Dynamic Job Research Articles

Probing an LSTM-PPO-Based reinforcement learning algorithm to solve dynamic job shop scheduling problem

With the growth of personalized demand and the continuous improvement in social productivity, the large-scale and few-variety centralized production model is gradually transitioning towards a personalized model of small batches and multiple varieties, which makes the manufacturing process of the job shop increasingly complex. Furthermore, disruptive events such as machinery failures and rush orders in the job shop increase the uncertainty and variability of the production environment. Traditional scheduling methods are usually based on fixed rules and heuristic algorithms, which are difficult to adapt to constantly changing production environments and demands. This may lead to inaccurate scheduling decisions and hinder the optimal allocation of job shop resources. To solve the dynamic job shop scheduling problem (JSP) more effectively, this paper proposes a Reinforcement Learning (RL) optimization algorithm integrating long short-term memory (LSTM) neural network and proximal policy optimization (PPO). It can dynamically adjust scheduling strategies according to the changing production environment, achieving comprehensive status awareness of the job shop environment to make optimal scheduling decisions. First, a state-aware network framework based on LSTM-PPO is proposed to achieve real-time perception of job shop state changes. Then, the state and action space of the job shop are described within the context of the state-aware network framework. Finally, an experimental environment is established to verify the algorithm’s effectiveness. Training the LSTM-PPO algorithm makes it feasible to achieve better performance than other scheduling methods. By comparing the initial planning time with the actual completion time of the rescheduling decision under different dynamic disturbances, the efficiency of the proposed algorithm is verified for the dynamic JSP

Writing Effective Cover Letters: Do Undergraduates Have the Written Communication Skills to Achieve the Spirit of SDG4 Quality Education?

Introduction: This study investigates the impact of English language instructors’ intervention on the cover letter writing process for job applications among specific ESL learners enrolled in a workplace communication course at a public university in Malaysia. This study adopts an action research technique, incorporating content analysis conducted before and after the intervention. The goal of this action research is to identify inaccuracies or issues in the format and content of cover letters written by the selected learners and implement instructor intervention to resolve the identified problems or errors. Generally, the results of this study revealed that the effectiveness of the learners’ cover letters improved after receiving extra input and feedback from their language instructors. A lack of learner-readiness and job knowledge resulted in some learners experiencing difficulty in writing coherent cover letters. More importantly, the learners indicated that their writing experiences raised greater awareness of the many aspects of cover letters and enhanced their confidence in writing cover letters. Overall, this study contributes the initiative to enhance learners' understanding of the ever-changing employment market and develop not only their ability to write cover letters but also their self-confidence and proficiency in the English language. Most significantly, the written communication skills will provide them with lifelong learning opportunities that are taught in higher education as stipulated in the fourth goal (SDG4) of the United Nations’ Sustainable Development Goals, which emphasizes quality education. Objective: The objective of this study is to assess the learners' proficiency in writing cover letters by identifying their strengths and weaknesses. The aim is to evaluate the efficacy of the writing methods employed in cover letters and determine their appropriateness. Theoretical Framework: Kolb’s experiential learning theory (ELT) serves as the primary conceptual framework to define the sequence of activities in the research procedure. David Kolb's (1984) cycle of experiential learning stages perceives learning as a dynamic interaction between learners and their environment (Kolb, 2014). The Experiential Learning Theory (ELT) underscores a cyclical learning process that includes experiencing, reflecting, thinking, and experimenting (Kolb & Kolb, 2005). Method: This study adopts the action research methodology and employed a six-phase method, which comprises the first phase, the diagnostic test, the second phase, the evaluation of cover letters, the third phase, the pre-writing strategies, the fourth phase, the writing phase, the fifth phase, the instructor feedback phase, and the sixth phase is the re-writing phase. Results and Discussion: The results obtained revealed the necessity for graduating learners to possess enhanced skills and knowledge prior to entering the challenging work market. There is currently a strong demand for graduates to develop more modern and thorough tactics to successfully secure employment. The results of this study confirm the need for innovative methods in language acquisition, specifically grammar mastery. Research Implications: Learners need a helping hand with their language and should be guided closely beginning with the rudiments of writing and other integrated language skills that would help bolster their confidence in writing cover letters and improve their communication skills. They need to learn to be independent and proactive in the job-search process. As instructors empower learners with lifelong skills of writing cover letters, the learners themselves should constantly keep abreast of the changes in the dynamic job market. Originality/Value: This study helps learners comprehend the shifting job market, prepare cover letters, and improve their English and self-confidence. Most importantly, it will offer lifelong learning, as stated in the UN's fourth Sustainable Development Goal (SDG4).

