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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.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

Anemia among adolescent girls is one of the health problems that is often overlooked, even though it has serious impacts on learning concentration, productivity, and reproductive health in the future. One of the main causes of anemia is iron deficiency and an unbalanced diet. This community service activity aims to increase adolescent girls’ knowledge about anemia prevention through health promotion using poster media with the approach of “Smart Nutrition for Iron Health.” The activity was carried out at Pesantren Al-Mubarak 12 November 2025, targeting 34 adolescent girls. The method included planning, preparation, implementation, and evaluation stages, with pre-test and post-test conducted to assess participants’ knowledge levels. The results showed a significant increase in adolescents’ knowledge regarding anemia prevention. Before the activity, 55% of respondents had poor knowledge, 35% moderate, and only 10% good. After the intervention, 75% of respondents had good knowledge and 25% moderate, with no respondents in the poor category. These results indicate that poster media is effective in increasing adolescents’ understanding of anemia and the importance of consuming a balanced diet rich in iron as an effort to prevent anemia among adolescent girls.

This study aims to examine and analyze the influence of auditor ethics, auditor competence, and audit risk on audit quality, with audit fees serving as a mediating variable. This research employs a quantitative associative approach with a casual explanatory design. Data were collected through a questionnaire survey administered to external auditors working at Public Accounting Firms (PAFs) in Surabaya, with a population of 55 firms. The sampling technique used was convenience sampling, resulting in 42 auditors as respondents with the characteristic of having worked at Public Accounting Firms in Surabaya for at least one year as of 2025. Data analysis was conducted using the Partial Least Square (PLS) method assisted by SmartPLS 4.0 software. The results indicate that auditor competence and audit risk have a positive and significant effect on audit fees, while auditor ethics does not have a significant effect. Furthermore, auditor ethics has a positive and significant effect on audit quality, whereas auditor competence, audit risk, and audit fees do not show a significant effect. In addition, audit fees are not proven to function as a mediating variable and therefore are unable to mediate the effects of auditor ethics, auditor competence, and audit risk on audit quality.

This study aims to examine the role of transformational leadership in enhancing innovative work behavior through organizational learning readiness and knowledge sharing behavior as mediating variables among employees of Brighton Real Estate Spazio Surabaya. Employing a quantitative explanatory research design, data were collected through questionnaires distributed to 98 employees of Brighton Real Estate Spazio Surabaya. Data were analyzed using the Smart-PLS 3.2 software for hypothesis testing. The findings indicate that transformational leadership has a positive and significant effect (β = 0.311) on organizational learning readiness, and a positive and significant effect (β = 0.333) on knowledge sharing behavior. Organizational learning readiness also positively and significantly affects knowledge sharing behavior (β = 0.359). However, transformational leadership shows a negative and insignificant effect (β = −0.033) on innovative work behavior, while organizational learning readiness exhibits a positive but insignificant effect (β = 0.167) on innovative work behavior. Interestingly, knowledge sharing behavior demonstrates a negative and significant effect (β = −0.514) on innovative work behavior.

The rapid development of smartphone technology today creates challenges for consumers and manufacturers in determining an objective price range based on highly varied technical specifications. This study aims to implement the Random Forest algorithm in classifying smartphone price ranges into four main categories, namely low, mid-range, high, and flagship. The research method was carried out systematically through the stages of loading a dataset of 2,000 entries, exploratory data analysis (EDA) to ensure data integrity, and model training with a training and testing data split of 80:20. The results showed that the Random Forest model achieved a significant overall accuracy rate of 89%. Based on feature importance analysis, it was found that RAM capacity was the most dominant determining factor, contributing 47% to prediction accuracy, followed by battery power and screen resolution as supporting features. These findings have strategic implications for manufacturers to prioritize memory capacity upgrades in determining product pricing in the market, as well as providing guidance for consumers in assessing the fairness of a device's price based on its technical capabilities.

