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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.

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.

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.  

Industrialization is an economic development process characterized by the growth of the industrial sector as a key driver of economic progress in Indonesia. Beyond its role in creating added value and enhancing national competitiveness, industrialization is closely interconnected with other strategic sectors, including agriculture, services, infrastructure, and employment. Strong linkages between industry and these sectors are essential to ensure that economic growth is inclusive and sustainable. This study aims to analyze industrialization strategies that integrate and strengthen relationships with other sectors in the development process. The research employs a literature review method using a qualitative descriptive approach. The data are derived from secondary sources, including scientific journals, research articles, policy reports, and official publications relevant to industrial and sectoral development. The findings indicate that industrialization strategies aligned with agriculture can increase productivity and value-added processing, while linkages with the service sector and infrastructure development can improve efficiency, distribution networks, and market access. Furthermore, integrated industrialization contributes significantly to employment creation and regional development, reducing economic disparities between areas. Therefore, the formulation of an industrialization strategy that is well-coordinated with other sectors is crucial to achieving balanced economic growth, strengthening structural transformation, and supporting sustainable development in Indonesia.

The development of digital technology has driven significant changes in people's transaction patterns, including the increased use of app-based financial services. This study aims to analyze the role of the DANA app in increasing the convenience of cashless transactions in Indonesia. The method used was a literature review, analyzing journals, reports, and official documents related to the use of digital wallets and the development of DANA services over the past three years. The results show that DANA contributes to accelerating transaction processes, increasing accessibility to financial services, and providing secure and efficient payment features. Features such as QRIS, flexible balance top-ups, instant transfers, and integration with various public and commercial services have proven to facilitate users' cashless transactions. However, challenges remain, including unequal digital literacy, data security risks, and network limitations in some regions. Overall, the DANA app plays a crucial role in accelerating the digital payment ecosystem and increasing transaction convenience for the Indonesian people.

The integration of artificial intelligence (AI) in forensic investigation has significantly transformed the analysis and authentication of digital evidence. This paper explores the role of AI technologies, specifically machine learning and deep learning algorithms, in examining digital evidence from various sources, including computers, mobile devices, and network systems. We provide an in-depth analysis of current AI-based forensic tools, their efficiency in evidence authentication, and the challenges they face regarding legal admissibility. Our findings indicate that AI-powered forensic systems can detect digital evidence tampering with 94.7% accuracy, drastically reducing analysis time from weeks to hours. However, challenges remain, particularly in areas such as algorithmic transparency, bias prevention, and ensuring the integrity of the chain of custody. This research offers a framework for incorporating AI in forensic laboratories, while also addressing crucial legal and ethical concerns to ensure the admissibility of AI-analyzed evidence in court. These considerations are essential for the widespread acceptance and use of AI in forensic investigations.

This study investigates the influence of financial literacy, entrepreneurship training, and financial inclusion on the performance of micro, small, and medium enterprises (MSMEs) through business sustainability. Using a systematic literature review (SLR) examines the impact of financial literacy, entrepreneurship training, and financial inclusion on MSME performance through business sustainability mediation by synthesizing empirical data from 12 research (2020–2025) across Scopus and Web of Science. Positive direct effects on sustainability (financial literacy via budgeting/risk management; training via adaptive resilience; inclusiveness via digital access) and performance metrics like profitability/growth are confirmed by results using the PRISMA 2020 flow.  Amid obstacles like financial access restrictions and COVID-19 disruptions, business sustainability appears as a crucial mediator, linking these factors to improved MSME results in developing contexts (Africa, Indonesia). Practical implications compel policymakers to give integrated literacy programs, contextual training, and inclusive finance top priority. Theoretical contributions combine financial literacy, entrepreneurial learning, and sustainability ideas into a holistic mediation model. The results highlight the importance of integrating financial education, entrepreneurial skill development, and inclusive financial systems to strengthen MSME resilience and competitiveness. This study provides practical implications for policymakers, financial institutions, and support organisations in designing effective interventions that foster sustainable business growth. The research also contributes theoretically by confirming the mediating role of business sustainability in the relationship between financial literacy, entrepreneurship training, financial inclusion, and MSME performance. Future studies may expand these insights by examining additional contextual factors such as digital technology adoption and business networking that further support sustainable MSME development.