Benefits and Objectives of ECVET Implementation in Bulgaria: an Empirical Study

The comprehensive analysis presented in this article examines the potential positive effects of complete ECVET implementation in Bulgaria, aligning with the European guidelines established by CEDEFOP. By focusing on a diverse range of topics, the report offers valuable insights for policymakers and practitioners seeking to enhance the quality and relevance of VET in Bulgaria. Despite the potential benefits of ECVET, its implementation in Bulgarian vocational education has not yet been realized and lacks a comprehensive credit system for credit exchange within both secondary and higher vocational education. Consequently, the development of an effective ECVET implementation system, including a robust assessment framework, remains an ongoing challenge. ECVET is poised to support learners in Bulgaria across several key dimensions. Its ability to foster adaptability, practical skill acquisition, and alternative learning strategies equip learners with the competencies necessary to thrive in today’s dynamic job market. Moreover, ECVET promotes the development of organizational skills, facilitates informed career choices, and accelerates professional implementation. The successful implementation of ECVET necessitates a concerted effort from various stakeholders, including VET institutions, employers, and learners. By embracing ECVET, Bulgaria can significantly improve the quality and relevance of its VET systems, better prepare learners for the workforce, and stimulate economic growth. Ultimately, ECVET contributes to learners’ personal satisfaction, self-esteem, and shorter paths to the labor market. These benefits underscore the transformative potential of ECVET in enhancing the educational experience and career prospects of learners in Bulgaria.

Real-time scheduling for two-stage assembly flowshop with dynamic job arrivals by deep reinforcement learning

Assembly flowshops differ from flowshops because their jobs have several components that are manufactured in the processing stage and then joined together in the assembly stage. Real-time scheduling of two-stage assembly flowshops is of great significance for many discrete manufacturing companies in customized order fulfilment. Therefore, we study two-stage assembly flowshop scheduling problem with the objective of total tardiness minimization considering dynamic job arrivals using deep reinforcement learning (DRL). First, an architecture of DRL-based real-time scheduling is proposed. Second, we establish a DRL-based real-time scheduling model by designing proper state space, action space and reward function. A scheduling agent is constructed and trained through the proximal policy optimization (PPO) using interactive simulation data. The experimental results show that the trained scheduling agent can learn to choose the appropriate dispatching rules efficiently so as to make a real-time scheduling quickly and effectively. The PPO algorithm outperforms six dispatching rules, genetic algorithm and two DRL algorithms (i.e., DDQN and DDPG) in terms of solution quality and computing speed. The proposed approach is highly applicable to many practical scheduling situations that requires real-time and quick decisions.

Dynamic job allocation method of multiple agricultural machinery cooperation based on improved ant colony algorithm