The rapid advancement of digital technologies and Artificial Intelligence (AI) has fundamentally reshaped the management and development of the tourism industry. Digital transformation strategies offer substantial opportunities to enhance destination competitiveness while simultaneously supporting economic, social, and environmental sustainability. This study aims to systematically examine the role of digital transformation and AI in strengthening sustainable tourism competitiveness through a literature review approach. A total of 42 peer-reviewed journal articles published between 2019 and 2025 were analyzed, drawing from Scopus, Web of Science, and Google Scholar. The analysis employed thematic synthesis to identify dominant patterns, conceptual relationships, and emerging themes across the literature. The findings indicate that AI-driven digital transformation enhances operational efficiency, enables personalized tourist experiences, supports data-informed resource management, and facilitates the development of smart tourism destinations. Nevertheless, persistent challenges related to human resource readiness, digital inequality, data governance, and ethical considerations remain evident. This review provides an integrated conceptual perspective on digital transformation and AI in sustainable tourism competitiveness and offers insights for policymakers, practitioners, and future research.

Background: Sleep disorders in adolescents are a significant health problem, with a global prevalence reaching 57.8% and particularly high rates in several cities in Indonesia. Poor sleep quality negatively impacts physical health, such as the risk of cardiovascular disease and anemia, as well as mental and cognitive health. Sleep behavior is influenced by a dynamic interaction between personal and environmental factors, as explained in Social Cognitive Theory (SCT). Research Objective: To identify the determinants of sleep deprivation in adolescents, specifically individual and environmental factors, based on a Social Cognitive Theory (SCT) perspective through a literature review from 2019 to 2025. Method: This study utilized a literature review. To obtain research data, the authors searched for scientific articles through Google Scholar, PubMed, and ScienceDirect databases, then analyzed 10 articles that met the inclusion criteria, published between 2020 and 2025. Results: Factors significantly associated with adolescent sleep quality were identified, with individual factors being the most dominant determinant (found in 7 studies), including academic stress and smartphone addiction. Furthermore, a positive association was found with environmental factors (found in 4 studies), such as bright lighting, noise, and uncomfortable room temperature. Conclusion: Within the framework of Social Cognitive Theory, adolescent sleep quality is the result of a reciprocal interaction between personal factors (perceived stress and self-control over gadgets), the physical environment, and sleep behavior. Individual factors such as stress and nighttime gadget use reduce self-efficacy for regular sleep, which is exacerbated by an unfavorable environment.

The development of information technology has driven digital transformation in various sectors, including the food and beverage (F&B) industry. However, many small to medium-scale F&B businesses still rely on manual ordering systems, resulting in long queues, order recording errors, limited menu information, and suboptimal user experience. This study aims to design the user interface (UI) and user experience (UX) of a web-based Smart Ordering System that provides convenience, efficiency, and comfort in the food ordering process. The research method used is the Design Thinking approach, which includes empathize, define, ideate, prototype, and testing stages. The design process involves user needs analysis, user flow development, wireframe creation, and high-fidelity prototype development using Figma. Usability testing is conducted using the Single Ease Question (SEQ) method to evaluate ease of use and user satisfaction. The results indicate that the proposed UI/UX design provides a clear ordering flow, intuitive interface, and easy-to-understand user experience. Based on the SEQ results, most users experienced no difficulty in using the system, indicating that the design meets usability criteria with a very good category and is suitable for implementation in the F&B industry.

The implementation of the One-Stop Integrated Service (PTSP) Website at the Ministry of Religious Affairs of Jambi City is part of the digital transformation of public services. This study aims to measure user satisfaction with the PTSP Website using the End User Computing Satisfaction (EUCS) method and the DeLone & McLean model. This research employed a quantitative approach using a survey method involving 100 respondents who are users of the PTSP Website. The EUCS variables consist of content, accuracy, format, ease of use, and timeliness, while the DeLone & McLean model includes system quality, information quality, and service quality. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0 software. The results indicate that system quality, information quality, and service quality have a positive effect on user satisfaction with the PTSP Website. This study is expected to serve as an evaluation reference for improving the quality of digital public services.

This study aims to analyze the influence of promotion, shopping convenience, advertising, recommendation, comment, and credibility factors on purchase intensity in the TikTok Shop platform among Indonesian households. The rapid development of social commerce services requires a deep understanding of factors driving online purchasing decisions, especially among families as primary users. A quantitative approach was employed, utilizing a Likert-scale questionnaire distributed online. The sample consisted of 150 active TikTok Shop users from various household backgrounds. Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS was used to analyze the direct effects of each factor on purchase intensity. The results indicate that promotion, shopping convenience, and credibility significantly and positively influence purchase intensity, while advertising, recommendation, and comment have relatively weaker yet still relevant effects. These findings underscore the importance of effective promotional strategies, ease-of-shopping enhancements, and building platform credibility to boost purchase intensity within the household consumer segment. The practical implications can guide e-commerce practitioners and digital marketers in formulating adaptive marketing strategies in the era of social commerce.