This study aims to examine the influence of product innovation, digital marketing, and business networking on the competitiveness of small and medium-sized enterprises (SMEs), with customer engagement positioned as a mediating variable. Employing a Systematic Literature Review (SLR) approach, thirty Scopus-indexed articles published between 2020 and 2025 were analysed to synthesise theoretical and empirical insights related to SME competitiveness in contemporary digital and urban business environments. The findings indicate that product innovation, digital marketing, and business networking each play a significant role in strengthening SME competitiveness, particularly within markets characterised by rapid technological change. Customer engagement emerges as a critical mediating mechanism that connects these strategic variables to sustainable competitive advantage. It enhances the impact of innovative and digital strategies by fostering stronger emotional, behavioural, and participative interactions between SMEs and their customers. The review also highlights that SMEs adopting integrated digital management practices, such as the utilisation of human-resource information systems (HRIS) and data-driven decision-making tend to demonstrate greater adaptability, market responsiveness, and long-term performance. The study contributes theoretically by integrating resource-based and dynamic capability perspectives, offering a holistic understanding of how digital and relational capabilities interact to elevate competitiveness. Practically, the findings provide strategic guidance for policymakers, SME managers, and practitioners in designing innovation-oriented and digitally enabled initiatives that support sustainable SME growth in the digital era.

This community service activity aims to improve digital literacy among children and adolescents in Telang Sari Village through basic computer training and networking introduction. The main problem faced by the community is the low level of technological literacy caused by limited facilities and lack of access to structured computer education. This program was implemented during the Thematic Community Service Program (KKNT) by applying participatory and practical training methods. The activities included introduction to computer hardware and software, basic typing skills, document creation, internet literacy, and simple LAN configuration practices for junior high school students. The results show a significant improvement in participants’ understanding and confidence in using computers and basic networking concepts. Children and adolescents who previously had minimal exposure to technology were able to operate computers, create simple documents, and understand responsible internet usage. The program also contributed to increasing students’ motivation to learn technology and preparing them to face digital challenges. Overall, this activity demonstrates that basic computer training with hands-on practice can effectively enhance digital literacy in rural communities.

Participatory village development planning is often treated as a technical-administrative routine. In practice, however, planning is inseparable from political dynamics that shape who participates, whose interests prevail, and how scarce resources are allocated. This community empowerment program (PKM) in Kedung Udi Village, Trawas District, Mojokerto Regency, East Java, aimed to strengthen village governance by enhancing the capacity of village officials and community representatives to design and facilitate participatory planning while explicitly addressing the political dimension of planning. The main intervention was a workshop conducted on 22 August 2025, preceded by coordination and situational observation. Workshop modules emphasized: (1) planning as a political decision; (2) navigating dual arenas: formal (Musdes/Musrenbang and RPJMDes, RKPDes, APBDes) and informal (elite networks and gatekeeping); (3) multi-level contestation and policy alignment; and (4) practical tools, including power–interest mapping, programmatic agreements, program tagging for alignment with district planning documents, and transparency/anti elite capture mechanisms. The program resulted in improved participant literacy regarding power relations in planning and produced a follow-up action plan oriented toward institutional advocacy, continuous social control, and routine capacity reinforcement through a university and village partnership.

The increasing volume of online hotel reviews provides valuable insights into customer perceptions but poses challenges for manual analysis due to its unstructured nature. This study aims to compare the performance of Recurrent Neural Network (RNN) and Bidirectional Encoder Representations from Transformers (BERT) in hotel review sentiment analysis. A total of 20,491 TripAdvisor hotel reviews were classified into three sentiment categories: negative, neutral, and positive. The research methodology includes text preprocessing, stratified data splitting, class imbalance handling using Random Over-Sampling, tokenization, and supervised model training. Model performance was evaluated using a confusion matrix and classification metrics. The results indicate that BERT outperforms RNN, achieving an accuracy of 80.54%, while RNN reached 62.21%. BERT demonstrated superior capability in capturing contextual and semantic information in hotel reviews. These findings suggest that transformer-based models are more effective for sentiment analysis of complex textual data in the hospitality domain and can support data-driven service improvement strategies.    

