Most research states that implementing environmental, social, and governance (ESG) has positive impacts. However, fewer studies have discussed ESG implementation in higher education. This study aimed to develop instruments to assess the ESG atmosphere in higher education institutions. A modified Delphi approach was employed. Experts were invited from a private higher education institution in Indonesia. A deductive study, discussion, and two stages of getting consensus from panelists were conducted. The instrument was distinguished into four types for four groups of higher education stakeholders: Students, Staff, Faculty Members, and Community Members. The I-CVIs ranged from 0.80–1.00, while the minimum values of S-CVI/Ave and S-CVI/UA were 0.98 and 0.91, respectively, meaning the content validity was excellent. The final version instrument has been tested and declared valid, reliable, and ready to be used for empirical research for universities to assess their contribution to the Sustainability Development Goals (SDGs). There are also opportunities to conduct further research on the existence of recursive and non-recursive models between factors.

Sustainable consumption has become a global priority, yet the factors that encourage people to adopt environmentally friendly purchasing behavior differ across cultures and technologies. This study explores how environmental knowledge, environmental attitude, and the perception of AI-driven personalization influence green purchasing intention and actual purchasing behavior among young consumers in Java, Indonesia. A survey of 517 university students was conducted, and the relationships among these factors were analyzed using structural equation modeling. The findings reveal that environmental knowledge strongly shapes environmental attitudes, which in turn enhance the intention and behavior to purchase green products. Perception of AI-driven personalization also strengthens green purchasing intention, although its direct effect on behavior is limited. These results suggest that digital platforms and marketers can promote sustainable consumption by combining environmental education with transparent and value-based AI personalization. The study contributes to understanding how psychological readiness and technological engagement together encourage greener consumption among youth in emerging economies.

The demand for integrated circuit (IC) chips has risen markedly across various industries in conjunction with advancements in global technology. Prior to packaging, IC elements undergo several processes, including wafer dicing, wire bonding, and encapsulation. Scanning Acoustic Tomography (SAT) effectively analyzes the internal structures of integrated circuit products, thereby preventing the supply of defective components, including chip fractures, delamination, voids, and adhesion issues. The study aims to reduce operator eye strain, enhance productivity, and minimize employee turnover rates by proposing the use of convolutional neural networks (CNN) to develop a predictive model for automating defect detection in integrated circuit (IC) products within SAT images, replacing traditional visual inspections. To enhance the accuracy of the CNN model, we implement the flood-fill algorithm as the primary augmentation strategy and create an image augmentation model in Python. This method produces a training set that accurately reflects the true characteristics of defects, thereby mitigating the problem of limited defect data and ensuring the model is trained on reliable information. The incorporation of various rates and scaling factors into SAT defect images, along with the manipulation of the original defect, contributes to the development of a robust dataset suitable for real-world testing. The CNN model is trained using various batch sizes, resulting in customized training datasets and predictive models to improve accuracy. Key findings indicate that employing 40× augmentation alongside a batch size of 32 enhances the model’s performance, yielding a missed detection rate below 0.4% and a false alarm rate of 0.1%. This model offers an improved solution to the issue of manual inspections on assemblies, thereby alleviating operator stress and establishing a robust framework for automated integrated circuit quality management.

Optimization is the search for the optimal value of a problem with the help of one or more objective functions. Optimization problems are often found in everyday life and become important in the operation of a production system. The simplest optimization problem in the industrial world is solved using linear programming. Linear programming equations are made to solve optimization problems in the industrial world. In optimizing a problem, computational tools are needed to produce the optimal solution. Many computational tools are used, one of which is Excel Solver. Excel Solver is operated because many Microsoft Office users have this computing tool. Where everyone commonly uses Excel. The world of industrial Engineering needs someone who can solve optimization problems, especially since the current industrial era is getting more advanced. More skills are necessary for dealing with the Industrial Revolution 4.0 and Society 5.0, and it becomes a mandatory ability for an industrial engineer to solve this problem. Therefore, this community service activity is proposed with the topic: "Technique for Calling Millennials (TMKM) for Optimization Modules using Linear Programming with Excel Solver" to introduce optimization in the industrial world using existing optimization techniques and simple tools needed as applications in industrial engineering science. This community service activity was carried out in collaboration with Citra Blessing Tangerang High School. This activity is given to all students and teachers at SMA Citra Blessing Tangerang. The target audience for this activity is 50 participants. It is hoped that this activity can provide knowledge in the field of optimization and also provide an overview of industrial developments in the Industrial Revolution 4.0 era and in Society 5.0. Besides that, it also opens students' insights about the field of Industrial Engineering, especially opening students' insights about career prospects in the Industrial Engineering field.