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The increasingly advanced era is certainly followed by various developments in the fields of economy, social, culture and even technology. Technological progress in various countries can be seen from the existence of a technology called AI. Artificial Intelligence or AI is an artificial intelligence technology that has the ability to solve problems like humans. In practice, AI will simulate human intelligence with the ability to recognize images, write and even make predictions based on data. Lately, AI has begun to be misused by individuals who feel they are benefiting. In Indonesia, the misuse of AI technology has been rampant where perpetrators use AI for fraud. An act that uses technology as the main weapon of crime is included in the category of cyber crime. The rampant case of cyber crime in Indonesia is the act of voice imitation or voice cloning. The perpetrators in committing their crimes usually use a technological intermediary that can change their voice so that it is similar to the voice of relatives, friends, family or important people or public figures recognized by the victim. Voice imitation or known as voice cloning is the ability to imitate a voice that is similar to the original voice. Voice cloning using AI technology is an imitation of the human voice with an extraordinary level of accuracy of similarity, both in intonation, tone, and also voice patterns. The law on information and electronic transactions contains various regulations regarding information and electronic transactions, but the law does not explain in detail the misuse of AI in voice cloning because there are still limitations in the ITE law regarding criminal acts of voice cloning fraud and until now criminal acts of voice cloning are still rampant.

Heart failure is the most common cardiovascular disease. Gambiran Regional Hospital has the 1st position of heart failure cases out of the top 10 diseases with the largest population in hospitalizations. To determine the prediction of heart failure disease in 2025-2028 which will increase or decrease. Using a descriptive research method, with a retrospective study research approach The population of all heart failure patients in 2021-2024 at Gambiran Regional Hospital, with a sampling technique of total sampling, the number of samples of inpatient heart failure patients in 2021-2024 at Gambiran Regional Hospital. Data collection was carried out by observation. The total number of heart failure patients increased significantly to 259 (2022). The trend has increased and decreased, the number of male patients jumped sharply to 150,3 (2024), while for women it jumped to 92,3 (2024). Overall, the prediction of the highest heart failure patient in 2028 will reach 316,2 while the lowest will be in 2025. The number of heart failure hospitalizations shows a trend of change that tends to increase during 2021–2024. Based on gender, male patients dominated visits. The 2025–2028 prediction predicts an increase in the number of patients, with the highest number in male patients and total visits reaching 316,2.: Hospitals can collaborate with local health departments to hold routine screening programs for those at high risk.

Misconseption are errors in understanding concepts than often occur in the context of science learning, particulary on the greenhouse effect, this study aims to address student’s misconceptions by implementing thePredict-Observe-Explain (POE) learning model in grade VI elementary school students. The study employed the Penelitian Tindakan Kelas (PTK) method based on the kemmis and McTggart model, which consists of four main stage: planning, implementation, observation, and reflection. The reseach subjek consisted of students from grade VI from SDN 13 Cingkariang. Data wewre Collected using Questionnaires and analyzed quantitatively based on pre-test and post-test result. The findings revealed the prior to that application of the POE model, only 15% of students had agoog understnading of the greenhouse effect, while 40% had partial understanding, and 45% did not understand the concept at all. After implementing the POE model, student’s understanding improve significantly, with 70% able to make accurate predictions. 80% actively involved in observation, ang 35% able to explain the greenhouse effect comprehensively. The implementation of the POE model has proven to be effective in correcting misconcepsions and improving student’s understanding through active and exploratory learning.

Misconseption are errors in understanding concepts than often occur in the context of science learning, particulary on the greenhouse effect, this study aims to address student’s misconceptions by implementing thePredict-Observe-Explain (POE) learning model in grade VI elementary school students. The study employed the Penelitian Tindakan Kelas (PTK) method based on the kemmis and McTggart model, which consists of four main stage: planning, implementation, observation, and reflection. The reseach subjek consisted of students from grade VI from SDN 13 Cingkariang. Data wewre Collected using Questionnaires and analyzed quantitatively based on pre-test and post-test result. The findings revealed the prior to that application of the POE model, only 15% of students had agoog understnading of the greenhouse effect, while 40% had partial understanding, and 45% did not understand the concept at all. After implementing the POE model, student’s understanding improve significantly, with 70% able to make accurate predictions. 80% actively involved in observation, ang 35% able to explain the greenhouse effect comprehensively. The implementation of the POE model has proven to be effective in correcting misconcepsions and improving student’s understanding through active and exploratory learning.

