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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.

The province of Lampung in Indonesia continues to face a great deal of poverty. The purpose of this study is to look at how poverty levels in this area are affected by population (X2) and unemployment (X1). Numerous primary and secondary sources of data are gathered, and relevant statistical techniques are applied to the analysis. The analysis's findings demonstrate that unemployment (X1) significantly affects Lampung's poverty level, with rising unemployment rates being correlated with rising poverty levels. Contrary to predictions, however, population size (X2) does not appear to have a meaningful impact on the amount of poverty. This implies that other variables might have a greater influence on how impoverished this area is. These results have significant ramifications for Lampung's development strategy. The main focus of policy should be on initiatives to lower the unemployment rate by increasing job opportunities for the populace and creating jobs through skill-training programs. Other aspects of poverty, such as access to healthcare and education, should also be taken into consideration.

This research applies the Monte Carlo simulation method to predict the movement of Apple Inc.'s stock price over a long period of time. Using historical data of Apple's stock price from 12 December 1980 to 24 March 2022, this study aims to generate a probability distribution of the future stock price. The method involves several steps, including data collection, log return calculation, parameter estimation, and simulation of the stock price path through random iterations based on the log return distribution. The simulation results show that the closing price of Apple stock can be predicted by following the historical trend, although there are differences with the real data due to the stochastic nature of the Monte Carlo technique. This research also applies a variance reduction method to improve simulation efficiency. The findings provide a valuable perspective for investors and financial analysts in identifying investment risks and opportunities through an in-depth understanding of the dynamics of stock price movements using Monte Carlo simulation. Suggestions for future research include the use of VaR methods with historical variance and covariance approaches, as well as considering longer data periods and more stock indices for more comprehensive results.

Behavioral economics has highlighted how cognitive biases influence financial decisionmaking, often leading to suboptimal outcomes. This paper explores the impact of behavioral biases such as overconfidence, loss aversion, and herding behavior on accounting and economic forecasting. By reviewing empirical evidence from market behavior, the study assesses how these biases affect financial reporting, auditing practices, and economic predictions. The paper concludes with recommendations for accountants and economists to incorporate behavioral insights into their practices to improve decisionmaking and forecasting accuracy.

Planning a business can be used as a basis for making decisions that will be taken for the future. With this planning, MSMEs can identify various risks that will arise so that their business can improve its operational performance. The aim of this service is to determine sales and income projections for MSMEs in Batulaya Village. The results of the service concluded that if there is a plan, the company can ensure the continuity of the company for the long term. Apart from good planning, a company also needs to prepare a budget, so that the activities the company will carry out can be planned properly. The company budget plays a very important role in planning all company activities for a certain period of time in the future. A sales budget can provide an overview of estimates or predictions about the number of products or services that will be sold in a certain time period in the future.

Skin diseases are highly prevalent and transmissible. It has been one of the major health problems that most people face. The diseases are dangerous to the skin and tend to spread over time. A patient can be cured of these skin diseases if they are detected on time and treated early. However, it is difficult to identify these diseases and provide the right medications. This study's research objectives involve developing an ensemble machine learning based model for classifying Erythemato-Squamous Diseases (ESD). The ensemble techniques combine five different classifiers, Naïve Bayes, Support Vector Classifier, Decision Tree, Random Forest, and Gradient Boosting, by merging their predictions and utilizing them as input features for a meta-classifier during training. We tested and validated the ensemble model using the dataset from the University of California, Irvine (UCI) repository to assess its effectiveness. The Individual classifiers achieved different accuracies: Naïve Bayes (85.41%), Support Vector Machine (98.61%), Random Forest (97.91%), Decision Tree (95.13%), Gradient Boosting (95.83%). The stacking method yielded a higher accuracy of 99.30% and a precision of 1.00, recall of 0.96, F1 score of 0.97, and specificity of 1.00 compared to the base models. The study confirms the effectiveness of ensemble learning techniques in classifying ESD.

