Publication Search

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.

The UNAMA Library employs information systems to enhance its academic services. Nevertheless, its administrative framework continues to encounter obstacles, such as inadequate service system support for users, constrained resources allocated for the management and upkeep of the system, an absence of standardized protocols for addressing technical challenges, and insufficient assessment system efficacy. If these issues remain unaddressed, the operational effectiveness of the library information system will be compromised, thereby thwarting the objective of delivering dependable information services. This investigation seeks to illuminate the maturity level of information system governance as delineated by COBIT 2019 within the Decision Support Systems (DSS) domain, specifically focusing on the DSS01 (Manage Operations) and DSS02 (Manage Service Requests and Incidents) processes. The findings suggest that the degree of information system governance capability the UNAMA Library is situated at the Established Process level (level 3), signifying that the process has undergone implementation; however, it has yet to be comprehensively documented and consistently evaluated. Moreover, a disparity persists between the existing state and the anticipated capability level of the organization, particularly concerning IT operations management, the standardization incident handling, and the documentation of operational procedures. An elucidation of the expected level is articulated, especially in terms of operational standards, incident documentation, and IT infrastructure oversight. Recommendations encompass the formulation of standard operating procedures (SOPs), the enhancement of documentation practices, and periodic assessments grounded in COBIT 2019. These findings are anticipated to assist libraries in augmenting the efficacy of information systems governance and the quality of IT services.

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.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

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.

The integration of Artificial Intelligence (AI) into educational settings, particularly in formative assessments, offers significant benefits in terms of personalized learning, real time feedback, and increased efficiency. However, the successful implementation of AI driven formative assessments depends not only on technological capabilities but also on socio cultural and organizational factors that shape its adoption. This study explores the socio technical factors influencing the use of AI in formative assessments, emphasizing the importance of considering cultural diversity, institutional culture, and educators' beliefs. AI technologies, while powerful in automating grading and providing personalized assessments, often face limitations in addressing complex student responses that require human judgment. Furthermore, cultural factors, such as students' prior exposure to technology and different cultural attitudes towards AI, play a critical role in the acceptance and effectiveness of these tools. Organizational factors, including leadership support, digital literacy, and the readiness of institutions to adopt AI, are also key determinants in the successful implementation of AI systems in education. Teachers’ beliefs about assessment influence their acceptance and use of AI tools, highlighting the need for professional development and training to ensure that AI enhances pedagogical goals rather than replacing human expertise. The study concludes that the alignment of technology, culture, and assessment beliefs is essential for the effective use of AI driven formative assessments in educational settings. Recommendations for educational institutions include adopting a socio technical approach to AI integration, with a focus on providing resources, training, and fostering a culture of innovation. Future research directions should focus on expanding studies to diverse educational contexts, conducting longitudinal research on AI’s impact on learning outcomes, and exploring additional socio technical frameworks to guide AI adoption in education.

This study explores the integration of critical pedagogy with digital literacy, aiming to reconceptualize digital literacy beyond its traditional focus on technical proficiency and operational skills. Traditional digital literacy models emphasize the ability to navigate and use technology, primarily focusing on operational tasks such as managing digital devices, utilizing software, and performing specific functions. However, these models often overlook the broader implications of technology on society, including its role in shaping power dynamics and perpetuating social inequalities. By integrating critical pedagogy, this study argues that digital literacy should not only encompass technical skills but also encourage learners to critically assess the social, political, and cultural implications of digital tools. The critical digital literacy approach promotes social justice, equity, and empowerment by encouraging learners to question, reflect on, and challenge the ways in which technology influences their lives and society at large. Through critical engagement with digital tools, learners are empowered to challenge existing power structures, fostering social transformation. The study discusses how educators can integrate critical digital literacy into teaching practices, moving beyond traditional skill based models by encouraging critical thinking, ethical use of technology, and a deeper understanding of the role digital technologies play in shaping societal structures. Moreover, the study highlights the transformative potential of critical digital pedagogy, proposing it as a tool for educational equity and social change. By reconceptualizing digital literacy through a critical pedagogical lens, this study offers a framework that empowers students to become not just proficient digital users but active participants in societal transformation, contributing to a more inclusive and just digital future. The study also suggests areas for future research, including empirical investigations on the practical implementation of critical digital literacy frameworks across various educational settings and contexts.

