With the rapid growth of the fashion industry, textile waste and its environmental impact have significantly increased. In response, the culture of thrifting has emerged as a sustainable consumption alternative. However, online secondhand trading still faces several challenges, including limited product features, lack of product transparency, and poor transaction and customer satisfaction management. This study aims to design and develop a web-based e-commerce system called ThriftMart to facilitate secure and efficient secondhand transactions. The system was built using the Waterfall methodology, which includes requirement analysis, system design, development, testing, and maintenance phases. Laravel and MySQL were utilized as the main technologies, supporting features such as product management, ordering, payment processing, customer reviews, multi-role dashboards, and real-time notifications. Testing results indicate that all core features function properly and meet user expectations. The system’s development has implications for promoting circular economy practices, improving operational efficiency in thrift businesses, and supporting the digital transformation of MSMEs in the secondhand fashion sector. This application is expected to strengthen a more sustainable and environmentally friendly digital thrift ecosystem in Indonesia.

Employee recruitment is a critical aspect of human resource management that significantly influences the success and sustainability of a company. Communion Coffee Brewer, a company in the food and beverage industry, faces challenges in effectively recruiting employees to ensure alignment with company needs and culture. To address this, the implementation of Decision Support System (DSS) using the SMART Methodology proves to be a relevant solution. This method allows for objective evaluation based on predefined criteria while streamlining the decision-making process. By integrating this technology, Communion Coffee Brewer can enhance efficiency and effectiveness in recruitment, support sustainable company growth, and strengthen its long-term operations.
 

CT is the ability to think to solve problems whose solution is computing. CT abilities cannot possibly grow in an instant, CT knowledge and skills need time to grow and develop so that they produce as expected. CT training for kindergarten and elementary school teachers at Gaussian Kamil School will be carried out in several stages, starting with an introduction to the CT concept, practice questions, practicing CT using digital methods, and Unplugged. It is hoped that this training can improve teachers' CT abilities, so that teachers will infuse it with students. Students who receive CT from an early age are expected to be able to be independent and behave better because they are used to solving problems in the correct, fast and efficient way.

Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces.