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This study aims to design and develop an electrical installation panel by applying engineering safety principles in the water spray booth of a vehicle body painting system. Field observations indicate that electrical panels in painting rooms often do not meet safety standards, which can lead to short circuits and potential fire hazards. The research employed a Research and Development (R&D) method using a simplified Borg and Gall model consisting of nine stages, starting from problem identification to effectiveness testing. Expert validation results obtained a score of 87.5% (highly valid), practicality testing yielded 90% (very practical), and effectiveness tests showed an average current of 4.1 A, with both the MCB and emergency stop functioning optimally. The developed panel product is declared feasible to be used as a practical learning media for automotive electrical systems. Based on the test results, the panel product was declared suitable for use and can be used as a learning medium in automotive electrical practice, helping to increase understanding of the application of safety in electrical installations in the automotive industry.  

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

This study aims to determine the relationship between learning motivation and learning outcomes in SMAW welding engineering among eleventh-grade students at SMKN 2 Solok. This research employed a quantitative method with a correlational approach. The population consisted of all students from classes XI TPM 1 and XI TPM 2, totaling 51 students, using a total sampling technique. Learning motivation data were collected through a validated and reliable questionnaire, while learning outcome data were obtained from post-test scores in the SMAW welding subject. Data were analyzed using the Pearson Product Moment correlation test with the assistance of SPSS software. The results showed a correlation coefficient of r = 0.783 with a significance value of 0.000 < 0.01, indicating a strong, positive, and statistically significant relationship between learning motivation and students’ learning outcomes. These findings suggest that higher learning motivation leads to better learning outcomes in SMAW welding engineering. Therefore, learning motivation plays an important role in improving students’ academic performance. This study is expected to provide useful insights for teachers and schools in developing instructional strategies that enhance students’ motivation and learning outcomes.

This systematic literature review examines research trends in problem-based learning (PBL) within blended learning contexts by analyzing 64 peer-reviewed articles from Scopus database published between2020-2024. Using PRISMA methodology, this study identifies key patterns including the dominance of medical education contexts (71.9%), the significant impact of COVID-19 pandemic on pedagogical innovations, and the prevalence of experimental research designs (34.4%). Findings reveal that blended learning emerged as the most researched approach (61%), followed by online/distance learning (23%) and flipped classroom models (11%). The analysis indicates increasing publication trends in 2022 and 2024, with Education Sciences and BMC Medical Education as leading publication venues. This review highlights research gaps in engineering and technology education contexts, limited exploration of long-term learning outcomes, and insufficient mixed-methods studies. These findings provide valuable insights for educators, researchers, and policymakers in designing effective learning strategies that integrate problem-based and blended learning approaches in diverse educational settings.

Vocational education requires a practice-based learning process supported by complete facilities so that students can master work competencies optimally. However, the condition of the Motorcycle Engineering workshop at Noemuti State Vocational High School shows that the completeness of the disc brake system practice facilities does not meet the ideal ratio standards, which affects the effectiveness of learning. This incompleteness has an impact on the low achievement of students' practical learning outcomes that have not reached the KKM. This study aims to describe the level of completeness of workshop facilities and the results of the disc brake system practice learning, and to determine whether there is a relationship between the two. The research approach uses a quantitative correlational method with a total sampling of 26 students. Data were obtained through observation, questionnaires, and documentation, then analyzed using descriptive statistics, normality tests, linearity tests, Pearson correlations, and coefficients of determination using SPSS 27. The results showed that the average completeness of workshop facilities was 42.27 and the results of practical learning were 42.81, both of which were in the good category. The Pearson correlation test produced a value of r = 0.960 with a significance of 0.000 (<0.05), indicating a very strong and significant relationship. The coefficient of determination (R² = 0.92) shows that 92% of the variation in practical learning outcomes is influenced by the completeness of workshop facilities. The more complete the workshop facilities, the higher the students' practical learning outcomes.

