists 133 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during September 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Clustering Of Student Learning Styles in the industri 4.0 Using KMeans Algorithm

Clustering is a technique for grouping homogeneous data so that the points in each cluster are as similar as possible according to convenience measures such as Euclidean-based distance or correlation-based distance. In the industrial era 4.0, learning media, the environment, the way teachers teach will affect student learning styles. From research on learning styles, many researchers agree on the importance of identifying learning styles to accelerate their learning performance. The purpose of this study is to classify student learning styles in the industrial era 4.0 using the Kmeans algorithm and the elbow method. The research method used is a waterfall. The number of research subjects was 108 students. the results of the research on the number of clusters (K), namely 6, obtained cluster 1 as many as 27 students, cluster 2 as many as 24 students, cluster 3 as many as 21 students, cluster 4 as many as 17 students, cluster 5 as many as 11 students and cluster 6 as many as 8 students. The performance of the grouping results based on the silhouette coefficient is 0.302, which means the grouping structure is weak. In cluster 1, the highest number has auditory elements, followed by kinesthetic and visual elements. The development of ICT-based media is one of the factors of student learning styles in the industrial era 4.