The development of vocational education requires Vocational High Schools (SMK) to align their competencies with student interests and industry needs. However, a mismatch between student interests and the competencies offered can result in low enrollment, requiring schools to consider closing certain programs. This study proposes the application of Artificial Neural Networks (ANNs) as a predictive method to determine the potential closure of vocational competencies based on an analysis of student interest patterns. The data used includes interest history, academic grades, and other preference indicators, which are then subjected to a preprocessing stage to ensure the quality of the model’s input. The ANN is trained to accurately recognize interest patterns, thus generating objective and adaptive decision-making recommendations. The results show that the ANN implementation provides high accuracy in predicting student interest trends and provides more precise The development of vocational education in Vocational High Schools (SMK) requires the ability to align skill competencies with students' interests and industry needs. A mismatch between students' interests and the competencies offered can lead to low interest in certain programs, which in turn may result in the decision to close those programs. This study proposes the application of Artificial Neural Networks (ANN) as a predictive method to determine the potential closure of skill competencies based on the analysis of students' interest patterns. The data used includes interest history, academic grades, and other preference indicators. This data is processed through a preprocessing stage to ensure the quality of input for the model. The ANN is trained to accurately recognize students' interest patterns, allowing it to generate more objective and adaptive decision recommendations. The results of the study show that the application of ANN has high accuracy in predicting students' interest trends and provides more precise recommendations compared to traditional methods. Therefore, this system can be an effective tool for schools to plan curriculum policies more strategically and sustainably, as well as support decisions regarding skill programs that align with students' interests and industry needs.  

Introduction: Achieving carbon neutrality in industrial systems is essential for mitigating climate change and promoting sustainability. The increasing demand for energy optimization and carbon emission reduction has driven the development of advanced technologies, particularly hybrid machine learning (ML) models. These models, combining ensemble learning and reinforcement learning (RL), offer significant promise in optimizing industrial processes, reducing energy consumption, and improving environmental performance. This study explores the application of hybrid ML models in achieving carbon neutral goals through dynamic process optimization and energy control in industrial settings. Literature Review: Hybrid ML models integrate different machine learning techniques to handle complex and dynamic environments effectively. Ensemble learning methods, such as boosting, bagging, and stacking, combine multiple algorithms to improve predictive performance and robustness. Reinforcement learning (RL), on the other hand, enables real time decision making and adaptation based on trial and error interactions with the environment. In energy optimization, these models are used to reduce energy intensity and carbon emissions, enhancing overall operational efficiency. Previous studies have demonstrated the effectiveness of ML models in energy management, but challenges such as data quality, model integration, and computational complexity remain. Materials and Method: The study applies hybrid ML models combining ensemble learning and RL to optimize energy consumption and minimize carbon emissions in industrial processes. Data from real time sensors and operational parameters are used to train the models. The ensemble learning component improves the accuracy of energy predictions, while RL ensures dynamic process adjustments in response to fluctuating energy demand. The models were tested in various industrial settings, including manufacturing processes, smart grids, and microgrid systems. Performance metrics such as energy efficiency, carbon emissions reduction, and operational costs were evaluated to assess the effectiveness of the models.  Results and Discussion: The hybrid ML models achieved significant reductions in energy intensity (15-20%) and carbon emissions (18-25%). The real time adaptability of the RL component allowed the models to adjust energy consumption patterns dynamically, improving energy efficiency and reducing waste. The models demonstrated their ability to adapt to varying operational conditions, ensuring optimal energy use. A cost-benefit analysis showed that the hybrid models provided substantial energy savings and reduced operational costs, with a return on investment (ROI) of 30-35% within the first year of deployment. However, challenges such as computational complexity and data quality issues were identified, highlighting the need for further refinement in model development.