Rapid advances in Information Technology (IT) in the 21st century have significantly transformed educational practices, particularly in vocational high schools (SMK) specializing in Software Engineering (RPL). Traditional learning media, such as textbooks, static slides, and lectures, often fail to provide an interactive learning experience, limiting students’ engagement and reducing the effectiveness of knowledge retention. To overcome this challenge, this study focuses on developing and validating an interactive quiz application called VOCANETIC, designed as an innovative learning aid for SMK RPL students. The research uses a Research and Development (R&D) approach, emphasizing the stages of product development, expert validation, and feasibility assessment. VOCANETIC is developed using VB.NET in the Microsoft Visual Studio environment, and MySQL is employed for efficient data management and storage. The application features interactive quizzes, real-time feedback, and user-friendly interfaces to enhance student engagement and motivation. Based on evaluation and analysis by expert validators, VOCANETIC meets the “Proper” feasibility criteria, demonstrating usability, interactivity, and educational effectiveness. The results indicate that VOCANETIC is capable of supporting active learning, improving students’ understanding of software engineering concepts, and providing an effective platform for self-assessment and practice. In conclusion, the VOCANETIC application is proven to be a valid, reliable, and practical tool that can be implemented as an interactive learning medium in SMK RPL, offering an innovative approach to facilitate student learning and engagement in the digital era.                                                              

This research is motivated by challenges in biology learning, including the lack of digital literacy assessment in schools and the use of conventional learning methods that tend to make students bored. Impractical printed teaching materials cannot be integrated into the form of videos. This research aims to develop an e-module based on Science, Technology, Engineering, and Mathematics (STEM) to analyze the feasibility, practicality, and effectiveness of e-modules in improving students' understanding of concepts and digital literacy on human respiratory system materials. This research uses the Development Research (R&D) method with the ADDIE (Analysis, Design, Development, Implementation, Evaluation) development model.  The research was carried out at SMAN 1 Malinau. The implementation stage used a non-equivalent control group  design involving 34 students in the experimental class and the control class. Data collection instruments include validation sheets for media and material experts, practicality questionnaires for teachers and students, and pretest-posttest  tests to measure concept understanding and digital literacy.  Analysis of effectiveness data using N-gain test and hypothesis test (independent sample t-test for concept understanding and Mann-Whitney for digital literacy). The results of the research based on the validation of media experts and subject matter experts show that the STEM-based e-module is declared "Excellent". The e-module is also rated "Very Practical" by biology teachers and students. The e-module was shown to be effective in improving conceptual understanding, where the average N-gain value of the experimental class (58.63; Quite Effective category) was significantly higher (p=0.017) than that of the control class (40.62).

The Indonesian Naval Academy (AAL) is a military educational institution responsible for training young officers for the Indonesian Navy. The learning process at AAL is structured thru classroom theory, field practice, and direct simulations, including using the Engine Room Simulator (ERS) located under the Department of Engineering. The ERS is an important facility for supporting the technical mastery of ship engines for cadets. The operation of this simulator is highly dependent on the supply of electrical energy, with a total electrical power requirement of 14.36 kW. Thru this research, the author aims to design a hybrid solar energy system that can combine electrical energy from the PLN grid with alternative energy from sunlight. The potential for solar energy in the Indonesian Navy Academy area is very large. The average sunlight intensity in the Surabaya area over 1 year per hour is 208.33 W/m2. Therefore, in the design study, the author chose the Solar PV Lesso HPM series 550W solar panel, which has a power of 550W, an efficiency of 21.3%, and an area per panel of 2.583 m2. Based on the calculations obtained, the author used 126 solar panels with an area of 323.64 m2, as well as 24 Panasonic LC-P12100NA batteries with a voltage of 12 V and a capacity of 100 Ah, installed in 3 series and 8 parallel circuits.

The use of Search-Based Software Engineering (SBSE) for optimizing software architecture has evolved from fully automated to interactive approaches, integrating human expertise. However, current interactive tools face limitations: they typically support only single decision-makers, confine architects to passive roles, and induce significant cognitive fatigue from repetitive evaluations. These issues disconnect them from modern, team-based software development, where collaboration and consensus are crucial. To address these shortcomings, we propose "ArchEvolve," a novel framework designed to facilitate collaborative, multi-architect decision-making. ArchEvolve employs a cooperative coevolutionary model that concurrently evolves a population of candidate architectures and distinct populations representing each architect's unique preferences. This structure guides the search towards high-quality consensus solutions that accommodate diverse, often conflicting, stakeholder viewpoints. An integrated Artificial Neural Network (ANN) serves as a preference learning module, trained on explicit team feedback to act as a surrogate evaluator. This active learning cycle substantially reduces the number of required human interactions and alleviates user fatigue. Empirical evaluation on two industrial case studies (E-Commerce System and Healthcare Management System) compared ArchEvolve to a state-of-the-art interactive baseline. Results indicate that ArchEvolve achieves statistically significant improvements in both solution quality and consensus-building. The preference learning module demonstrated over 90% accuracy in predicting team ratings and reduced human evaluations by up to 46% without compromising final solution quality. ArchEvolve provides a practical, scalable framework supporting collaborative, consensus-driven architectural design, making interactive optimization a more viable and efficient tool for real-world software engineering teams by intelligently integrating cooperative coevolutionary search with a preference learning surrogate.

