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This study aims to determine the management of workshop and laboratory equipment in vocational education. This research uses a qualitative approach with a case study method. The research subjects were the head of the department, the head of the workshop, the lecturer and the workshop technician. Data collection techniques used are observation, interviews, and documentation. The results showed that the workshop equipment management, namely: Workshop equipment planning carried out through several stages of the procedure: the needs analysis based on the curriculum, determine the priority scale, and determine the budget. This plan involves all personnel in the organizational structure. In the workshop, there are some activities done: preparation of materials and equipment, equipment lending, and use of the workshop. Preparation of practice materials is carried out at the beginning of each semester by following the existing curriculum. Equipment lending is carried out by providing equipment lending cards that are filled out before and after practice. Supervision of equipment in the workshop was controlled by a lending card and equipment usage as well as checking equipment before and after practice. The final results of this supervision will be in the form of an annual report given to the school as an evaluation for future planning.

This study aims to determine the level of planning, implementation, and assessment of learning outcomes in the XI class of shielded metal arc welding (SMAW) techniques at the state vocational high school 1 Sedayu, Bantul Regency. This type of research is a descriptive study with a survey method. Data collection using a questionnaire. The study population was 90 students of class XI state vocational high school 1 Sedayu. The sample was taken the same as the population. Data collection using a questionnaire. The data analysis technique used a quantitative descriptive analysis. Based on the data analysis results, the planning category is medium, with a mean of 29.48. The implementation is a very high category with a mean of 49.60, the assessment of learning outcomes categorized as high with a mean of 10.68. The results showed that the evaluation SMAW technical learning was in the high category. Results also showed that the level of evaluation of the SMAW technique learning on planning is in the medium category and the evaluation of the implementation of the SMAW technique learning was in the very high category.

This study aims to determine the achievement of competency standards of graduates in the fields of lathe machining, milling machining, grinding machining, CNC machining at Piri 1 Yogyakarta Vocational High School. This study uses a descriptive method with a quantitative approach. The subjects of this study were teachers and students of class XII of the engineering technique of Piri 1 Yogyakarta Vocational High School. The data collection technique used a questionnaire and documentation. The data analysis technique used is descriptive statistical analysis. Based on the results of the research, it is known that the achievement of the competency standards of lathe machining graduates is in the excellent category with an average value of 82.51 and the results of the questionnaire show the excellent category The achievement of competency standards for milling machining graduates was in the excellent category with an average score of 84.74 and the results of the questionnaire showed an excellent category. The achievement of the competency standard of grinding machining graduates is in the excellent category with an average score of 80.42, and the results of the questionnaire show an excellent category. The achievement of competency standards for CNC machining graduates is in the excellent category with an average score of 77.81, and the results of the questionnaire show an excellent category.

This study aims to reveal the relevance of practical competencies in mechanical engineering vocational schools to the industry's competencies in the Yogyakarta Special Region. This research is a quantitative descriptive study using survey research methods with five research subjects representing the manufacturing industry. The results of this study are, in general, the competence of learning machining practices at Mechanical Engineering Vocational Schools is very relevant to the manufacturing industry's needs. Details of competency relevance data for each practical subject are as follows: (1) competency in lathe machining 42.10% relevant and 57.90% highly relevant; (2) competence of 40.90% milling machining is relevant and 59.10% highly relevant; (3) competence in grinding 33.33% relevant and 66.67% very relevant; (4) numerical control/computer numerical control and computer aided manufacturing competences 2.90% relevant and 97.10% highly relevant.

Revitalization of Vocational High Schools (VHS) is an effort to increase the role of all stakeholders in developing education. This study aimed to measure the level of machining industry contribution in VHS revitalization, especially in Machining Skills Competence in Yogyakarta Special Region (DIY). This study used descriptive research with quantitative approaches. The population was 20 machining industry in Yogyakarta Special Region. Samples were determined by saturated sampling techniques. The number of samples selected were 14 machining industries. Questionnaires were used to measure the level of the machining industry contribution in implementation of VHS revitalization. Instrument validation used logical and empirical validity tests. Quantitative data analysis techniques used descriptive analysis. The results of the study showed that the level of machining industry contribution in VHS's revitalization, especially in Machining Skills Competence in DIY as a whole was in the \"very low\" category with a score of 42.67%.

International surveys, such as TIMSS and PISA, frequently put Indonesia in the low ranks. It is an indication that the higher-order thinking skills (HOTS) of students in Indonesia are still low. This research aims to analyze students’ difficulties in solving problems that measure HOTS. This is a case study research with a qualitative approach. Participants studied were 93 high school students in grade XI. Data were collected using test instruments that measure HOTS, which was developed based on the standard contents of high school mathematics. The difficulties were analyzed descriptively by observing students’ errors in answering HOTS test items. Students’ errors were classified based on Newman’s Error Procedure (NEP). The result shows that around 8.33% of the students had difficulties in comprehension, 15.59% in transformation, 32.53% in process skills, and 1.34 % in encoding.