In this study, the population growth rate of Pematang Siantar City during the period 2022–2025 is discussed through an improper integral approach. Population growth patterns must be analyzed and predicted in the long term. Linear, geometric, and exponential growth models are used to analyze data taken from the Statistics Center. The calculation results show that the population growth rate changes every year, with an average change of 0.10% per year. The prediction for 2025–2026 shows a growth of 0.59%, indicating an increase in population. However, through an improper integral approach, it is found that a population that experiences a decrease in growth rate over time will reach a limited cumulative total population value, even if the population continues to increase. These results indicate that a long-term downward trend can lead to population shrinkage, uncontrolled growth can lead to population density. As a result, this study is expected to provide a scientific basis for population policies and sustainable development planning in Pematang Siantar City.

In geotechnical engineering, building robust structures is crucial to ensure the bearing capacity of structures against external forces, so making sure soil strength and unreliable build cost and duration prediction are also very important and preliminary aspects of any construction project. Therefore, in this first-of-its-kind modern examine, the capability of various artificially intelligent (AI)-based models toward reliable forecasting and estimation of preliminary construction expenses, duration, and strength at shear is explored. First, background information about the revolutionary artificial intelligence (AI) technique along with its many distinct models ideal for geotechnical and building engineering problems is presented, The use of AI-based models in the literature for the aforementioned construction and maintenance applications is discussed in a number of current works, together with their benefits, drawbacks, and future directions. Several important input elements that significantly affect the preliminary price of construction, construction time, and soil's shear strength estimation are listed and given through analysis. Finally, some obstacles to employing AI-based models for precise forecasts in these applications are discussed, along with elements influencing the problems with cost overruns. Thus, this work can help civil engineers make effective use of artificial intelligence (AI) to solve difficult and risky tasks. It can also be used to Internet of Things (IoT) environments for self-learning applications like smart architectural health-monitoring systems

Pressure vessels are critical components in the energy industry, used to store and process high-pressure fluids. The structural reliability of these vessels plays a pivotal role in ensuring operational safety and system efficiency. This study aims to analyze the design and reliability of pressure vessels using both numerical and experimental approaches to optimize performance and enhance safety factors. The numerical method was conducted through Finite Element Analysis (FEA) using ANSYS software to evaluate stress distribution, stress concentration, and potential failure modes under various operational load scenarios. Meanwhile, the experimental method involved hydrostatic pressure testing, strain measurements using strain gauges, and displacement analysis to validate the numerical simulation results. Data were collected from simulations and laboratory experiments, then analyzed quantitatively by comparing key parameters such as stress distribution, deformation patterns, and safety factors against industry standards. The results indicate that combining numerical and experimental approaches improves the accuracy of pressure vessel behavior predictions, enables more efficient design optimization, and enhances structural reliability. In conclusion, the methods applied in this study can serve as a reference for developing safer, more efficient pressure vessel designs that comply with industrial standards, thereby supporting improved safety and operational efficiency in the energy sector.

Given the increasing prevalence of mental health challenges in digital work settings, especially among IT remote workers, early detection mechanisms have become critically important. This study aims to improve the prediction accuracy of mental health conditions among IT remote workers by integrating feature engineering techniques within machine learning models. Five algorithms consisting of Random Forest, Logistic Regression, K-Nearest Neighbors, Decision Tree, and Naive Bayes were evaluated. The Random Forest model achieved the best performance, with 83% accuracy, 83% precision, 100% recall, and a 90% F1-score, followed closely by Logistic Regression with 82% accuracy. Nevertheless, the results demonstrate the feasibility of applying machine learning to support the early detection of mental health risks, offering a strong foundation for future research in predictive analytics and the development of intelligent support systems within digital work environments.

