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In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the entire year of 2025. The ETL stage focused on data cleaning and handling missing values, while time series analysis began with the Augmented Dickey-Fuller (ADF) stationarity test, which yielded a significant p-value of 0.000006. The parameter model was optimized using the auto_arima algorithm, which determined the ARIMA(2,0,0) configuration as the best model. The evaluation results of the model show fairly stable performance with a Root Mean Squared Error (RMSE) value of 2.002 and a Mean Absolute Error (MAE) of 1.704 on the test data. Research findings reveal a consistently higher purchasing pattern during the mid-month and end-of-month periods, with an average of 5.52 daily transactions, compared to the beginning of the month, which saw 5.48 transactions. The 30-day forecast results provide valuable insights for online store managers to proactively adjust inventory and logistics workforce allocation strategies. This research concludes that integrating data engineering techniques and statistical analysis can provide predictive solutions for the dynamics of the digital market.

This paper explores the transformative influence of artificial intelligence (AI) on the accounting profession, particularly within the Accountant General of the Federation (OAGF). The research investigates how AI-driven innovations are reshaping traditional accounting practices and redefining the role of accountants. By conducting a systematic literature review, this study identifies three primary dimensions of AI’s impact: the automation of repetitive tasks such as data entry, transaction processing, and reconciliation; enhanced data analytics capabilities, which include predictive modeling and real-time decision support; and the evolution of accountants' roles toward more strategic and value-added activities, such as financial advisory and risk management. The automation of routine processes through AI allows accountants to focus on higher-level tasks that require judgment, creativity, and expertise, ultimately enhancing the overall efficiency of the accounting function. Furthermore, AI’s advanced data analytics tools provide more accurate insights, enabling accountants to offer more effective financial guidance and make more informed decisions. As AI reduces the time spent on manual processes, accounting professionals can improve their role in advising on business strategy, improving risk management, and identifying new growth opportunities. The study’s findings underscore the importance of embracing AI in the accounting profession, not only to improve operational efficiency, reduce costs, and scale operations but also to enable accountants to stay competitive in a rapidly evolving technological landscape. The paper concludes by emphasizing that adopting AI is essential for accountants to remain relevant and continue providing valuable contributions to their organizations. Future research should focus on the long-term implications of AI on accounting ethics and the development of necessary skills for accounting professionals to thrive in the age of AI.

Delivery delays pose a major challenge in the e-commerce industry, often leading to decreased customer satisfaction and negatively impacting business operations. In this study, the XGBoost (Extreme Gradient Boosting) algorithm is applied to predict delivery delays based on a dataset containing 96,476 records. These records include various features relevant to the delivery process, such as shipping distance, carrier performance, and order characteristics. The model achieves a high overall accuracy of 93.24%, indicating strong general performance. In particular, XGBoost demonstrates excellent results in predicting on-time deliveries, achieving a precision of 93% and a recall of 100%. However, the model struggles to correctly identify delayed deliveries. The recall for delayed deliveries is 0%, and the F1-score is extremely low at 0.01. This significant discrepancy reveals a critical limitation in the model's performance — the inability to detect minority class cases (delayed deliveries) due to class imbalance within the dataset. The results highlight the importance of addressing data imbalance in predictive modeling for delivery outcomes. When the dataset is dominated by on-time delivery records, the model tends to be biased toward that class, failing to learn the patterns associated with delays. To improve performance, the study recommends integrating class balancing techniques such as SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic samples of the minority class. Additionally, the use of alternative evaluation metrics beyond accuracy — such as precision, recall, and F1-score for each class — is suggested to provide a more comprehensive understanding of model effectiveness. Overall, the study provides valuable insights into the complexities of predicting delivery delays and outlines practical strategies for enhancing future models in e-commerce logistics analytics.

This study presents a machine learning approach for predicting the dimensions of microstrip antenna slots based on antenna performance parameters such as frequency, gain, directivity, return loss (S11), radiation efficiency, and VSWR. A two-phase methodology was employed. In the first phase, ten regression algorithms were evaluated, and Random Forest was identified as the most effective model based on Mean Absolute Error (MAE) and R-squared (R²) scores. In the second phase, hyperparameter tuning was conducted using Grid Search to further improve the model’s performance. The optimized Random Forest model demonstrated consistent improvements in predictive accuracy, with R² values increasing across all output variables. These results indicate that the combination of regression-based modeling and systematic hyperparameter tuning is effective for capturing complex relationships in antenna design tasks. The proposed approach offers a promising data-driven alternative for geometric prediction in microstrip antenna development, particularly when analytical models are insufficient.