Contemporary large-scale and systematic agricultural operations demand the collaborative efforts of multiple agricultural machines with distinct functionalities. However, the failure of a single agricultural machine during collaborative operations jeopardizes the entire undertaking. To address this challenge, this paper proposes a multi-machine collaborative dynamic job allocation method based on the improved ant colony algorithm. Initially, the improved ant colony algorithm is employed to determine the optimal solution for harvester scheduling. This solution is then fed into the data conversion algorithm to acquire the necessary unloading point information for transport vehicle scheduling. Subsequently, the improved ant colony algorithm is once again utilized to optimize the transport vehicle scheduling. In cases of agricultural machinery failure or changes in the operating environment, two distinct methods are employed based on the situation. The first involves double-layer rescheduling of both harvester and transport vehicles, while the second employs single-layer rescheduling exclusively for the transport vehicles, yielding the respective rescheduling results. The outcomes demonstrate that the proposed solution method effectively identifies the current optimal scheduling plan for both the harvester and transport vehicle in the event of malfunctions. Moreover, under the premise that the unproductive waiting time of the harvester is reduced to zero, and the number of transport vehicles is minimized, it achieves the minimization of operating time cost and transportation cost. This method exhibits significant potential for seamless integration into the practical application of unmanned farms, providing a foundation for addressing scheduling and management challenges in multi-agricultural machinery collaborative operations within complex farmland operating environments.

A clustering-aided multi-agent deep reinforcement learning for multi-objective parallel batch processing machines scheduling in semiconductor manufacturing

Batch processing machines are often the bottleneck in semiconductor manufacturing and their scheduling plays a key role in production management. Pioneer researches on multi-objective batch machines scheduling mainly focus on evolutionary algorithms, failing to meet the online scheduling demand. To deal with the challenges confronted by incompatible job families, dynamic job arrivals, capacitated machines and multiple objectives, we propose a clustering-aided multi-agent deep reinforcement learning approach (CA-MADRL) for the scheduling problem. Specifically, to achieve diverse nondominated solutions, an offline multi-objective scheduling algorithm named Multi-Subpopulation fast elitist Non-Dominated Sorting Genetic Algorithm (MS-NSGA-II) is firstly developed to obtain the Pareto Fronts, and a clustering algorithm based on cosine distance is employed to analyze the distribution of Pareto frontier solution, which would be used to guide reward functions design in multi-agent deep reinforcement learning. To realize multi-objective optimization, several reinforcement learning base models are trained for different optimization directions, each of which composed of batch forming agent and batch scheduling agent. To alleviate time complexity of model training, a parameter sharing strategy is introduced between different reinforcement learning base model. By validating the proposed approach with 16 instances designed based on actual production data from a semiconductor manufacturing company, it has been demonstrated that the approach not only meets the high-frequency scheduling requirements of manufacturing systems for parallel batch processing machines but also effectively reduces the total job tardiness and machine energy consumption.

Integrating Entrepreneurship into Education: Cultivating Skills for Innovation, Leadership, and Future Workforce Success

Background and Aims: Integrating entrepreneurship into education is critical for equipping students with essential skills like innovation and adaptability, which are required for success in a competitive job market. This approach not only enhances individual career opportunities but also promotes a more dynamic and resilient society capable of meeting today's challenges. This paper will investigate the integration of entrepreneurship into education. Methodology: This paper investigates existing literature and case studies to assess the effectiveness of various educational approaches in developing entrepreneurial skills. It summarizes findings and makes evidence-based recommendations to improve entrepreneurship education. Results: The finding found that Integrating entrepreneurship into education provides students with critical skills for navigating today's dynamic job market while also encouraging a mindset of resilience and continuous learning. As educational institutions evolve to include entrepreneurship in their curricula, students will be better prepared for both entrepreneurial and traditional careers. This integration not only improves individual prospects but also helps to create a more innovative and adaptable society, which drives economic growth and addresses global challenges. Conclusion: Integrating entrepreneurship into education gives students valuable skills for a rapidly changing job market while also encouraging resilience and lifelong learning. This approach not only improves career readiness but also contributes to the development of a more innovative and adaptable society capable of driving economic growth and addressing global challenges.

