Riana Magdalena Silitonga

The rapid development of AI Technology has significantly influenced digital art and triggered widespread discussion on social media platforms, particularly X. The use of AI in generating visual artworks and digital content has elicited diverse public responses, ranging from support for technological innovation to concerns regarding originality and the role of human artists. In this study, a total of 1737 tweets were collected through a data crawling process using relevant keywords and processed using RapidMiner through preprocessing stages to analyze user sentiment on the X platform toward the application of AI in digital art. The data include data cleaning, text normalization, and tokenization, before being classified into positive and negative sentiments. Three classification algorithms, Naïve Bayes, support vector machine (SVM), and decision tree, were applied to compare sentiment distributions. The results show that the Naïve Bayes model classified 30.5% of tweets as positive and 69.5% as negative, while the SVM and Decision Tree models showed a stronger bias toward negative sentiment, with 93.3% and 88.8% negative classifications, respectively. These findings indicate that negative sentiment toward AI in digital art is more dominant among users.

In the rapidly evolving era, with the advancement of AI technology in education, Chat Generative Pre-trained Transformer (ChatGPT) is widely used in education to help students simplify the learning process. In other words, the implementation of ChatGPT makes the learning process more efficient and relevant. This study was conducted to analyze sentiment from social media platforms such as X to determine the impact of ChatGPT’s use in higher education in Indonesia. The research method involves data collection using the data crawling method for the X platform, which is integrated with the RapidMiner application. This sentiment analysis aims to identify trends in positive, negative, and neutral sentiment towards the use of ChatGPT in higher education in Indonesia and Thailand by using the Naive Bayes Classifier classification method and the Cross-Industry Standard Process for Data Mining method to design, execute, and evaluate data analytics projects. This analysis is expected to provide an initial overview of emerging sentiment trends as well as insights into how ChatGPT is perceived in the higher education environment. Overall, the results of this study provide an overview of public perception regarding the influence of ChatGPT in higher education in Indonesia and serve as a foundation for developing policies related to more responsible AI implementation in the academic environment.

A production system comprises a series of interconnected processes involving planning, processing, and product distribution. The effectiveness and efficiency of such systems play a vital role in reducing operational costs, enhancing productivity, and improving product quality. As such, regular evaluation of production systems is essential to identify inefficiencies, waste, and bottlenecks, and to develop targeted strategies for improvement. This research aims to construct a simulation model of a production system using FlexSim software as a decision-support tool to facilitate performance evaluation and the development of scenario-based solutions. By employing a simulation-based approach, this study enables the analysis of the production process without interfering with actual operations, thereby minimizing associated risks and reducing the consumption of time and resources. Furthermore, simulation allows for virtual testing of various operational scenarios, including modifications in production capacity, workforce allocation, workflow configurations, and the implementation of emerging technologies. In this case study, the production process was predominantly constrained by operator waiting time, which constituted approximately 30% of the total processing time. In response, an alternative scenario was developed wherein operators with lower utilization rates were reassigned to workstations characterized by high operator wait times. The implementation of this scenario yielded a 29.5% reduction in average queue waiting time and a 31.7% decrease in total production time. These findings demonstrate a substantial improvement in production efficiency. Therefore, the outcomes of this study are expected to provide valuable insights for strategic decision-making and support the optimization of production systems in industrial environments.

The COVID-19 pandemic has significantly impacted the retail industry, particularly shopping malls, which have experienced a 30% to 40% decline in customer footfall in the first quarter of 2021 compared to previous years. Despite the growing body of research on retail recovery, there remains a gap in understanding specific customer sentiments and experiences within mall environments during this period. This study aims to address this gap by analyzing customer feedback extracted from Google reviews to identify key attributes influencing the shopping experience. The research employs text-mining techniques to convert unstructured data into structured formats, eliminating irrelevant words and concepts. Following data cleaning, the Naïve Bayes Classifier method is utilized to analyze the sentiments, achieving an accuracy of around 92.86%. The findings will be visualized through word clouds and bar charts to highlight the top ten positive and negative sentiments expressed by customers. Ultimately, this research contributes to enhancing the customer experience at shopping malls by collaborating with relevant stakeholders to create a comprehensive Customer Journey Map based on the analyzed data. This map will serve as a strategic tool for mall management to implement targeted improvements and foster a more engaging shopping environment.