The learning model that can be used to make students active is the Problem-Based Learning (PBL) model. The Problem-Based Learning model is a problem-based learning, where learning is centered on students so that students are required to provide solutions to the problems given. This study aims to determine how much impact the application of the PBL model has in improving student learning outcomes on learning the Basics of Electronic Engineering at SMK Negeri 2 Dumai. The type of research used is quantitative research with a quasi-experimental method using a pretest-posttest control group design. The research subjects consisted of 64 students divided into two groups, namely class X TEI SMK Negeri 2 Dumai as an experimental group that applies the PBL model (36 students) and class X TEI SMK Negeri 3 Dumai as a control group that applies the conventional learning model (28 students). The research instrument was a multiple-choice objective test of 25 items whose validity and reliability had been tested with a Cronbach's Alpha value of 0.843. The data analysis technique used descriptive statistics and Independent Samples T-Test. The results showed a significant difference in learning outcomes between the experimental and control classes, with a t-test value of 4.168 and a significance level of 0.000 (p < 0.05). The experimental class experienced an average increase in learning outcomes of 15.55 points (from 64.78 to 80.33), while the control class only increased by 7.57 points (from 63.43 to 71.00), with a posttest average difference of 9.333 points. Furthermore, the PBL model was also effective in reducing the ability gap between students, as indicated by a decrease in the standard deviation from 9.457 to 8.021. It can be concluded that the implementation of the PBL learning model has a significant impact on improving student learning outcomes in the Fundamentals of Electronic Engineering course at SMK Negeri 2 Dumai.

The rapid growth of open-source software (OSS) in machine learning (ML) has intensified the need for reliable, automated methods to assess project quality, particularly as OSS increasingly underpins critical applications in science, industry, and public infrastructure. This study evaluates the effectiveness of a diverse set of machine learning and deep learning (ML/DL) algorithms for classifying GitHub OSS ML projects as engineered or non-engineered using a SMOTE-enhanced and explainable modeling pipeline. The dataset used in this research includes both numerical and categorical attributes representing documentation, testing, architecture, community engagement, popularity, and repository activity. After handling missing values, standardizing numerical features, encoding categorical variables, and addressing the inherent class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), seven different classifiers—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Logistic Regression (LR), Support Vector Machine (SVM), and a Deep Neural Network (DNN)—were trained and evaluated. Results show that LR (84%) and DNN (85%) outperform all other models, indicating that both linear and moderately deep non-linear architectures can effectively capture key quality indicators in OSS ML projects. Additional explainability analysis using SHAP reveals consistent feature importance across models, with documentation quality, unit testing practices, architectural clarity, and repository dynamics emerging as the strongest predictors. These findings demonstrate that automated, explainable ML/DL-based quality assessment is both feasible and effective, offering a practical pathway for improving OSS sustainability, guiding contributor decisions, and enhancing trust in ML-based systems that depend on open-source components.

This community service activity aimed to enhance English pronunciation skills and establish a consistent speaking habit among students of SMKN 3 Palangka Raya. Thirty-six students of class X TKJ (Computer and Network Engineering) participated in a one-month program using thematic sentence templates, repetitive pronunciation drills, and self-reflection through voice recording feedback. The implementation employed a participatory and descriptive-qualitative approach, involving active collaboration between facilitators, teachers, and students throughout all stages. Weekly themes included self-introduction, daily activities, hobbies, and opinions to create a contextual and meaningful learning situation relevant to students’ daily lives. The results showed noticeable improvement in pronunciation clarity, increased awareness of pronunciation errors, and higher confidence in speaking English during classroom activities. However, aspects such as fluency and vocabulary variation remained limited due to the short duration of the program. Overall, the pronunciation habituation strategy proved effective in fostering awareness, comfort, and sustainable speaking habits when applied consistently over a longer period.