Skill Education under NEP 2020: A Pathway to Enhanced Employability and Economic Growth

The National Education Policy (NEP) 2020 marks a pivotal shift in the Indian education system by integrating skill education to boost student employability and foster economic growth. This study evaluates the impact of NEP 2020's skill education strategies on job readiness and their broader economic implications. Through policy document analysis, educational frameworks, and empirical data, this research reveals how NEP 2020 aligns educational outcomes with industry requirements. Findings suggest that the policy’s emphasis on vocational training, experiential learning, and competency-based education effectively enhances graduate employability. Additionally, NEP 2020’s focus on lifelong learning and adaptability prepares students for a dynamic job market, contributing to economic development. This paper underscores NEP 2020's role in reshaping India's educational landscape and cultivating a workforce ready for the 21st-century economy. Keywords: NEP 2020, Skill Education, Employability Skills, Hands-on Practice, Transformation, NCERT, NCVT

Developing commercial awareness and digital video interview skills in second-year life sciences students

Employers are increasingly using video interviews in the application process, highlighting the need for graduates to adapt to digital technologies. To ensure graduates are adequately prepared, a Year 2 skills module was redeveloped, replacing face-to-face interviews with asynchronous video interviews using ShortlistMe. Aligning the assessment to a careers event allowed students to engage with employers and develop commercial awareness. Out of 430 students, 395 completed the video interview, and 329 connected with employers during the careers event (a 63% increase compared to the previous year). A survey explored student perceptions of the video interview (n= 39). Student confidence in completing a video interview increased by 44.6%, and in demonstrating commercial awareness by 30%. This model effectively connected employers with the curriculum at scale, fostering digital fluency, commercial awareness, and enhancing employability skills. This adaptable approach could benefit any discipline, equipping students for success in a dynamic job market. This article was published open access under a CC BY-NC-ND licence: https://creativecommons.org/licenses/by-nc-nd/4.0/ .

Employability Skills Requirements in the Professional Services Sector: An In-depth Analysis of Graduates

In today’s competitive job market, the gap between academic preparation and industry expectations has become increasingly evident, highlighting the urgency of identifying the key employability skills required by graduates. This study evaluates the essential employability skills of graduates in the professional services sector. Using a descriptive research design, a questionnaire was administered to 282 respondents in the Philippines. The sample size was determined using Slovin's formula and a stratified random sampling technique. Data analysis involved frequency percentage distribution, weighted mean, and analysis of variance (ANOVA). Findings reveal a significant proportion of respondents as team leaders, holding bachelor’s degrees, and working in professional services firms with more than 151 employees. Effective communication skills emerged as the foremost requirement, complemented by leadership skills that foster enthusiasm and commitment within organizations. Notably, disparities in employability skills were found when respondents were categorized based on organizations’ sizes. Concurrently, respondents reported challenges in articulating well-defined career objectives and demonstrating adaptability. These findings underscore the imperative for higher institutions to emphasize curricula that enhance graduates’ employability skills, particularly in communication, leadership, critical thinking, and problem-solving. Universities should tailor their academic programs to strengthen these crucial skills, benefiting both graduates and the professional services industry. Furthermore, continuous professional development and industry-academic partnerships are recommended to keep curricula aligned with evolving industry needs, ensuring that graduates are well-equipped for the dynamic job market.

A self-learning particle swarm optimization for bi-level assembly scheduling of material-sensitive orders

In modern assembly production, maximizing fulfillment becomes challenging amid resource scarcity and process unpredictability. This study addresses such issues with an effective approach to solving the integrated problem. This confluence presents a scenario where resource allocation mirrors a multidimensional knapsack and job sequencing corresponds to distributed assembly permutation flowshop scheduling, factoring in sequence-dependent setup times. Employing a mixed-integer mathematical model, we capture the configurations and objectives of the integrated problem. A special dual-level cooperative self-learning particle swarm optimization (SLPSO) metaheuristic is developed for improved hierarchical decision-making. Adaptive learning is harnessed to guide dynamic job selection under uncertainty, aiming for the highest total value. To minimize tardiness, a neighborhood search operator customizes sequences for each shop, considering changing setup times between jobs. Benchmark tests have been performed, showing that SLPSO surpasses established genetic algorithms and swarm techniques by 8%–15%. When implemented in an operational traditional pharmaceutical manufacturer, significant improvements were observed. The integrated structure harmonizes planning and scheduling stages, enhancing the algorithm via bidirectional feedback between decisions at various levels under uncertainty, thus providing guidance for real-world production.