Accurate sales forecasting is essential for stakeholders to make strategic decisions. This study aims to compare the performance of two deep learning models, namely Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), in forecasting domestic motorcycle sales produced by AISI member manufacturers. The forecast is based on historical data from January 2021 to December 2024. The model was trained using time series data and the forecasting results for the period January to March 2025 were evaluated using the metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that the LSTM model produces lower MAE and MAPE values than CNN, which shows its superiority in providing more accurate and consistent predictions. On the other hand, the CNN model has lower RMSE and MSE values, thus being able to reduce large prediction errors. By comparing the results of LSTM, CNN, and actual data forecasting, the LSTM model is more suitable for forecasting motorcycle sales in Indonesia

Communication is the process of exchanging information, ideas, thoughts, and feelings between individuals or groups through the use of words, signs, or actions. This process can take place verbally or non-verbally and involves various media and channels, such as face-to-face conversations, writing, gestures, facial expressions, and digital technology. This research was conducted at STMIK Kaputama Binjai, namely the WhatsApp group between lecturers and students. This study uses the Support Vector Machine (SVM) method. SVM is a type of supervised learning machine learning that requires sample data. Support Vector Machine (SVM) is an algorithm developed by Boser, Guyon, and Vapnik in 1992. Support Vector Machine (SVM) has a concept that is combined with previous computational theories. This method can transform training data into higher dimensions using non-linear patterns. The results of the Support Vector Machine method classification with a total of 16 positive sentiments, 40 neutral sentiments and 71 negative sentiments. Accuracy value 67%, margin error 39%. Positive prediction precision 75%, neutral prediction precision 83% and negative prediction precision 88%..

Health risk stratification is crucial for preventive healthcare, yet existing models often rely on binary classification generalized disease prediction, neglecting personalized health indicators and graded risk levels. Many studies apply feature selection techniques like Relief and Univariate Selection without quantifying the weighted impact of features. To address these gaps, this study introduces a Big Data-driven Health Index (HI) framework using PySpark for scalable health risk stratification. The HI is computed as a weighted sum of health-related features using SHAP Analysis, XGBoost, Random Forest, and Correlation Analysis. PySpark enables efficient processing of large-scale health data, and individuals are classified into Low and High Risk. Optimal classification thresholds are determined using the Youden Index from the ROC curve to balance sensitivity and specificity. Personalized health recommendations are generated based on risk categories to guide preventive interventions. Performance evaluation reveals that Correlation Analysis achieves 100% precision and 98.90% recall, outperforming other methods. SHAP prioritizes recall but has low precision, while XGBoost and Random Forest improve precision but struggle with recall. By leveraging Big Data techniques with PySpark, this study enhances computational efficiency, scalability, and classification accuracy, addressing prior research limitations and providing a robust data-driven approach to personalized health monitoring.

Travel insurance provides financial protection for individuals during their trips, both domestically and internationally. With the increasing demand for travel insurance, insurance companies face challenges in efficiently managing claims. This study aims to develop a predictive model to classify whether an insurance policy will be claimed based on historical customer and transaction data. This research utilizes a dataset containing various features related to travel and policyholders, such as agent type, distribution channel, insurance product, travel duration, and premium amount. The methods used include data exploration, feature processing, and the application of machine learning algorithms such as Logistic Regression, Random Forest, and XGBoost. Experimental results indicate that the XGBoost model performs the best, achieving the highest accuracy compared to other models. With this predictive model, insurance companies can optimize claim evaluation processes, reduce fraud risks, and improve operational efficiency in handling travel insurance claims.

Customer churn is a major challenge in e-commerce, directly affecting revenue and profit. This study aims to develop a machine learning model using XGBoost to predict churn probability. To handle class imbalance, SMOTE was applied as a resampling method, and hyperparameter tuning was performed to enhance performance. The model was evaluated using the F2-score, prioritizing recall while maintaining precision. The results show that the XGBoost model with SMOTE achieves strong performance, with an F2-score of 0.849 on the tuned test data. This model can help businesses identify at-risk customers early, enabling proactive retention strategies.

This qualitative literature review explores the transformative role of generative artificial intelligence (GenAI) in reshaping organizational problem-solving. Moving beyond prediction, GenAI supports ideation, design, and decision-making by enhancing exploration, reducing cognitive constraints, and enabling hybrid human-machine intelligence. Drawing on recent studies in strategic management, organizational learning, and AI innovation, this review synthesizes evidence of GenAI’s capacity to augment creativity, frame redefinition, and solution diversity. The findings highlight both opportunities—such as improved search efficiency and strategic adaptability—and challenges, including algorithmic opacity, trust issues, and socio-technical complexity. Ultimately, GenAI represents a generative shift in how organizations define problems and pursue innovation, requiring thoughtful integration to maximize its cognitive and strategic value