In the contemporary digital marketplace, consumers increasingly face diverse product choices and brand communications. Understanding the mechanisms through which product quality and brand perception influence customer loyalty remains critical for competitive advantage. The mediating role of customer trust in this relationship has received limited empirical attention within Indonesian market contexts. This research analyzes the direct and indirect effects of product quality and brand perception on customer loyalty, with customer trust as a mediating variable, using Partial Least Squares Structural Equation Modeling (PLS-SEM) methodology on 103 respondents. A quantitative cross-sectional survey design was employed, collecting data via Likert-scale questionnaires (1-5) with 15 measurement items across four latent constructs: Product Quality (5 items), Brand Perception (4 items), Customer Trust (3 items), and Customer Loyalty (3 items). Data analysis utilized PLS-SEM via SmartPLS 3.0, including assessment of measurement model validity (outer model), structural relationships (inner model), and mediation effects through bootstrapping (5000 iterations). The outer model demonstrated adequate validity with 12 of 15 indicators loading above 0.7, and all constructs meeting composite reliability (CR > 0.7) and average variance extracted (AVE > 0.5) criteria. The inner model revealed that product quality significantly influenced customer trust (β = 0.624, p < 0.001), while brand perception showed no significant direct effect (β = 0.045, p = 0.767). Customer trust strongly predicted loyalty (β = 0.650, p < 0.001). Product quality demonstrated a significant indirect effect on loyalty through trust (β = 0.405, p < 0.001), indicating full mediation. The model explained 43.5% of trust variance and 42.2% of loyalty variance. Product quality emerged as the dominant antecedent of customer trust and loyalty, while brand perception did not significantly contribute. Trust served as the critical mechanism translating quality into loyalty. These findings suggest that companies should prioritize quality assurance and consistent delivery over brand marketing campaigns for sustainable loyalty development. The research contributes to mediation theory in consumer behavior and provides actionable strategic guidance for practitioners in emerging markets.

This study aims to compare the effectiveness of QRIS with other cashless payment methods in driving the development of the digital economy in Indonesia. The background to this study stems from the increasing use of digital transactions and the strategic role of QRIS as a national standard for QR-based payments. Google Forms was used to disseminate the survey online and successfully collected 100 respondents who were users of cashless digital payment services. A purposive sampling technique was used to ensure that respondents had experience using QRIS and other digital payment methods. Data were examined using the SmartPLS 4 program using a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach to test the relationships between variables. The results indicate that perceived ease of use, transaction security, and influence on digital behavior have a positive or significant influence on QRIS effectiveness. However, availability and accessibility variables did not have a significant effect. This finding indicates that QRIS effectiveness is more influenced by user experience and perceptions, rather than availability or ease of access. This research is expected to contribute to the development of strategies to increase digital payment adoption in Indonesia.

The rapid development of artificial intelligence, particularly ChatGPT, has created new opportunities to support students’ academic activities in higher education. However, its utilization needs to be evaluated in terms of the alignment between academic task characteristics and technological capabilities to ensure optimal outcomes. This study aims to examine the feasibility of using ChatGPT in students’ academic activities by applying the Task–Technology Fit (TTF) model. This research employed a quantitative approach using Structural Equation Modeling based on Partial Least Squares (SEM-PLS). Data were collected through questionnaires distributed to university students and analyzed using SmartPLS 4 software. The variables examined included Task Characteristics, Technology Characteristics, Task–Technology Fit, Performance Impact, and Utilization. The results indicate that Task Characteristics and Technology Characteristics have a positive and significant effect on Task–Technology Fit. Furthermore, Task–Technology Fit significantly influences Performance Impact and Utilization. Performance Impact also shows a positive and significant effect on the utilization of ChatGPT by students. These findings suggest that the alignment between academic task requirements and the capabilities of ChatGPT plays a crucial role in improving students’ performance and encouraging sustained technology use. The implications of this study highlight the importance of selective and purposeful use of ChatGPT in higher education and provide a reference for higher education institutions in formulating policies related to the ethical and effective integration of artificial intelligence technologies as learning support tools.

