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Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.

This study aims to analyze and project consumer prices of cabbage commodities at four levels: Ngawi Regency, Pacitan Regency, East Java Province, and nationally, using the additive Holt–Winters forecasting model. Monthly price data for the period January 2020–December 2024 were used to capture the dynamics of levels, trends, and seasonal patterns that affect price fluctuations. Model performance was evaluated using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) indicators. The results showed differences in model accuracy between regions. East Java Province produced the best performance with the lowest MAE and RMSE values, indicating a more stable price pattern that was easier for the model to capture. In contrast, Ngawi Regency showed the highest volatility, resulting in greater forecasting errors. Pacitan Regency displayed a relatively consistent seasonal pattern with moderate accuracy, while national data showed smoother fluctuations due to the aggregation effect. Overall, the additive Holt–Winters model is effective for short-term projections in regions with low to moderate variability, but is less optimal in regions with highly volatile price dynamics.

Information system security faces serious challenges due to increasingly complex cyber attacks. Intrusion Detection Systems (IDS) require efficient approaches to handle high-dimensional data such as the NSL-KDD dataset with 41 features. This study aims to implement the Genetic Algorithm (GA) for feature selection on the NSL-KDD dataset to improve the efficiency and accuracy of network attack detection. The method used is computational experimental research, involving data preprocessing, GA implementation for feature selection, building a classification model using Random Forest, and performance evaluation based on accuracy, precision, recall, F1-score, and computation time. The results show that GA successfully reduced features from 41 to 12 features (70.7% reduction), significantly improving computational efficiency. However, model accuracy slightly decreased from 0.4973 to 0.4951, indicating that while GA is effective for feature selection, the elimination of certain features may reduce classification capability. The implication of this study is that GA can be used as a tool to simplify intrusion detection models, but it should be combined with parameter optimization and data imbalance handling to achieve more optimal performance.  

Effective management of fixed assets plays a crucial role in maintaining the reliability and transparency of a company’s financial reporting. Errors in the capitalization process can lead to misstatements in financial statements and affect investment decisions. This study aims to analyze and forecast asset capitalization trends using the Autoregressive Integrated Moving Average (ARIMA) model. The research utilizes monthly recap data of asset capitalization recorded during the Settlement to Fixed Asset process from January 2021 to August 2025. The data were processed through several stages, including stationarity testing, model identification, parameter estimation, and model accuracy evaluation. The findings indicate that the data are stationary without differencing (d = 0). From several candidate models, ARIMA(0,0,3) was identified as the best model based on the lowest AIC value of 39.76. The selected model was then applied to predict asset capitalization values for the next ten periods, resulting in forecasts ranging from 1.12 to 1.56 trillion rupiah. Model evaluation showed a MAPE of 29.01%, which implies a moderate forecasting accuracy. Consequently, the ARIMA model can be considered a suitable analytical tool for monitoring and forecasting asset capitalization quantitatively.

This study aims to examine the influence of transformational leadership, work motivation, and organizational culture on organizational commitment at PT Kembang Jawa Permai. The research employs a quantitative approach with an associative causal design, using a survey method and data collected through a questionnaire distributed to all 46 employees as respondents. The sampling technique used is saturated sampling, considering the small population size. Data analysis was performed using multiple linear regression analysis with validity, reliability, and classical assumption tests (normality, multicollinearity, heteroscedasticity, and linearity) conducted beforehand to ensure model accuracy. The results indicate that transformational leadership, work motivation, and organizational culture each have a positive and significant effect on organizational commitment, both partially and simultaneously. Among the three variables, organizational culture shows the most dominant influence, indicating that strong organizational values and teamwork orientation play a key role in strengthening employee commitment. The coefficient of determination (R²) of 0.848 implies that 84.8% of the variation in organizational commitment can be explained by the three independent variables. This study contributes to human resource management theory and provides practical insights for organizations to enhance employee commitment through effective leadership, motivation, and cultural reinforcement.

Human Resource Management (HRM) plays a strategic role in improving organizational competitiveness through proper management of employee placement, training, and performance evaluation. To support the achievement of these goals, a predictive model is needed that can provide an accurate picture of employee performance. This study utilizes a Human Resource Management (HRM) dataset of 1,200 data and applies several classification algorithms to compare their effectiveness, namely J48 or C4.5, Random Forest, Naive Bayes, K-Nearest Neighbor (KNN), Logistic Regression, and Support Vector Machine (SVM). To obtain more optimal results, this study uses resampling techniques and attribute selection methods with a correlation attribute eval approach, so that class distribution can be more balanced and model accuracy increases. From the test results, the Decision Tree J48 algorithm showed the best performance with an accuracy level reaching 95.41%, a kappa value of 0.8925, a mean absolute error (MAE) of 0.0432, a precision of 0.955, a recall of 0.954, and an area under the ROC curve of 0.964. These findings indicate that J48 has excellent predictive capabilities compared to other algorithms. Furthermore, this study also found that the most influential variables in determining employee performance include the percentage of the last salary increase (EmpLast Salary Hike Percent), the level of work environment satisfaction (Emp Environment Satisfaction), the length of time since the last promotion (Years Since Last Promotion), and experience in the current role (Experience Years in Current Role). Overall, the results of the study indicate that the C4.5 algorithm with the application of the resampling technique can be an optimal solution in building an employee performance prediction system. Thus, this model has the potential to be a strong basis for managerial decision-making, particularly in designing HR development strategies and policies to improve organizational performance.