The number of incidents occurring on the island of Sumatra, especially West Sumatera and Jambi provinces that affect the vibration of the earthquake in Rengat City on a large scale can lead to the movement of land such as landslides and stagnant ground water sources can lead to liquefaction. The purpose of this research is to know how the arrangement of soil type at research location Rengat City and to know the location reviewed in Rengat City, and its surrounding experience potential liquefaction based on some existing penetration test data. This research was conducted to predict liquefaction potential that occurred in Rengat City of Indragiri Hulu Regency with secondary data of penetration test of konus with Seed and Idriss method (1971). The calculation of potential liquefaction prediction is done by determining the number of soil layers, estimating the weight of the soil volume, determining the soil overburden, determining the effective soil stress, determining the corrected confront resistance, calculating Cyclic Strees Ratio (CSR), calculating Cyclic Resistance Ratio (CRR). After calculating the value of Magnitude Scalling Factor (MSF) and calculating the value of safe factor by comparing the value of Cyclic Strees Ratio (CSR) with Cyclic Resistance Ratio (CRR) if the calculation <1 factor is safe then potentially liquefaction occurs. Based on the results of the analysis of the 7 locations with a total of 2 cone penetration test conus analyzed in this study in Rengat City, obtained the type of soil structure is not uniform and based on the results of analysis, from the 7 research sites with tottal 21 point penetration test konus reviewed at magnitude 5 to 10 in Rengat City Indragiri Hulu Regency. In the type of sand soil in the dominant study area can not be achieved liquefaction, this is reinforced from the calculation (SF)> 1 safe factor (SF: 1.0).

The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction through CNN with temporal dependency modeling via LSTM to enhance predictive accuracy and clinical decision support. A quantitative experimental design was employed, utilizing multi-source healthcare datasets that underwent preprocessing, normalization, and feature engineering prior to model training. The performance of the hybrid model was evaluated using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Mean Absolute Error (MAE), and compared with conventional machine learning models and standalone deep learning architectures. Experimental results demonstrate that the proposed CNN–LSTM model achieves superior performance, with improved classification accuracy and reduced prediction error, while maintaining strong generalization capability. The findings indicate that integrating spatial and temporal feature learning significantly enhances disease detection, risk stratification, and personalized treatment planning. This approach supports the development of intelligent clinical decision support systems and scalable smart healthcare environments. The proposed framework offers a reliable and efficient solution for advanced healthcare analytics in IoT-enabled systems.

Autonomous vehicles (AVs) are revolutionizing transportation by relying on advanced AI techniques like deep learning and reinforcement learning for decision-making and navigation. However, concerns about the opacity of traditional AI models in safety-critical applications such as autonomous driving raise issues related to safety, accountability, and trust. This study explores the integration of Explainable AI (XAI) techniques in AV systems to enhance transparency and interpretability while maintaining high prediction accuracy. XAI methods, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive ExPlanations), provide understandable justifications for AI-driven decisions, addressing biases, fairness, and accountability. These techniques also support regulatory compliance and foster public trust in AVs. A mixed-methods approach, combining experimental simulations and user surveys, was employed to integrate XAI into AV systems and test its performance in urban traffic and highway driving scenarios. Feedback from users, collected through questionnaires and in-depth interviews, revealed that XAI-enhanced systems significantly improved the interpretability of AV decisions, leading to higher user trust and satisfaction. The study highlights the importance of balancing model complexity with interpretability, demonstrating that XAI techniques are crucial for building trust and ensuring accountability in autonomous driving systems.

This systematic literature review examines the evolving role of Quality Engineering (QE) in optimizing production processes within Industry 4.0 contexts. By analyzing 78 peer-reviewed studies (2010–2025), the research identifies critical shifts in QE methodologies, emphasizing integration with artificial intelligence (AI), machine learning (ML), and real-time digital twin technologies. Key findings reveal enhanced robustness through adaptive optimization algorithms (e.g., Bayesian optimization, NSGA-II), improved defect prediction via AI-driven quality control systems, and streamlined process interoperability through Manufacturing Execution Systems (MES) and Quality 4.0 frameworks. The study underscores digital integration as a catalyst for reducing variability, accelerating decision-making, and aligning quality management with Industry 4.0’s demands for agility and interconnected systems. Recommendations include adopting hybrid methodologies combining classical Six Sigma with ML-driven analytics and investing in workforce training for digital QMS adoption.