The Teaching Campus Program provides students with direct experience in collaborating with teachers and principals to improve the quality of learning, particularly in literacy, numeracy, and technology adaptation. This study aims to evaluate the implementation of the Teaching Campus Program Batch 8 among students of the Faculty of Engineering, Padang State University (FT UNP). This study uses a program evaluation research type with a quantitative approach. The research population consisted of 35 students, all of whom were sampled using total sampling technique. The research variable was the evaluation of the implementation of the Campus Teaching Program based on the CIPP (Context, Input, Process, Product) evaluation model. Based on the results of the study, the evaluation of the implementation of the Campus Teaching Program among FT UNP students was overall “Fairly Good”. This provides learning experiences outside the campus and improves student skills. However, efforts to improve the quality of each evaluation dimension, especially in terms of student placement relevant to their study programs, clarity of guidelines, and preparatory curriculum, are necessary to achieve the “Very Good” category and maximize the program's contribution.

This community service program aims to enhance students’ understanding of narrative language features at SMKN 3 Palangka Raya through the implementation of the Detective Case-Solving Game model. The method applied was participatory learning, involving 36 eleventh-grade Computer and Network Engineering students in two sessions. The first meeting focused on introducing the game concept, including rules, character roles, and examples of narrative clues, while the second meeting emphasized the application of more complex case scenarios that required deeper analysis and storytelling skills. The results revealed a significant improvement in students’ ability to identify narrative structures, apply past tense, and use direct–indirect speech correctly. Moreover, students showed higher enthusiasm, actively engaged in group discussions, demonstrated teamwork, and were able to provide linguistic reasoning during presentations. In conclusion, this model effectively strengthens language skills while fostering 21st-century competencies, including critical thinking, communication, collaboration, and creativity, making the learning process more interactive, enjoyable, and meaningful for vocational students.

This study aims to determine the contribution of the variables of Self-Motivation, Learning Discipline, and Parental Support both partially and simultaneously to student learning achievement in the Automotive Engineering expertise program at SMK Negeri 2 Pengasih. This type of research is included in quantitative correlational research with multiple regression analysis techniques. The entire population was sampled with a total of 104 grade XII students. Data collection used a closed questionnaire and student final exam results documents. The research instrument that has passed the validity and reliability test, then continued with the classical assumption test to ensure that the regression model meets the eligibility requirements. The results showed that the three independent variables contributed significantly to student automotive learning achievement with a value of F = 26.718. Simultaneously with a significance value of 0.000 (p <0.001) and an Adjusted R Square value of 0.428 which means the model is able to explain 42.8% of the variation in learning achievement. Partially, the Self-Motivation variable significantly (p<0.05) provides the largest contribution to student learning achievement with a t-value of 2.120 followed by Learning Discipline with a t-value of 1.992 and Parental Support with a t-value of 2.021. The magnitude of the real contribution of each variable, namely: Self-Motivation of 12.15%, Learning Discipline of 10.49%, and Parental Support of 10.24%. These results emphasize the importance of strengthening students' intrinsic motivation, habituating learning discipline, and active parental involvement to improve learning outcomes in vocational education.

This research aims to enhance students' English technical competence by implementing Problem-Based Learning (PBL) for the English for Mechanical Engineering course at Politeknik Negeri Fakfak. However, the setting of this research is rooted in students' difficulties in understanding technical terms and applying English in engineering work environments. Implementing a participatory action research model with descriptive qualitative approaches, the research was conducted among 50 students of two classes. Data were collected through questionnaires, observations, and interviews. The findings showed that 88% of students perceived the learning material as highly relevant, 84% reported improved mastery over technical vocabulary, and 90% reported greater collaboration and problem-solving skills. Furthermore, the intervention fostered active learning behaviors, peer leadership, and a positive shift towards more favorable attitudes among students towards English as a professional tool. These findings confirm that PBL is a valuable approach to English language learning contextualization in the vocational education context, enhancing the development of both language and soft skills. The research proposes wider implementation of PBL across technical curricula to improve educational outcomes in correspondence with industry requirements.