Efficiency and Cost-Benefit Analysis of the Manual and Intelligent Venous Blood Sampling in Outpatient Area of Hospital.

To evaluate the efficiency and cost-benefit of the manual and intelligent venous blood sampling in outpatient area. The hospital selected had two branches with the outpatient area in Branch A using manual venous blood sampling as the control group and the outpatient area in Branch B using intelligent venous blood sampling as the experimental group. Analyze the differences between the two groups in operation time, cost-benefit, and service efficiency through on-site investigation and project cost methods. Compared with manual venous blood sampling, intelligent venous blood sampling project is more optimized with shorter operation time and higher nursing service efficiency. But both groups have negative returns. The intelligent venous blood sampling process is more optimized, combined with dynamic job implementation, resulting in lower labor costs and higher job service efficiency compared to manual venous blood sampling.

Design of an Iterative Method for MapReduce Scheduling Using Deep Reinforcement Learning and Anomaly Detection

Due to high complexities within distributed computing environments, there's a critical need for advanced scheduling frameworks that are capable of optimizing MapReduce systems. Current approaches have static policies that limit their capability to adapt to changing system dynamics and workload variations for different cloud scenarios. To overcome these issues, this study introduces a robust MapReduce framework empowered by intelligent scheduling algorithms, tailored to enhance system efficiency and resilience levels. The framework introduces three novel scheduling models: Deep Reinforcement Learning for Dynamic Job Scheduling (DRDJS), Anomaly Detection-driven Adaptive Scheduling (ADAS), and Cluster-based Job Categorization and Scheduling (CJCS). DRDJS utilizes deep reinforcement learning to dynamically generate optimal scheduling policies based on multiple metrics that include historical job data, system metrics, and workload characteristics. This adaptive approach leads to significant reductions in job completion times, outperforming static scheduling methods by up to 30%. Next, ADAS leverages anomaly detection to prioritize critical tasks and efficiently allocate resources in response to anomalies, resulting in a notable improve scheduling speed by up to 40%. Finally, CJCS employs clustering algorithms to categorize jobs based on their resource requirements and execution profiles, enabling more accurate resource allocation and reducing average job completion times by up to 25%. By integrating these methods into a unified scheduling framework, the proposed solution addresses the variability in job characteristics and system performance, thereby enhancing the overall throughput and stability of MapReduce operations.

Dynamic Jobs of Ladies in the Development of Consumable and Restorative Mushrooms of Focal India as a Pay Age Movement

The world's population is growing, leading to increased concerns about hunger, malnutrition, and declining nutritional value in commonly consumed foods. As a result, there is a growing need for alternative sources of protein, particularly in developing countries where the production of pulses has not kept pace with population growth. Edible mushrooms, recommended by the FAO for their nutritional value, are gaining attention as a viable solution. Mushrooms are rich in proteins, carbohydrates, vitamins, fibers, and essential salts, making them an ideal supplement to cereal-based diets. However, fresh mushrooms have a short shelf life, making their commercialization challenging. To address this issue, various preservation methods, such as canning and drying, along with value addition techniques, are employed. Additionally, surplus mushrooms can be processed into novel value-added products to reduce losses and enhance income for growers. Mushrooms are not only valued for their nutritional content but also for their medicinal and therapeutic properties. They contain bioactive compounds such as polysaccharides, dietary fibers, triterpenoids, and vitamins, which contribute to their antioxidant, antitumor, and antimicrobial properties. Moreover, mushroom cultivation has the potential to alleviate food insecurity and create economic opportunities, particularly in developing countries like Ethiopia. This study evaluates the suitability of waste paper supplemented with cornstalk and wheat bran as substrates for oyster mushroom cultivation in Axum, Ethiopia. The economic importance of mushrooms lies in their rich nutrient profile, including high levels of vitamins, minerals, and protein, making them beneficial for human health. Oyster mushrooms, in particular, are known for their ability to grow in a wide range of temperatures and utilize various organic substrates. Overall, mushrooms offer a cost-effective means of supplementing nutrition and promoting health, making them an important food source with significant economic potential.