This study explores the integration of blockchain technology with federated learning (FL) to enhance cross-organizational healthcare analytics while ensuring privacy and data security. Federated learning allows multiple institutions to collaboratively train machine learning models without sharing sensitive patient data. Instead, local data is used to train models, and only model parameters are exchanged. However, privacy concerns and data sharing inefficiencies have hindered broader healthcare collaboration. Blockchain, a decentralized ledger technology, addresses these concerns by ensuring data integrity and transparency, providing an immutable and tamper-proof record of all transactions. This study investigates how the combination of blockchain and federated learning can overcome these challenges, facilitating secure and efficient data sharing between healthcare institutions. The study uses synthetic multi-institution healthcare datasets to simulate real-world collaboration scenarios. The blockchain-enabled federated learning system ensures that no raw patient data is shared, significantly reducing the risk of privacy breaches while still allowing healthcare institutions to collaborate on predictive model development. The results show that while there is a slight decrease in model accuracy compared to centralized methods, the trade-off is outweighed by the privacy and security benefits. Blockchain’s integration ensures that model updates are transparent, enhancing trust between institutions and reducing concerns about data integrity. Moreover, the use of blockchain’s smart contracts automates and enforces compliance, further streamlining collaboration. This research contributes to the field by demonstrating how blockchain-integrated federated learning can create a secure, scalable, and privacy-preserving framework for collaborative healthcare analytics. The findings underscore the potential for this approach to enhance healthcare outcomes and improve decision-making across institutions while ensuring patient data protection.

This study explores the development and application of a digital twin-driven cybersecurity risk assessment model for Industrial Internet of Things (IIoT) networks. The increasing complexity and interconnectivity of IIoT systems have expanded the attack surface, making them vulnerable to a wide range of cyber threats. The digital twin model addresses this challenge by creating real-time virtual replicas of physical systems, which can simulate and predict network vulnerabilities and attack vectors. The model uses machine learning algorithms and real-time data to simulate cyberattacks, including Distributed Denial of Service (DDoS), malware, and data breaches. By providing continuous monitoring and dynamic risk predictions, the digital twin model enhances the resilience of IIoT networks compared to traditional cybersecurity frameworks. The findings indicate that the model's ability to predict potential cyber threats and simulate various attack scenarios provides a more proactive and accurate approach to cybersecurity in IIoT environments. Additionally, the study highlights key mitigation strategies, including adaptive security mechanisms, real-time anomaly detection, and the use of lightweight encryption for resource-constrained devices. Despite its effectiveness, challenges such as computational requirements, integration with legacy systems, and scalability were identified. This research underscores the strategic importance of digital twin models in securing IIoT systems and advancing Manufacturing 4.0 ecosystems. Future research should focus on enhancing model accuracy, expanding its application to diverse industrial sectors, and improving interoperability with legacy systems to further strengthen the security posture of IIoT networks.

Fraud in digital transactions has become a serious issue threatening the security and integrity of the fintech and e-commerce sectors. To address this problem, machine learning technology has emerged as an effective solution for automatically detecting anomalies and fraudulent transactions. This study aims to analyze the application of machine learning algorithms, specifically Support Vector Machine (SVM), Random Forest, and Ensemble Learning, in detecting fraud in digital transactions. The research adopts a quantitative approach with experimentation, testing the effectiveness of the three algorithms using a digital transaction dataset consisting of both fraudulent and non-fraudulent transactions. The results show that the Random Forest algorithm performs the best in terms of accuracy and recall, followed by Ensemble Learning, which enhances fraud detection performance by combining multiple prediction models. Meanwhile, SVM demonstrates satisfactory performance but is prone to overfitting issues when handling large and complex datasets. The study also finds that the problem of imbalanced data can affect model accuracy, and data balancing techniques such as oversampling are required to improve fraud detection performance. Overall, the findings suggest that machine learning, particularly Random Forest and Ensemble Learning algorithms, can be relied upon to improve fraud detection in digital transactions. However, challenges such as model interpretability and the need for periodic algorithm updates still need to be addressed to enhance the effectiveness of fraud prevention systems in countering the ever-evolving nature of fraud.