Financial literacy has grown significantly in recent years, leading to increased accountability among individuals in managing their spending, investments, and financial planning. To effectively develop new technologies, how potential users respond to them beforehand must be understood. In this study, we developed a model based on the unified theory of acceptance and use of technology (UTAUT), encompassing five primary components: performance expectations, social influence, facilitating conditions, satisfaction, and continuation intentions. A total of 250 participants’ data were analyzed using an artificial neural network (ANN) to evaluate the integrated model. The results showed variables affecting lower-middle-class users of a digital financial literacy application’s acceptance of new technology.

Environmental, social, and governance (ESG) has become a concern for companies, investors, and regulators. Its significance cannot be underestimated, as stakeholders increasingly demand accountability and transparency regarding corporate practices in these areas. Government agencies enforce laws mandating companies adhere to established ESG standards in response. However, despite these regulatory pressures, several obstacles have hindered organizations from effectively implementing sustainability initiatives, often resulting in lackluster outcomes. In this study, we developed a framework to implement ESG principles across various companies, utilizing the critical success factor (CSF) theory. By incorporating the perspectives of stakeholders, we identified the essential elements to achieve ESG. The developed framework in ESG studies employed the hybrid Delphi technique and the analytical hierarchy process (AHP), a structured method for organizing and analyzing complex decisions. Based on the results obtained from targeted questions, variables that influence ESG performance were identified. The effectiveness of different sustainability initiatives was also assessed to understand stakeholder engagement strategies and evaluate the impact of organizational culture on ESG adoption.

Reading is an essential skill for learners, yet numerous individuals face challenges in understanding text, particularly in digital learning. This study aims to introduce an experimental design plan to improve reading performance in online learning environments. The experimental design incorporates various visual components, including images and colored text, alongside audio elements that may influence reading comprehension. This experimental design utilizes an eye-tracking study methodology. The result of this study can be used for robust experimental designs. The result of the experiment can be used to optimize online learning materials, thereby enhancing students’ reading skills in an online context.

This study presents a breakthrough in sustainable injection molding by uniquely combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO) to overcome traditional optimization challenges. The BPNN’s exceptional ability to learn complex nonlinear relationships between six key process parameters (including melt temperature and holding pressure) and product quality is amplified by PSO’s intelligent search capability, which efficiently navigates the high-dimensional parameter space. Together, this hybrid approach achieves what neither method could accomplish alone: the BPNN accurately models the intricate process-quality relationships, while PSO rapidly converges on optimal parameter sets that simultaneously meet strict quality targets (66–70 g weight, 3–5 mm thickness) and minimize energy consumption. The significance of this integration is demonstrated through three key outcomes: First, the BPNN-PSO combination reduced optimization time by 40% compared to traditional trial-and-error methods. Second, it achieved remarkable prediction accuracy (RMSE 0.8229 for thickness, 1.5123 for weight) that surpassed standalone BPNN implementations. Third, the method’s efficiency enabled SMEs to achieve CAE-level precision without expensive software, reducing setup costs by approximately 25%. Experimental validation confirmed that the optimized parameters decreased energy use by 28% and material waste by 35% while consistently producing parts within specifications. This research provides manufacturers with a practical, scalable solution that transforms injection molding from an experience-dependent craft to a data-driven science. The BPNN-PSO framework not only delivers superior technical results but does so in a way that is accessible to resource-constrained manufacturers, marking a significant step toward sustainable, intelligent production systems. For SMEs, this framework offers a practical pathway to achieve both economic and environmental sustainability, reducing reliance on resource-intensive CAE tools while cutting production costs by an estimated 22% through waste and energy savings. The study provides a replicable blueprint for implementing data-driven sustainability in injection molding operations without compromising product quality or operational efficiency.