Challenges in Islamic Education Curriculum Development: A Comparative Study of Indonesia, Pakistan, and India

This study identified the challenges in Islamic education curriculum development in Indonesia, Pakistan, and India. By using a paradigmatic interpretive approach, data were obtained from interviews with 15 international students enrolled for the Islamic studies doctoral program in Malaysia; these students were from Indonesia, Pakistan, and India. These students each have more than five years of experience teaching Islamic education subjects in their respective countries. The study found that, in Indonesia, the main challenges in Islamic education curriculum development are integrating the national curriculum with local and global diversity and increasing the accessibility of education for people at all levels of society. The gap between Islamic education patterns in madrasas and public schools should also be addressed. In Pakistan, the main challenge was the accessibility of education, especially in rural areas, and overcoming limited infrastructure and facilities that are the result of the government’s low education budget. Complex challenges, such as gender inequality, ethnic discrimination, and political conflict also hamper curriculum development efforts. In India, challenges center on the conflict between Islam and Hinduism; furthermore, disputes over history and religious identity complicate the integration of Islamic values into the broad curriculum. Another significant barrier is shortcomings in the technological expertise of teachers. While the challenges in the different countries differ, they share similarities in terms of the complexity of crafting a curriculum that is relevant to technological developments, and the needs of a dynamic job market. This study can contribute to policy-making, so that these three countries can work together to improve and enhance the quality of Islamic education.

Task Scheduling with Dynamic Load Balancing for Cloud Computing

Using powerful cryptographic techniques and complex machine learning algorithms, this study explores an enhanced way for dynamic job scheduling, data recovery, and workflow management in cloud computing. To improve data recovery procedures and optimize job scheduling, we investigate the combination of the Random Forest, Q-learning from artificial neural networks (ANNs), and the J48 decision tree algorithms. To ensure data redundancy and integrity, we also use the Kochi matrix for effective data chunk generation and backup. The Advanced Encryption Standard (AES) is used to protect data. Our all-encompassing approach is to increase the effectiveness of resource allocation, hasten data recovery, and offer strong security in cloud environments. The efficiency of the suggested methodologies in improving overall system performance, security, and reliability is highlighted by the experimental findings, which show notable advancements over conventional approaches. Key Words: Load Balancing, Machine Learning, ANN, Random Forest, AES, Q-learning

Dynamic Landscape of Higher Education, The Intersection of Student Skills and Industry Demand

In today's dynamic job market, higher education students are confronted with the challenge of sifting through numerous career options that align with their skills, interests, and educational background. To tackle this hurdle, "Dynamic Landscape Of Higher Education, The Intersection Of Students Skills And Industry Demand" introduces a personalized job recommendation system tailored explicitly for higher education students. This system utilizes advanced recommendation algorithms and data analysis techniques to provide students with targeted job suggestions based on their academic qualifications, skills, interests, and career preferences. With a user-friendly interface, students can effortlessly input their information and receive tailored recommendations that evolve over time through continuous learning from user feedback. By offering data-driven insights and facilitating informed career decisions, this system empowers students to explore diverse career pathways and seamlessly transition from education to employment. The user interface of this system is designed to be intuitive and user-friendly, enabling students to easily input their information and preferences to receive tailored job recommendations. The recommendation system is adaptive, continuously learning from user feedback and interaction, ensuring that the recommendations provided evolve over time to better meet the changing needs and preferences of students. Recognizing the widening gap between industry requirements and graduating students' skills, this project proposes a comprehensive job recommendation system to bridge this divide and enhance placement opportunities. Leveraging advanced algorithms and machine learning techniques, the system evaluates students' skill sets and matches them with relevant job roles, facilitating a smooth transition from academia to the workforce. This multifaceted approach includes data collection, algorithm development, user interface design, and integration with existing campus placement systems. Collaborative efforts with academic institutions and industry partners aim to provide personalized job recommendations tailored to individual students' abilities and aspirations, with a focus on fairness and equity. Key components of the project include the design and implementation of an intuitive user interface, data-driven skill assessment methodologies, and continuous refinement of the recommendation algorithm based on user feedback and industry insights. Ethical considerations regarding data privacy and security are carefully addressed, reflecting a commitment to responsible use of student data. Ultimately, the success of the job recommendation system is measured by its ability to match students with suitable employment opportunities and its broader impact on career readiness and long-term professional success. By empowering students with the tools and resources needed to navigate the complexities of the modern job market, this project aims to catalyze positive change and foster a more inclusive and equitable future for all stakeholders involved.