The implementation of the Electronic-Based Government System (SPBE) is essential for achieving efficient, transparent, and accountable village governance. Sido Rukun Village in Merangin Regency, Jambi Province, has begun using several government applications but lacks a structured enterprise architecture aligned with the national SPBE framework. This study aims to develop an enterprise architecture for SPBE in the business process domain at Sido Rukun Village. The research employs the TOGAF ADM (The Open Group Architecture Framework – Architecture Development Method) approach, involving stages such as identifying current business processes, designing a target architecture, and conducting a gap analysis between the as-is and to-be states. The findings include a business process architecture blueprint compliant with Presidential Regulation No. 95 of 2018 and Presidential Regulation No. 132 of 2022 on the National SPBE Architecture. This blueprint encompasses BPMN-based business process models and supporting artifacts that serve as a foundation for integrated information systems at the village level. The study’s implications are significant: it provides Sido Rukun Village with a practical and standardized technical blueprint for implementing a sustainable electronic-based government system, thereby supporting its transformation toward a Smart Village capable of adapting to evolving information and communication technology trends.

The use of the VSCO application continues to face technical issues, including errors during editing, limited feature access, and login problems that affect user satisfaction. This study analyzes user satisfaction with the VSCO application using the End User Computing Satisfaction (EUCS) method. The study involved 385 VSCO users as respondents, with data collected through questionnaires and analyzed using SmartPLS 3.0. In this research, Accuracy variable does not affect user satisfaction, whereas the Content, Format, Ease of Use, and Timeliness variables have a significant effect on user satisfaction. The study shows that content quality, interface design, ease of use, and system timeliness are the main factors influencing user satisfaction with the VSCO application.

This study aims to investigate the impact of social media advertising on clothing choices at Universitas Dinamika Bangsa Jambi students. In today's world, where many people, especially young people who frequently shop online, often struggle to accurately determine the quality of items. A quantitative approach was employed, with a survey as the primary method of data collection. A questionnaire was distributed online via Google Forms and successfully elicited responses from 102 active students who are also social media users. The sampling technique used was purposive sampling, with participants selected based on criteria that matched the focus of the study. The data were then processed using SmartPLS 4 software with the Partial Least Squares Structural Equation Modeling (PLS-SEM) method to test the relationship between variables. The main findings indicate that social media promotions have a strong positive influence on students' clothing purchasing decisions. This underscores the crucial role of targeted advertising strategies in the digital world in shaping consumer preferences. This research is expected to serve as a guide for clothing entrepreneurs in developing online marketing plans that better suit the tastes and needs of students as their target market.

Sudden Infant Death Syndrome (SIDS) is a sudden and unexpected death in infants that is often associated with the prone sleeping position. This study aims to develop an automated monitoring system capable of detecting SIDS risk factors using the YOLOv8 algorithm and to analyze the effect of data augmentation on model performance. The dataset consists of two classes, baby-lying-on-back (supine) and baby-lying-on-stomach (prone), which were processed through model training and evaluation using precision, recall, F1-score, and mAP metrics. The model was trained under two scenarios, without data augmentation and with data augmentation. The results show that the model without augmentation achieved a precision of 90%, recall of 85%, F1-score of 86%, and mAP50 of 93.7%. After applying augmentation, performance improved to a precision of 90%, recall of 87%, F1-score of 88%, and mAP50 of 95.1%. These findings indicate that augmentation increases detection accuracy and enhances model generalization, including robustness against variations in lighting and camera angles. Furthermore, testing with image and video inputs revealed that the non-augmented model exhibited a tendency toward overfitting, particularly in favor of the baby-lying-on-stomach, whereas the augmented model successfully classified both classes accurately. The developed system is also equipped with an alarm feature and early-warning notifications via Telegram to smartphone when a prone position is detected for a certain duration. Overall, the results demonstrate that YOLOv8 with data augmentation is effective for an automated, non-invasive monitoring system for infants, making it suitable for detecting and preventing potential SIDS risk factors.