This study investigates the long-term impact of adaptive digital learning ecosystems on students' self-regulated learning (SRL) behaviors and academic persistence. Adaptive learning systems personalize the learning experience by adjusting content and feedback to meet individual students' needs, preferences, and performance. These systems enhance engagement, motivation, and learning outcomes through real-time adjustments and continuous feedback. The research aims to explore how adaptive learning systems influence SRL and academic persistence in university courses over time. Using a longitudinal quantitative design, the study tracks SRL behaviors and academic persistence at multiple points during the semester. Results show significant improvements in SRL behaviors such as goal setting, planning, self-monitoring, and reflection among students engaged with adaptive learning environments. These students exhibited greater autonomy, improved metacognitive awareness, and higher motivation. Additionally, students in adaptive systems demonstrated greater academic persistence, as indicated by more time spent on tasks, higher assignment completion rates, and sustained engagement. The findings suggest that adaptive learning platforms promote SRL and academic persistence by offering personalized, responsive learning experiences. Unlike static, non-adaptive environments, adaptive systems provide dynamic support, enhancing students' ability to regulate their learning and remain engaged despite challenges. The study concludes that adaptive learning systems are vital for long-term academic success, though further research is needed to assess the sustainability of these effects in various educational settings and among diverse student populations.

This study presents an ethical framework for learning analytics aimed at addressing key challenges related to the collection and use of student data in higher education. Learning analytics, a powerful tool for improving student outcomes and institutional decision-making, has raised ethical concerns regarding data privacy, transparency, fairness, and equity. The proposed framework integrates four core principles: data privacy, informed consent, transparency, and fairness, ensuring that institutions use learning analytics responsibly while safeguarding student rights. A central feature of the framework is its focus on promoting equitable decision-making, minimizing bias, and preventing the reinforcement of existing inequalities in algorithmic and data-driven decisions. The framework also emphasizes the importance of continuous ethical oversight, holding institutions accountable for ethical data use and adapting practices as technology evolves. The study concludes that the framework offers a comprehensive solution to the ethical challenges in learning analytics, providing institutions with a practical guide to embedding ethical principles in data practices. Additionally, the research discusses its potential to foster fairness, equity, and transparency in decision-making processes. Future research is recommended to refine the framework and explore its application across various educational contexts, ensuring responsible and inclusive use of learning analytics.

Creative writing plays an important role in developing students’ imagination, self- expression, and language competence in the digital era. However, many learners still struggle with generating ideas, maintaining motivation, and gaining confidence in writing. This study aims to analyze how the use of Wattpad, viewed through the Social Constructivist framework, can enhance students’ creative writing skills, motivation, and confidence. This research applies a qualitative descriptive approach supported by survey data from 30 high school students who actively use Wattpad. The results show that 95.7% of respondents have used Wattpad before, with most of them using it to read and learn from other writers. A total of 60.9% of respondents stated that reading other writers’ works was the most helpful for improving their writing, while 17.4% highlighted peer comments and feedback as additional support. Despite its benefits, 43.5% of students mentioned difficulty in organizing ideas as their main challenge. Open-ended responses revealed that positive feedback and community interaction on Wattpad helped increase students’ motivation and confidence in writing. The discussion emphasizes that the social interaction and feedback features of Wattpad foster collaborative learning, aligning with Vygotsky’s concepts of scaffolding and the Zone of Proximal Development. These findings suggest that integrating Wattpad into writing instruction can effectively enhance students’ creativity, critical thinking, and confidence in producing literary works.