This research relates to predictions of laptop sales in computer shops in Central Aceh, with a focus on laptop brands Acer, Asus, HP and Lenovo. Over the last three years, sales of these laptops have reached 1,629 units, with a monthly average of between 108 and 150 units. Business owners today prefer brands with the highest percentage of sales, but this can lead to dead stock problems. Therefore, the author proposes using data mining techniques, especially the K-Nearest Neighbor (K-NN) method, to make recommendations for the number of products to be purchased by business owners based on past sales data. The K-NN method requires complete, structured and continuous sales data. It is important to choose an appropriate K value, and other factors such as weather, seasons, promotions, and special events also affect laptop sales. K-NN models may need to be combined with other data to improve prediction accuracy. It is hoped that this research will provide academic benefits in expanding knowledge about the use of the K-NN method in sales prediction, as well as practical benefits for business owners in planning their sales strategies. The research conclusions highlight the importance of good data collection, choosing the right K value, and considering external factors in the laptop sales prediction process.      

Artificial Intelligence (AI) has been applied to many human endeavors, and epidemiology is no exception. The AI community has recently seen a renewed interest in applying AI methods and approaches to epidemiological problems. However, a number of challenges are impeding the growth of the field. This work reviews the uses and applications of AI in epidemiology from 1994 to 2023. The following themes were uncovered: epidemic outbreak tracking and surveillance, Geo-location and visualization of epidemics data, Tele-Health, vaccine resistance and hesitancy sentiment analysis, diagnosis, predicting and monitoring recovery and mortality, and decision support systems. Disease detection received the most interest during the time under review. Furthermore, the following AI approaches were found to be used in epidemiology: prediction, geographic information systems (GIS), knowledge representation, analytics, sentiment analysis, contagion analysis, warning systems, and classification. Finally, the work makes the following findings: the absence of benchmark datasets for epidemiological purposes, the need to develop ethical guidelines to regulate the development of AI for epidemiology as this is a major issue impeding it’s growth, a concerted and continuous collaboration between AI and Epidemiology experts to grow the field, the need to develop explainable and privacy retaining AI methods for more secured and human understandable AI solutions.

Fraudsters increasingly exploit unauthorized credit card information for financial gain, targeting un-suspecting users, especially as financial institutions expand their services to semi-urban and rural areas. This, in turn, has continued to ripple across society, causing huge financial losses and lowering user trust implications for all cardholders. Thus, banks cum financial institutions are today poised to implement fraud detection schemes. Five algorithms were trained with and without the application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.

The term "depth of anesthesia" refers to the extent to which a general anesthetic agent anesthetizes the central nervous system with a specific concentration of force at the time the drug is administered. The depth level of anesthesia plays an important role in determining surgical complications, and it is very important to keep the depth level of anesthesia under control for the operation to be successful. The purpose of this study is to determine the Monitoring Picture of Anesthesia Depth in General Anesthesia. The literature search will be conducted between 2018 and 2023. The methodof journals and scientific articles contained in this study are national and international journals that have been accounted for their validity. Sources of information obtained from databases are Google Scholar, Science Direct, and PubMed. Anesthesia depth monitoring with the most popular modern technique in practice is the Bixpectral Index Score (BIS) monitor. Anesthesia depth monitoring using BIS is more accurate than traditional anesthesia depth monitoring. Objective assessment of the depth of sedation can use the BIS tool that provides the best prediction of the patient's degree of consciousness to] prevent the patient from waking up and remember actions, drug additions, and wake predictions more accurately by looking at changes in brain electrical activity depicted through EEG

This study explores the application of stochastic differential equations (SDEs) in modeling population dynamics. By introducing randomness into differential equations, we capture the inherent uncertainties in environmental and genetic factors affecting population growth. We derive analytical solutions for specific cases and provide numerical simulations to demonstrate how SDEs enhance predictions in ecological modeling. Our findings suggest that stochastic models provide a more robust framework for understanding population fluctuations in uncertain environments.

The occurrence of scorch during the production of flexible polyurethane is a significant issue that negatively impacts foam products' resilience and generally jeopardizes their integrity. The likelihood of foam product failure can be decreased by optimizing production variables based on machine learning algorithms used to predict the occurrence of scorch. Investigating technology is required because prevention is the best approach to dealing with this problem. Hence, machine learning algorithms were trained to predict the occurrence of scorch using the thermodynamic profile of polyurethane foam, which is made up of recorded production variables. A variety of heuristics algorithms were trained and assessed for how well they performed, namely XGBoost, Decision trees, Random Forest, K-nearest neighbors, Naive Bayes, Support Vector Machines, and Logistic Regression. The XGboost ensemble was found to perform best. It outperformed others with an accuracy of 98.3% (i.e., 0.983), followed by logistic regression, decision tree, random forest, K-nearest neighbors, and naïve Bayes, yielding a training accuracy of 88.1%, 66.7%, 84.2%, 87.5%, and 67.5% respectively. The XGBoost was finally used, yielding 2-distinct cases of non(occurrence) of scorch. Ensemble demonstrates that it is quite capable and is an effective way to predict the occurrence of scorch.