Hotel booking cancellations pose significant challenges to the hospitality industry, affecting revenue management, demand forecasting, and operational efficiency. This study explores the application of machine learning techniques to predict hotel booking cancellations, leveraging structured data derived from hotel management systems. Various classification algorithms, including Random Forest, XGBoost, and LightGBM were evaluated to identify the most effective predictive model. The findings reveal that XGBoost model outperforms other models, achieving F2-score of 0.7897. Key influencing factors include deposit type, total number of special requests, and marketing segment. The results underscore the potential of predictive modeling in optimizing hotel revenue strategies by enabling proactive measures such as dynamic pricing, targeted customer engagement, and improved overbooking policies. This study contributes to the ongoing advancements in data-driven decision-making within the hospitality industry, offering insights into how machine learning can mitigate financial risks associated with booking cancellations.

Natural disasters such as earthquakes, hurricanes, and floods pose significant risks to critical infrastructure. AI-driven disaster response systems provide real-time analytics, predictive modeling, and automated response strategies to mitigate damage and improve recovery efforts. This paper explores how AI-powered drones, satellite imagery, and sensor networks enhance disaster monitoring and decision-making. Additionally, the study discusses the role of AI in optimizing emergency resource allocation and predicting infrastructure vulnerabilities. Through an analysis of past disaster management strategies, this research aims to propose AI-integrated frameworks that enhance disaster preparedness and resilience.

Natural disasters such as earthquakes, hurricanes, and floods pose significant risks to critical infrastructure. AI-driven disaster response systems provide real-time analytics, predictive modeling, and automated response strategies to mitigate damage and improve recovery efforts. This paper explores how AI-powered drones, satellite imagery, and sensor networks enhance disaster monitoring and decision-making. Additionally, the study discusses the role of AI in optimizing emergency resource allocation and predicting infrastructure vulnerabilities. Through an analysis of past disaster management strategies, this research aims to propose AI-integrated frameworks that enhance disaster preparedness and resilience.

This work explores the potential of PennyLane and variational quantum-classical algorithms (VQCA) to forecast lung cancer using a structured dataset. The VQCA model performs exceptionally well, with flawless training, validation, and test accuracies of 1.0, demonstrating its capacity to identify patterns in the dataset and provide reliable predictions successfully. Contrarily, the accuracy of the quantum neural network (QNN) and classical neural network (NN) models is lower, demonstrating the benefits of utilizing quantum computing methods for enhanced predictive modeling. We provide a complete examination of the data, stressing the better performance of the VQCA model and its promise in correctly predicting lung cancer. The results highlight the importance of quantum-classical algorithms and help us understand the benefits and drawbacks of various strategies for predicting lung cancer. The study highlights the potential applications of quantum computing techniques in advancing the field of healthcare analytics. It shows the capability of the VQCA model to predict lung cancer using a tabular dataset accurately. Further research in this area is needed to explore scalability and practical implementation aspects. In summary, this study showcases the potential of VQCA and PennyLane in predicting lung cancer and underscores the benefits of quantum computing techniques in healthcare analytics.

Universities' development hinges significantly on student admissions, necessitating accurate predictions for effective planning. This study applies the Monte Carlo simulation method to forecast new student arrivals at the Faculty of Mathematics and Natural Sciences (FMIPA) at Universitas Negeri Medan (UNIMED). Utilizing data from 2021 to 2023 sourced from the PDDikti website, the research employs PHP programming for implementation. The Monte Carlo algorithm's numerical prowess ensures precise statistical data simulation, comprising data collection, probabilistic distribution computations, cumulative distribution determinations, random number generation, and simulation analyses. Simulation results for 2022, 2023, and 2024 exhibit consistent trends, projecting an average of 860 to 930 new students per program. This methodology surpasses manual estimations, offering robust insights for university resource allocation and strategic management. Despite its effectiveness, study limitations, such as model assumptions, warrant continuous validation with actual data. This research advances predictive modeling in higher education, providing a foundation for future enhancements and comprehensive prediction integrations.

Power systems are critical infrastructure that face significant challenges due to increasing demand and inherent complexity. Predicting failures in power systems is crucial for enhancing grid reliability, minimizing downtime, and optimizing maintenance processes. This study evaluates various deep learning models, specifically convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models, for predicting power system failures. By analyzing these models’ performance metrics on historical power grid data, the study provides insights into the strengths and weaknesses of each approach. The findings contribute to the development of more robust predictive models for power system reliability.