The integration of information and communication technology (ICT) is essential for supporting student-centered learning and developing 21st-century skills. However, many primary school teachers still experience difficulties in implementing ICT meaningfully due to limited digital pedagogical competence and a lack of contextualized professional development, particularly in primary and Islamic school contexts. This study aims to explore the implementation and outcomes of an ICT-based teacher training programs conducted through a field study approach, focusing on its influence on teachers’ digital pedagogical competence and instructional practices. A qualitative descriptive field study design was employed. Participants were primary school teachers from an Islamic public school in Indonesia who took part in a six-session ICT training programs. Data were collected through observations, documentation of teachers’ digital products, and reflective field notes, and analyzed using thematic analysis. The findings reveal improvements in teachers’ pedagogical understanding of ICT, technical skills, and readiness for classroom implementation. Teachers produced various ICT-based learning media aligned with instructional objectives, including interactive quizzes, game-based activities, visual materials, animated videos, and digital classrooms. High levels of engagement and collaboration were observed despite infrastructural challenges. Field-based, practice-oriented ICT training effectively bridges technological skills and pedagogical application, supporting sustainable digital pedagogy in primary education.

This study aims to determine the effect of interactive learning media based on Math Playground on student learning outcomes on fraction materials in class IV SDN Kedung Dalem III. The problem studied is whether there is a significant influence between the use of interactive learning media based on Math Playground on student learning outcomes in grade IV SDN Kedung Dalem III. This study uses a quantitative method with a one group pretest-posttest design. The sample of this study were fourth grade students of SDN Kedung Dalem III, totaling 17 students. The research instrument used is a test (Pretest-Postest) of student learning outcomes on fraction material, which consists of 10 multiple choice questions. The results of the analysis using the wilcoxon test show that the sig value (2 tailed) is 0.000 which indicates significance <0.05 so that H0 is rejected and H1 is accepted. This shows that the interactive learning media Math Playground has a significant effect on improving student learning outcomes. The conclusion of this study is that Math Playground interactive learning media can improve student learning outcomes on fraction material. Therefore, teachers and educators can use Math Playground as an alternative learning media to improve student learning outcomes.

Quality schools are the goal of every educational institution, therefore it is important to pay attention to various aspects which are believed to influence school quality. This research was conducted with the aim of examining the influence of work commitment and teacher certification on school quality (State High School in Palembang City). The method used in this research is a quantitative method, with data obtained through questionnaires and documentation. The data obtained from the research process was then analyzed quantitatively through statistical computing (t test and F test). The results of the research conducted state that work commitment has a positive and significant effect on school quality; teacher certification has been proven to make a positive and significant contribution to school quality; In simultaneous testing it was found that work commitment and teacher certification affected the quality of State High Schools in Palembang City. The contribution of the two independent variables to the dependent variable is in the quite high category

This research aims to analyze the role of the Special Job Exchange (BKK) in increasing the absorption of Palembang City State Vocational School alumni in the world of work. BKK is one of the units that supports the labor absorption process by connecting the world of education and the world of industry. This research uses a qualitative approach with a case study method involving primary data through in-depth interviews with BKK, alumni and collaborating companies. The research results show that BKK has a significant role in facilitating Palembang City State Vocational School alumni in obtaining employment opportunities. Apart from that, BKK also provides training and information regarding job vacancies that are relevant to the skills possessed by alumni. However, several challenges are still faced, such as a lack of certain skills needed by companies and limited collaboration networks with the industrial world. This research recommends the need to improve the quality of training and expand the cooperation network between vocational schools and the industrial world to increase the effectiveness of BKK in accelerating workforce absorption.  

The epistemology of modern science develops with the assumption that neutrality and objectivity distinguish facts from values. This belief leads to the reduction of values, especially those related to ethical, metaphysical, and spiritual values, during the development of science. The purpose of this paper is to examine the problem of value reduction in the epistemology of modern science and to examine the Islamization of science as an epistemic alternative. This research method is qualitative, literature-based, and critical-philosophical. Epistemological critiques of value-free science, based on positivism and modern empiricism, and a study of the theory of the Islamization of science by Syed Muhammad Naquib al-Attas and Ismail Raji al-Faruqi are used to complete the analysis. The results of this study indicate that the epistemological separation between facts and values ​​in contemporary science leads to a decline in values. The Islamization of science offers an alternative epistemic framework that positions science as a meaningful, valuable, and goal-oriented activity. Thus, without rejecting the methodological progress of contemporary science, it remains a plausible option. Despite lingering problems with the methodological formulation, this article asserts that the Islamization of science is relevant as a theoretical response to the crisis of values ​​in the epistemology of modern science.

