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The implementation of industrial engineering technologies, such as automation, the Internet of Things (IoT), artificial intelligence (AI), and lean manufacturing, has significantly transformed human resource (HR) capabilities in the production sector, particularly in response to the Industry 4.0 paradigm. This study aims to examine the relatively low level of technology adoption in Indonesia, estimated at only 6–20% of manufacturing companies, and its impact on the development of HR competencies. The analysis focuses on changes in technical skill requirements, including digital literacy, data analytics, and technology-based decision-making, as well as the shift in job roles from manual tasks to more strategic functions. This research employs a qualitative descriptive approach grounded in sociotechnical systems theory and the strategic alignment model. The findings indicate that existing skill gaps can be addressed through continuous upskilling and reskilling programs, supported by strengthened triple helix collaboration among government, industry, and educational institutions. The implementation of these strategies has been shown to increase productivity by approximately 30–72% and enhance the competitiveness of the national production sector in the global industrial landscape.  

Optimizing human resource (HR) performance through a business process reengineering (BPR) approach is a crucial strategy in a competitive and dynamic industrial environment. This study qualitatively examines through a literature review how BPRs radically redesign business processes to improve productivity, quality, time efficiency, and reduce operational costs, with a focus on HR aspects such as recruitment, training, performance appraisal, and career development. The BPR theory by Michael Hammer and James Champy emphasizes the principles of results-based workflow reorganization, the integration of information technology such as ERP and AI, and the empowerment of HR to eliminate task redundancy. In the Indonesian industrial context, the integration of BPR with digital transformation supports Industry 4.0, where HR acts as a catalyst for innovation through adaptive skills and cross-functional collaboration. The study results show that BPR implementation strategies including as-is process analysis, to-be design, change management, and continuous evaluation increase employee motivation, retention, and sustainable competitive advantage. Case studies such as PT Telkom Indonesia and PT Cahaya Mega Group demonstrate efficiency increases of up to 100%. This approach not only streamlines operations but also builds an organization resilient to market and technological disruption.

Zinc (Zn) is one of the most common heavy metal contaminants found in industrial wastewater and solid residues such as slag, electroplating waste, and metal ash. At excessive concentrations, Zn can cause environmental disturbances, including toxicity to aquatic organisms, disruption of microbial activity, and groundwater contamination. Long-term exposure may also lead to bioaccumulation and potential health risks to humans. This article presents a comprehensive literature review that discusses the chemical properties of Zn, its environmental behavior, and the development of recent treatment technologies within the last five years. Several techniques, including adsorption using modified or composite materials, biosorption utilizing microalgae and agricultural biomass, as well as solidification–stabilization with amendment agents such as biochar or iron-sulfide compounds, are evaluated and compared. The literature indicates that no single treatment method is universally effective for all waste types; therefore, hybrid or integrated treatment systems are considered more efficient and sustainable. Based on the reviewed evidence, this study proposes an engineering concept that emphasizes environmental safety, cost-effectiveness, and industrial applicability.

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.

This study aims to develop interactive learning media for logic gate material in Industrial Electronics Engineering subjects at SMK Negeri 5 Batam by utilizing Adobe Flash CS6 integrated with CircuitVerse. This media is designed to provide a more interesting, interactive, and applicable learning experience, so it is expected to improve students' understanding of the concept of logic gates which is a basic material in the field of electronics. The study uses a Research and Development (R&D) approach with a 4D development model that includes the Define, Design, Develop, and Disseminate stages. Research data were collected through validation by material experts and media experts, as well as practicality tests involving students. Data collection instruments were in the form of validation sheets and student response questionnaires. The validation results showed that the developed learning media obtained a score of 88.20% from material experts and 93.88% from media experts, both of which are in the "very valid" category. The practicality test involving grade XI students obtained a score of 92.80%, indicating that the media is "very practical" to use in the learning process. This interactive media combines elements of text, images, animation, and digital circuit simulations using CircuitVerse, so students can learn logic gate concepts visually and directly practice them virtually. This integration not only makes it easier for students to understand the theory but also trains problem-solving and critical thinking skills. The findings of this study indicate that interactive learning media based on Adobe Flash CS6 and CircuitVerse has great potential to increase learning motivation, student engagement, and conceptual understanding of logic gate material. This media is also relevant to support the implementation of the Independent Curriculum, which emphasizes project-based learning, independent learning, and complete competency mastery.  