CareerBoost: Revolutionizing the Job Search with Resume Enhancement and Tailored Recommendations

The Resume Enhancer and Job Recommendation System is designed to meet the unique challenges faced by job seekers in today's dynamic job market. Leveraging cutting-edge natural language processing (NLP) techniques, the present system provides a tailored solution to streamline the job search process. The present Resume Enhancer component utilizes advanced NLP algorithms to analyse resumes and job descriptions, generating comprehensive eligibility scores and targeted skill recommendations. This ensures that candidates' resumes are optimized to effectively showcase their qualifications and expertise to potential employers. The present Job Recommendation feature delivers personalized job listings tailored to each user's selected roles or career aspirations. The authors implemented machine learning algorithms such as the Random Forest Classifier and K-means Clustering, the system matches candidate preferences and qualifications with relevant job opportunities, increasing the likelihood of finding the perfect fit. Overall, the Resume Enhancer and Job Recommendation System serves as a valuable tool for job seekers, empowering them to navigate the complexities of the modern job market with confidence. With its user-centric approach and advanced technology, the present system enhances employability and facilitates career growth for individuals at every stage of their professional journey.

Strategi Manajemen Keuangan Untuk Pelajar di Era Gen Z Bagi Siswa Smk Islam Darurrohman

Generation Z refers to the demographic group consisting of individuals born between the mid-1990s to the mid-2010s. This generation is also known as "Gen Z" or "iGen," and they are a group that is growing rapidly in an era of technology and information that continues to develop rapidly. As the first generation to grow up with the internet, social media and mobile technology, Generation Z is considered the most digitally connected group. They are growing up in an era where access to information is easier than ever. Generation Z is also often considered an intelligent, adaptive and collaborative group. They have become a significant marketing target due to their outsized influence on consumer trends and preferences. In addition, Generation Z is faced with various challenges, including preparing themselves for a dynamic job market, facing social and mental pressures, and being responsible for the impact of technology on daily life. Darurrohman Islamic Vocational School is an educational institution that has long been committed to providing high quality education with an Islamic approach to its students. With a strong vision and mission, this school not only focuses on academic aspects, but also pays special attention to the development of students' character and practical skills. The number of participants who took part in this seminar was 69 people. One of the aspects that is really paid attention to at Darurrohman Islamic Vocational School is awareness of financial management in the Gen Z era which is carried out for Darurrohman Islamic Vocational School students as a collaboration between lecturers and students of STIE Ekadharma Indonesia.

The Influence of Leadership Spirit and Creativity on Technopreneur-Based Entrepreneurial Interest in Vocational Education

The lack of interest in technopreneur-based entrepreneurship raises concerns about stu-dents' readiness to face the demands of an ever-changing and dynamic job market. There-fore, this research aims to understand the factors that influence interest in technopreneur-based entrepreneurship, especially students in Vocational Education related to leadership and creativity. The research method used was a survey followed by data collection through questionnaires distributed to Vocational Education students, followed by multiple regression analysis using the T test and F test to test the three proposed hypotheses. The research results show that the spirit of leadership and creativity significantly influences the entrepreneurial interest of technopreneur-based Vocational Education students. These two variables together explain most of the variation in entrepreneurial interest, with 91.4% of the variation explained by the combination of the two factors. These findings highlight the importance of developing leadership qualities and stimulating student creativity to foster interest and success in technopreneur-based entrepreneurship.