A number of researchers prefer using multi-channel surface electromyography (sEMG) pattern recognition in hand gesture recognition to increase classification accuracy. Using this method can lead to computational complexity. Hand gesture classification by employing single channel sEMG signal acquisition is quite challenging, especially for low-rate sampling frequency. In this paper, a study on the pattern recognition method for sEMG signals of nine finger movements is presented. Common surface single channel electromyography (sEMG) was used to measure five different subjects with no neurological or muscular disorder by having nine hand movements. This research had several sequential processes (i.e., feature extraction, feature reduction, and feature classification). Sixteen time-domain features were employed for feature extraction. The features were then reduced using principal component analysis (PCA) into two and three-dimensional feature space. The artificial neural network (ANN) classifier was tested on two different feature sets: (1) using all principal components obtained from PCA (PC1–PC3) and (2) using selected principal components (PC2 and PC3). The third best principal components were then used for classification using ANN. The average accuracy using all subject signals was 86.7% to discriminate the nine finger movements.

Background This study used machine learning techniques to evaluate cardiovascular disease risk factors (CVD) and the relationship between sex and these risk factors. The objective was pursued in the context of CVD being a major global cause of death and the need for accurate identification of risk factors for timely diagnosis and improved patient outcomes. The researchers conducted a literature review to address previous studies' limitations in using machine learning to assess CVD risk factors. Methods This study analyzed data from 1024 patients to identify the significant CVD risk factors based on sex. The data comprising 13 features, such as demographic, lifestyle, and clinical factors, were obtained from the UCI repository and preprocessed to eliminate missing information. The analysis was performed using principal component analysis (PCA) and latent class analysis (LCA) to determine the major CVD risk factors and to identify any homogeneous subgroups between male and female patients. Data analysis was performed using XLSTAT Software. This software provides a comprehensive suite of tools for Data Analysis, Machine Learning, and Statistical Solutions for MS Excel. Results This study showed significant sex differences in CVD risk factors. 8 out of 13 risk factors affecting male and female patients found that males and females share 4 of the eight risk factors. Identified latent profiles of CVD patients, suggesting the presence of subgroups among CVD patients. These findings provide valuable insights into the impact of sex differences on CVD risk factors. Moreover, they have important implications for healthcare professionals, who can use this information to develop individualized prevention and treatment plans. The results highlight the need for further research to elucidate these disparities better and develop more effective CVD prevention measures. Conclusions The study explored the sex differences in the CVD risk factors and the presence of subgroups among CVD patients using ML techniques. The results revealed sex-specific differences in risk factors and the existence of subgroups among CVD patients, thus providing essential insights for personalized prevention and treatment plans. Hence, further research is necessary to understand these disparities better and improve CVD prevention.

Driver behavior is a variable that significantly influences fuel use, which is a very concerning issue due to the high cost of fossil fuels caused by the limited amount of energy in the market. Therefore, several breakthroughs have been conducted to realize vehicles with high fuel efficiency. This is in addition to the continuous study of electric, hybrid, gas, and fuel cell vehicles, as well as the development of intelligent control systems. Research on driver behavior has been carried out with several variables, however, none have been conducted on this factor related to fuel consumption. This research aims to review the development of driver behavior as the supporting variable in vehicles. Data were collected from dozens of scientific articles stored in search engines, such as Science Direct, Scopus, Springer link, and ProQuest. The articles found were then filtered based on the closeness with the themes discussed, hence only 13 were reviewed and grouped into five research theme areas. These include car, safety systems, vehicle and emission control, as well graphic display themes. The results provided an overview of the potential development of driver behavior in the future.

The Internet of Things (IoT) and Wireless Sensor Networks (WSNs) heavily rely on the lifetime of sensor nodes, which is inversely proportional to transmission power. Nodes with greater separation demand higher transmission power, while those closer together require less power. In practice, node placement varies significantly due to diverse terrain and contours, making power transmission configuration a critical and challenging issue in WSNs. This paper introduces an Enhanced Grey Wolf Optimization (EGWO) algorithm designed to optimize power transmission in WSN environments. Traditional Grey Wolf Optimization (GWO) employs a parameter that decreases linearly with iterations to regulate exploitation. In contrast, the proposed EGWO adopts a concave decline in the exploitation rate, allowing for more precise optimization in areas under exploration. The enhancement utilizes a cosine function that gradually decreases from 1 to 0, providing a smoother and more controlled transition. The experimental results demonstrate that EGWO outperforms other optimization algorithms. The proposed method achieves the lowest fitness value of −4.21, compared to 1.22 for standard GWO, −2.81 for PSO, and 2.86 for BESO, indicating its superiority in optimizing power transmission in WSNs.