Reverse engineering is the process of reproducing or recreating an existing model—whether it is a component, subassembly, or complete product—without relying on the original design documentation or CAD data. One of the key techniques that supports this process is photogrammetry, a method that utilizes a series of photographic images to identify, capture, and reconstruct the geometry of physical objects in three dimensions. Photogrammetry offers advantages such as low cost, portability, and non-contact data acquisition, making it a valuable tool in various engineering and industrial applications. This study aims to analyze the influence of two important factors—namely, the number of photographs taken and the intensity of lighting—on the dimensional accuracy or deviation of 3D models produced through photogrammetry-based reverse engineering. A quantitative experimental approach was used, where both variables were systematically varied to evaluate their effect on model precision. Data analysis was conducted using a Two-Way ANOVA factorial test to determine the statistical significance of each factor and their interaction. The experimental results revealed that using 54 photographs yielded the lowest average dimensional deviation among all tested conditions, although the difference was not statistically significant. Similarly, variations in light intensity showed no statistically significant effect on accuracy, but visually, an illumination level of 200–250 lux tended to provide more consistent and stable model outputs. No significant interaction was found between the two variables, indicating that they operate independently in affecting model accuracy. Overall, the combination of 54 photographs and a light intensity of 250 lux was visually identified as the most effective setup. The findings of this research contribute to the optimization of photogrammetry workflows in reverse engineering, offering practical insights for producing high-quality CAD models for use in design, prototyping, and manufacturing processes.

Conflict management is an unavoidable challenge in complex and multicultural industrial environments, particularly when involving cross-functional teams with diverse backgrounds and expertise. This study aims to explore the sources of conflict, resolution strategies, and their impact on team performance in industrial organizations. Special emphasis is placed on the managerial competencies of a Project Manager in navigating the dynamics of a technical team. A reflective qualitative approach was employed, utilizing semi-structured interviews with an experienced Project Engineering Manager in the industrial sector. Thematic analysis was conducted to identify patterns of conflict, management strategies, and their influence on project outcomes. The findings indicate that the effectiveness of conflict management is largely determined by the leader’s ability to foster open communication and empathy within the team. A humanistic and constructive leadership style was found to minimize the negative impacts of conflict, while a confrontational approach tended to diminish motivation and performance. The practical implications of this study underscore the importance of enhancing leadership capacity and managerial communication to develop adaptive and collaborative project teams.

Waste management problems in Kudus City show high complexity, marked by an increase in the volume of daily waste generation that is not balanced by an adequate handling system. This study aims to optimize the waste management system in an integrated manner with an industrial engineering approach, especially through the application of the Lean Waste Management concept, redesigning facilities and logistics, and strengthening community participation based on Human-Centered Design (HCD). A qualitative method with a case study approach was used, with data triangulation techniques in the form of in-depth interviews, direct observation, and document analysis. The results of the study showed that 75% of households did not sort their waste, only 2 out of 6 TPSTs were active, and the TPA capacity had exceeded its limit since 2022. The efficiency of the transportation system is also low due to suboptimal routes and frequencies. This study offers solutions based on technology integration, facility redesign, logistics optimization, and community empowerment. With a systemic approach based on industrial engineering, an efficient and sustainable waste management model can be applied, not only in Kudus, but also in other areas with similar challenges.

This study aims to determine the contribution of industrial work practice (Prakerin) experience to the learning motivation of 12th-grade students in the Mechanical Engineering Department at SMKS Dhuafa Padang. A quantitative correlational research approach was employed, using a questionnaire distributed to 30 students who had participated in the Prakerin program. The results indicate a strong and significant relationship between Prakerin experience and learning motivation, with a Pearson correlation coefficient of 0.763 (Sig. < 0.001). Simple linear regression analysis shows that industrial practice experience contributes 58.3% to students' learning motivation. These findings suggest that relevant and meaningful implementation of Prakerin can encourage students to become more motivated in their academic activities at school.