The purpose of this study is to determine the effect of learning independence and logical intelligence on Physics learning achievement in 1st semester students of STMIK Pranata Indonesia Bekasi Odd Semester 2021/2022 Department of Informatics Engineering with survey research methods. Student research respondents totaled 45 people, selected based on purpose sampling techniques. Data collection is carried out by distributing questionnaires directly to the sample. Data analysis uses descriptive statistics such as finding the mean, median, mode, standard deviation, and inferential statistics to find coefficients. The method in this study is a survey. That there is a change in the dependent variable, which is 40.8% and the remaining 59.2% which is influenced by variables other than variables X1 and X2. Because the value of F = 14.457 is used to test the hypothesis in predicting the contribution of independent variables (X1 and X2) to the dependent variable (Y)

Indonesia, a country located in the Pacific Ring of Fire, has a high level of vulnerability to earthquakes. This condition demands an increase in the capacity of civil engineering human resources to design safe and disaster-resistant buildings. This study aims to evaluate the effectiveness of project-based learning training with a focus on the application of SNI 1726:2019, structural modeling using ETABS/RSA, lateral load calculations, and the design of simple earthquake-resistant houses. The research method involved final-year civil engineering students and alumni who participated in a series of trainings, with evaluations through pre-tests, post-tests, and design assessments. The results showed an increase in competency in all aspects, especially the understanding of the SNI 1726:2019 standard and lateral load calculations. In addition, participants were able to produce simple house designs that meet the basic criteria for earthquake resistance, material efficiency, and structural stability. These findings indicate that applied training can bridge the gap between academic theory and practical skills needed in the workplace. This research contributes to strengthening the capacity of civil engineering in supporting resilient infrastructure development and has important implications for the integration of similar training into higher education curricula and community service programs.

This research aims to identify and develop effective strategies to enhance language skills with self-confidence among students of Electrical Engineering Education at Universitas Negeri Medan. Language proficiency and self-confidence are crucial factors in supporting both academic and professional communication in the field of electrical engineering, especially when students are required to present ideas, convey research findings, and interact in the workplace. The research employed a qualitative method with direct observation, in-depth interviews, and literature study to obtain valid and comprehensive data. The findings indicate that the implementation of interactive learning methods, regular speaking practice, the utilization of technological media, and the reinforcement of a positive mindset significantly improve students’ language abilities while simultaneously fostering their confidence in communication. Furthermore, the presence of a supportive learning environment and the active role of lecturers as facilitators contribute to encouraging students to perform confidently in public. The implications of this study are expected to serve as a reference for educators and educational institutions in improving the quality of language learning, particularly in technical study programs. Thus, graduates are not only equipped with technical expertise but also possess strong communication competencies to face global challenges.

Background: Maritime engineering has traditionally relied on reactive and preventive maintenance strategies, often leading to operational inefficiencies, unplanned downtime, and excessive costs. With the rise of smart ship technologies, predictive maintenance (PdM) has emerged as a data-driven solution, leveraging sensor-based monitoring and real-time diagnostics to optimize ship maintenance. However, its integration into maritime education remains underexplored, particularly in training vessels used for vocational learning. Original Value: This research contributes new insights into the feasibility, effectiveness, and educational relevance of predictive maintenance in maritime vocational training. Unlike previous studies that focus on commercial ship applications, this study examines PdM within the context of training vessels at Poltekpel SULUT, bridging the gap between academic training and industry expectations. Objectives: The study seeks to answer: How does predictive maintenance improve the efficiency, cost-effectiveness, and reliability of naval auxiliary systems in training vessels? Methodology: A qualitative approach was employed, integrating sensor-based performance analysis, structured interviews, and questionnaire surveys involving cadets, instructors, and industry professionals. Data were analyzed through thematic categorization, cross-group comparisons, and narrative synthesis. Results: PdM demonstrated high effectiveness in reducing downtime (92/100), optimizing maintenance efficiency (91/100), and aligning with industry practices (89/100). However, challenges in sensor accuracy (85/100) and training integration were identified. Conclusions: The findings highlight the necessity of incorporating predictive maintenance into maritime training curricula to equip future engineers with the skills required for Industry 4.0 maintenance solutions, ensuring better operational efficiency and sustainability in the maritime sector.