Early detection and prediction of preeclampsia are crucial to prevent severe complications and ensure timely interventions, Specific ECG patterns, including PR segment, Q wave duration and amplitude, ST segment, U wave, and sinus rhythm were under study for their potential indicators of preeclampsia. This study aims to investigate the predictive role of these ECG patterns in preeclamptic pregnant women in the third trimester of pregnancy. Fourty (40) consenting pregnant women were recruited from St. Philomina Catholic Hospital, Edo State, Nigeria. These subjects consisted of  twenty (20) normotensive  and twenty (20) preeclamptic pregnant women in their  third trimester of pregnancy. After the subjects were  identified and recruited into the study, they were taken to the laboratory where their vital signs was taken and their ECG patterns recorded with ECG machine. Data obtained from this study were analysed using Graph Pad Prism 9. Results generated were expressed as mean ± SEM and a P-value of ≤ 0.05 were considered statistically significant. results from this present study show no significant differences were observed in the P-R segment, R-R interval, Q wave duration, Q wave amplitude The study underscores the multifactorial nature of cardiovascular changes in preeclampsia and highlights the potential of ECG parameters in aiding early detection, risk stratification, and management of the condition, despite  parameters showing no significant differences. However, PR Segment, Q Wave duration and amplitude, ST Segment , U wave and Sinus rhythm cannot be used to predict preeclampsia  

This study explores the application of machine learning algorithms, specifically Linear Regression and Decision Tree Regressor, for predicting student academic performance using academic grade data from Kaggle. The analyzed factors include attendance, assignment grades, midterm exam grades, and final exam grades. The research methodology encompasses data collection, preprocessing, model development, training, and validation. This study contributes to the field of educational data analytics by demonstrating how machine learning can provide actionable insights into students' learning patterns and academic outcomes. The findings emphasize the effectiveness of Linear Regression for linearly distributed data and Decision Tree Regressor for capturing complex, non-linear relationships. The implications of this research suggest that machine learning models can assist educators in identifying key factors influencing student performance, enabling targeted interventions to enhance learning outcomes. Future research should explore larger, more diverse datasets and incorporate ensemble methods, such as Random Forest or Gradient Boosting, to improve model generalization and prediction accuracy. Additionally, integrating socio-economic and psychological factors could provide a more holistic perspective on academic achievement.

Predicting credit worthiness is an important step for banks to reduce the risk of bad credit. This research compares the performance of four classification algorithms, namely SVM, Naïve Bayes, Random Forest and Decision Tree using simulated datasets. The results obtained on the metrics of accuracy, precision, recall, F1 score, and AUC-ROC, show that Decision Tree has the best performance with 42.5% accuracy, 48.3% precision, 47.5% recall, 47.5% F1 score, and AUC 0.60, indicating its ability to is in differentiating credit worthiness. Random Forest achieved an accuracy of 37.5% and an AUC of 0.493, while Naïve Bayes had the lowest accuracy with an accuracy of 27.5% and an AUC of 0.425. SVM gives better results than Naïve Bayes but is still inferior to Decision Tree. This research recommends implementing a Decision Tree as the main model with optimization through hyperparameter tuning, adding relevant features, and handling data accounting. These results are expected to support banking decision making more effectively and efficiently.

This study aims to analyze the application of Deep Neural Networks (DNN) as an artificial intelligence approach in processing weather data to support more accurate and stable climate predictions. Increasingly unpredictable and fluctuating weather patterns demand modern analytical methods capable of capturing non-linear relationships among atmospheric variables. DNN is utilized due to its ability to learn complex data structures through multilayer representations that extract deeper features from input variables. Weather data such as temperature, humidity, rainfall, air pressure, and wind speed are processed through several preprocessing stages to ensure optimal model performance. This research employs a descriptive qualitative method based on literature studies to examine the role of DNN in weather prediction systems. The findings indicate that DNN demonstrates strong generalization abilities, robustness to fluctuating data, and more stable predictive outputs compared to conventional statistical approaches. Thus, DNN is considered a promising component for the development of early warning systems and modern data-driven climate analysis, offering improved reliability in understanding and forecasting atmospheric conditions.