This study aims to develop an interactive learning media based on android using a valid and practical smart app creator to improve students' understanding of the SPBI course in the Industrial Automation Engineering Department of SMK Negeri 1 Pariaman. This study uses the Research and Development (R&D) method with the ADDIE model as a guideline in the development process. The results of this study provide evidence that the interactive learning media created falls into the "Valid" category. This is proven based on validation carried out by material experts with a validity value of 87% (0.870) and validation carried out by media experts with a validity value of 92.11% (0.921). Furthermore, a Media Practicality Test was carried out by class XI students of SMK Negeri 1 Pariaman with a practicality value of 91.60% (0.961) falling into the Very Practical category.

This study aims to reveal how symbolic interaction between principals and teachers is interpreted as a social strategy in improving the employability of vocational high school students, especially in the Mechanical Engineering Expertise Competence. Using a qualitative case study approach, the research population was conducted at the Center of Excellence Vocational High Schools in Pasuruan Regency/City, while the sample was 4 vocational high schools that have mechanical engineering expertise competencies. Data collection techniques include in-depth interviews, participatory observation, and documentation studies. The results of the study indicate that the symbolic interaction that occurs is not only verbal communication, but also reflects shared values, expectations, and visions in preparing graduates who are adaptive to the world of work. The principal builds meaning through symbols of consistent leadership actions, such as involving teachers in strategic decision-making and instilling an industrial work culture. Teachers, on the other hand, respond to these symbols with more contextual and work-oriented learning practices. This interaction forms a productive school habitus that is the basis for improving student employability

The use of log wood as raw material for furniture and construction industry requires an optimal approach in the dimensions of management and production efficiency to increase economic value. This study aims to analyze the relationship between log wood dimensions (length, width, diameter) with the value of production results based on the percentage of efficiency (60%, 50%, 40%, and 30%). The data used are the results of observations of log wood dimensions from several types (unnamed and with codes: Piton, DL, UP, UD) along with the basic price value of the log. Calculations are carried out to determine the economic value of log wood based on the production efficiency scenario. The results of the study indicate that increasing production efficiency significantly affects the economic value obtained, with the UD log type providing the highest value at all levels of efficiency. This study provides recommendations for wood production optimization strategies based on comparisons of dimensions and efficiency as a basis for decision making in the wood resource-based industry.

This literature review discusses the application of Agile methodology in modern software development. Agile offers a flexible, iterative approach that contrasts with traditional waterfall models. The objective of this study is to provide insights into how Agile frameworks such as Scrum, Kanban, and Extreme Programming have influenced development speed, software quality, and team collaboration. Methodologically, the study synthesizes findings from recent academic and industrial literature from the last five years. The results show that Agile enhances adaptability and customer satisfaction, although challenges remain in large-scale implementations.  

This study uses a qualitative approach that describes the object studied through the process of exploring facts and data on objects in the field as they are, to achieve these objectives, data is needed that is excavated from an in-depth observation process with the principal, vice principal, head of the welding and metal fabrication engineering expertise program and teachers of productive subjects. Data collection techniques using in-depth interviews with, participant observation, and documentation. Data analysis techniques using data reduction, data presentation and drawing conclusions or verification. Checking the validity of the data using triangulation methods. The results of the study indicate that the suitability of students' technical and non-technical competencies with industry needs greatly determines the success rate of graduates in the workforce. Relevant competencies, such as mastery of modern welding technology and soft skills such as communication and teamwork, are key factors. In addition, it was found that aligning the curriculum with industry standards and the active involvement of industry partners through internship programs contributed significantly to improving the quality of graduates. This study recommends strengthening cooperation between vocational schools and the industrial world and developing training programs based on labor market needs.

This research aims to identify the nature of the cash flow statement, methods of preparing it and its indicators. Identify the nature of profitability and explain its indicators, shed light on the topic of predicting the financial distress of economic units, the causes of distress and ways to treat it, and use cash flow and profitability indicators to help predict the financial distress of Iraqi industrial companies listed on the Iraq Stock Exchange in the second and third years preceding the financial distress. The research community is represented by the industrial companies listed on the Iraq Stock Exchange, which number (21) companies until January 2023, while the study sample is the Iraqi Engineering Works Company in order to apply the current research in it. The research reached several conclusions, the most important of which was that the increase in cases of financial distress to which Iraqi industrial companies are exposed is due to the lack of instructions or directives specific to the industrial sector and the failure to use financial indicators through quantitative methods and methods to predict financial distress before it occurs, and to determine what the financial position will be in the future.