The rapid spread of infectious diseases remains a major global health threat, and early detection is vital to minimize their impact. This research investigates the role of predictive modeling using big data in the early detection of infectious disease outbreaks. The primary objective of this study is to assess the effectiveness of big data systems in forecasting potential outbreaks and the implications of these forecasts for public health systems. The study employs machine learning-based predictive models to process large health datasets, including electronic health records, sensor data, and social media information. The results demonstrate that the predictive model achieved an accuracy rate of 87%, significantly surpassing traditional methods in terms of early detection. By integrating various data sources such as medical records, sensor networks, and real-time digital traces, the system is capable of providing more accurate, timely predictions, which can greatly improve the ability of public health authorities to respond effectively to emerging health threats. Furthermore, the application of big data in public health not only improves the speed of response but also enhances the allocation of resources, allowing for more targeted and efficient interventions. Despite these successes, challenges remain, particularly in relation to data quality, privacy, and regulatory issues, which could hinder the broader implementation of such systems. Thus, collaboration between government agencies, healthcare institutions, and technology developers is essential to overcome these obstacles and ensure the sustainable integration of big data into public health infrastructures. This research highlights the significant potential of big data to transform public health responses, offering valuable insights for future epidemic management strategies.  

Rice plays a vital role as the main food source for almost half of the global population, contributing more than 21% of the total calories humans need. Production predictions are important for determining import-export policies. This research proposes the XGBoost method to predict rice harvests globally using FAO and World Bank datasets. Feature analysis, removal of duplicate data, and parameter tuning were carried out to support the performance of the XGBoost method. The results showed excellent performance based on which reached 0.99. Evaluation of model performance using metrics such as MSE, and MAE measured by k-fold validation show that XGBoost has a high ability to predict crop yields accurately compared to other regression methods such as Random Forest (RF), Gradient Boost (GB), Bagging Regressor (BR) and K-Nearest Neighbor (KNN). Apart from that, an ablation study was also carried out by comparing the performance of each model with various features and state-of-the-art. The results prove the superiority of the proposed XGBoost method. Where results are consistent, and performance is better, this model can effectively support agricultural sustainability, especially rice production.

A blood donor is someone who wants to donate their own blood to people in need without any element of coercion from anyone. Predicting the number of blood donors is very important and necessary to find out the number of blood donors in Langkat Regency in 2023-2024, and the prediction results can help PMI Langkat Regency in increasing the number of blood donors. The method applied in this prediction system is Linear Regression, where this analysis determines whether or not each variable is in accordance with the prediction results being tested and estimates that the value of the variable will increase or decrease each month. The prediction system is carried out using the RapidMiner application because this application is very appropriate for producing information output in the form of prediction results for the coming year. The prediction results obtained by testing using the Linear Regression method show increases and decreases every month. There are 11 months where there has been an increase and decrease in the predicted results and are in accordance with the data in 2023, then there is 1 month which has decreased in the predicted results and does not match the data in 2023. From the overall data results, it can be calculated the number of blood donors in Langkat Regency in 2023 and every month. Measuring the error level of prediction results using RMSE, the resulting accuracy level was 83.574%.

This study aims to evaluate the application of a new methodology in investment decision-making, specifically using the regression tree approach on stock market indices. This approach is expected to enhance prediction accuracy and assist investors in making more informed investment decisions, especially in volatile and uncertain markets. Based on the literature review, regression trees offer advantages in identifying non-linear relationships between market variables that are often undetected by traditional models such as the Capital Asset Pricing Model (CAPM). Despite its advantages, the application of regression trees also faces challenges, such as overfitting issues and the need for large and complex data. This study concludes that regression trees can improve investment decision-making, but careful attention is required regarding model tuning and data quality.