The study describes the implementation of the Christian Religious Education teaching module with the theme Allah Creates Man to Manage in grade VI students of SD Negeri 1 Kereng Bangkirai. The qualitative descriptive approach applied direct observation, documentation of activities, and assessment of learning outcomes in the affective, cognitive, and psychomotor domains to the 11 students who attended. The teaching module is aligned with the Learning Outcomes of Phase C of the Independent Curriculum providing a directed learning structure from Genesis 2:8-15. The opening activities include greetings, prayers, praise, Bible readings, as well as students' experiential questions about nature to build a relationship between the material and everyday reality. Core activities utilize explanations, group discussions, presentations, and clipping projects with the theme of loving God's created nature that increase active participation, cooperation, and creativity of students. Assessments showed good results in all three learning domains despite constraints such as the discomfort of cross-gender discussions and the introverted nature of students; The role of teachers in creating a safe atmosphere strengthens the effectiveness of the process. Reflection confirms meaningful learning that shapes an attitude of gratitude, environmental concern, and Christian responsibility.

This study aims to analyze and synthesize research findings related to the integration of Buddhist values in the development of interactive learning media. The research employed a qualitative approach using a systematic literature review method on fifty selected articles published between 2018 and 2025. Data were collected through a structured process of identification, screening, and extraction from primary sources consisting of accredited national journals and reputable international journals. The data were analyzed using thematic and comparative synthesis techniques to identify patterns, effectiveness, and research gaps. The results show that interactive learning media based on Buddhist values can enhance students’ motivation, moral understanding, and engagement through the use of technologies such as gamification, educational animation, augmented reality, and mobile applications. The effectiveness of these media is strongly influenced by the alignment between Buddhist ethical principles, instructional design, and the cultural context of learning. Furthermore, the study reveals that the successful integration of spiritual values into digital media depends on educators’ readiness, digital literacy, and technological infrastructure support.

This study aims to analyze the influence of self-discipline and burnout (Thīna-Middha) on active participation in religious activities among Buddhist adolescents aged 15–18 in Central Java. The approach used is quantitative with a survey method, and the sampling technique is cluster sampling. The instrument used is a semantic differential scale questionnaire to measure self-discipline, burnout, and active participation. Data analysis is performed using multiple linear regression to determine the influence of each variable on active participation. The results show that self-discipline has a positive and significant effect on active participation in Buddhist religious activities, while burnout has a negative and significant effect. These two variables contribute 41.1% to active participation in religious activities. These findings highlight the importance of self-discipline as a factor that drives active participation, as well as the negative impact of burnout on adolescents' involvement in religious activities. The implications of this study can be used as a basis for designing more effective and targeted programs for Buddhist adolescents, considering the factors of self-discipline and efforts to mitigate burnout. These programs need to be tailored to the spiritual and psychological needs of adolescents to enhance their active participation in religious activities.

This study aims to explore the phenomenon of fundraising conducted in the name of Buddhism and humanity in Kisaran City, North Sumatra, using a qualitative approach. Data was collected through direct observation, in-depth interviews with the participants, and document analysis related to the fundraising activities that had been conducted. The research findings show that fundraising is carried out in various forms, such as food donations, social activities, and online donations. All of these activities are rooted in Buddhist values, particularly dana (giving) and karuna (compassion). The use of digital technology, especially online donation platforms, has increased community participation and transparency in the fundraising process. However, the study also identified challenges such as the lack of accountability in managing the funds and the risk of misuse that could undermine public trust. In this context, transparency and active community involvement are essential to building trust and ensuring the effectiveness of religious philanthropy. The study concludes that the sustainability of Buddhist fundraising practices in modern society can be achieved by combining spiritual integrity, good governance, and appropriate technology adaptation. Through this holistic approach, Buddhism-based fundraising can remain relevant and effective in supporting humanitarian activities in the digital age.