Internet Network is one of the fields in informatics and electronics engineering which is now growing rapidly due to the issue of the industrial revolution 4.0 which is increasingly closely related to Cloud computing technology and the Internet of Things. Without resources and knowledge about computer networks, the Internet of things and Cloud computing are quite impossible to design. Computer networks give birth to internet access which is very much needed by every agency and even the entire community in the world. Especially in educational institutions such as Madrasah Aliyah (MA) Raden Fatah, which is located in Kalasan, Yogyakarta when in the era of the Covid-19 pandemic, it faces the challenge of disruption from offline learning to online learning. To answer the demands of the times, MA Raden Fattah is very enthusiastic in developing its institution towards a quality smart school. The network infrastructure available at MA Raden Fattah has not been optimized, so through this service, network design and management are carried out so that the need for access points that help students and teachers can be met. This service has succeeded in increasing the number of access points, optimizing the management of internet network resources at MA Raden Fattah, and improving the quality of teaching and learning services at the institution

CV. Rasyid is a business unit specializing in the production of ecoprints. Currently, one of the most sought-after clothing colors by consumers is burnt coral red. However, CV. Rasyid has been unable to meet this demand due to challenges in the production process, specifically the deterioration of the color during the fixation stage. To address this issue, the community service team from the Industrial Engineering Study Program at Muhammadiyah University of Gombong (Unimugo) provided facilitation and support. The program aims to equip participants with the knowledge and skills needed to produce burnt coral red for ecoprints using secang dye. The activities include preparation, implementation, monitoring, and evaluation. The implementation process was conducted with guidance on color extraction, mordanting, design printing, and fixation. Monitoring results indicate that CV. Rasyid successfully carried out the burnt coral red ecoprint production process independently without significant issues. While the colors produced may vary slightly, this is not a concern, as such variations are a unique characteristic of ecoprint products.

Abstract. From the perspective of Micro, Small, and Medium Enterprises (MSMEs), fluctuations in raw material prices are highly concerning as they can significantly impact business stability. While MSMEs may tolerate price fluctuations to some extent, from an industrial engineering perspective, such a passive approach contradicts the principles of continuous improvement. This study seeks to predict the price of large red chili peppers using five regression models implemented through Orange Data Mining: Linear Regression, Support Vector Machine, Decision Tree, k-Nearest Neighbors (kNN), and Gradient Boosting. Due to the limited availability of daily data, particularly within a daily timeframe, the study utilized weekly data spanning three years. The results of the Test and Score evaluation shows Gradient Boosting as the best-performing model, achieving a Mean Absolute Percentage Error (MAPE) of 0.7%. However, the MAPE for predictions in January 2025 increased to 15.8%. This error is expected to decrease as more weekly data becomes available to mitigate the inaccuracies inherent in this model. Keywords: prediction, red chilli, regression, supervised learning , orange data mining. Abstrak. Dalam perspektif UMKM, fluktuasi harga bahan baku adalah suatu hal yang paling ditakuti karena berakibat pada ketahanan usaha yang menjadi tidak menentu. Pada suatu kondisi, fluktuasi harga dapat diterima para UMKM, namun dalam perspektif teknik industri, sikap UMKM tersebut tidak sesuai prinsip continuous improvement. Penelitian ini mencoba untuk memprediksi harga cabai merah besar dengan menggunakan 5 model regresi dibantu Orange Data Mining. Yaitu Linear Regression, Support Vector Machine, Tree, kNN, Gradient Boosting. Data yang diperlukan sebagian besar tidak tersedia, khususnya dalam kerangka waktu harian sehingga penelitian ini menggunakan data mingguan selama 3 tahun. Hasil Test and Score menunjukkan model Gradient Boost terpilih menjadi model terbaik dengan tingkat MAPE 0.7% namun MAPE pada tahap Prediction di bulan Januari 2025 menjadi 15.8%. Error tersebut akan berkurang ketika data mingguan sudah cukup banyak untuk menambal kesalahan yang dihasilkan model ini Kata kunci: prediksi, cabai merah, regression, supervised learning , orange data mining.