ARP spoofing attacks are a serious threat to network security, particularly in vulnerable Internet of Things (IoT) environments. This final project aims to detect ARP spoofing attacks on IoT net-works using a combination of Random Forest (RF) and Robust PCA methods. RF is chosen for its classification capabilities and handling of non-linear data, while Robust PCA is used for di-mensionality reduction and handling outliers in the data. The dataset used is "MITMArpSpoof-ing.pcap.csv," which contains network traffic data. The data is processed by performing prepro-cessing, feature scaling, and converting labels to binary (0 for benign, 1 for ARP spoofing). Subsequently, Robust PCA is applied to reduce data dimensions, and then the data is trained using the RF model. The test results show that the RF model with Robust PCA achieves an accu-racy of 96.02% in detecting ARP spoofing attacks. This method has proven effective in identify-ing and classifying ARP spoofing attacks on IoT networks.

Student dropout remains a significant challenge for higher education institutions as it impacts academic quality, educational management efficiency, and students' success in completing their studies. Therefore, an approach that can identify students at risk of dropping out is necessary so that timely academic interventions can be made. This study aims to develop a dropout detection model using an Artificial Neural Network (ANN). The data used come from a publicly available higher education dataset, ensuring research reproducibility. Data preprocessing steps were carried out to improve data quality before modeling, and the Synthetic Minority Over-Sampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) was applied to address class imbalance issues. The ANN model's performance was evaluated using accuracy, precision, recall, F1-score, and area under the ROC curve (ROC-AUC). The test results show that the ANN model can provide excellent predictive performance in detecting at-risk students. The application of SMOTE-ENN also proved to enhance the model’s sensitivity toward the minority class, as indicated by improvements in recall and F1-score. These findings indicate that the developed ANN model has the potential to be used as a student dropout detection system to support data-driven decision-making and strategy development within higher education institutions.

The massive adoption of Internet of Things (IoT) devices is expanding the cyberattacks surface, particularly by the Mirai botnet, which exploits the dynamic characteristics of data traffic. This research proposes a Mirai detection approach based on a Recurrent Neural Network (RNN) optimized using Bayesian Optimization to improve prediction accuracy on sequential data. Unlike previous studies, this research utilizes the latest CIC IoT-DIAD 2024 dataset and applies probabilistic optimization to the hyperparameter space, including RNN units, dropout, and learning rate. The experiment was conducted on 201,021 valid data points, with dimensionality reduction using PCA as the optimal point to represent essential features without redundancy. The results show a significant increase in accuracy from 97.95% to 99.69%, accompanied by an 84% decrease in False Negatives, an 86% decrease in False Positives, and an AUC value of 0.9999. These findings confirm that integrating RNN and Bayesian Optimization not only improves numerical performance but also strengthens the reliability of the intrusion detection system for modern IoT ecosystems with controlled computational loads.

The rapid development of the Internet of Vehicles (IoV) has introduced new security challenges, particularly in protecting Controller Area Network (CAN Bus) communications from cyberattacks such as Denial of Service (DoS) and spoofing attacks. This study proposes the implementation of the Extreme Gradient Boosting (XGBoost) algorithm combined with Information Gain feature selection to improve intrusion detection performance in IoV environments. The CICIoV2024 dataset, which represents both benign and malicious traffic, is used as the primary data source. The research process includes data integration, preprocessing, feature selection, data splitting, and model training using a 5-fold cross-validation approach. Experimental results demonstrate that the proposed model achieves outstanding performance, with accuracy, precision, recall, and F1-score exceeding 99.99%, and an Area Under Curve (AUC) value approaching 1.00. Furthermore, Information Gain successfully identifies the most influential CAN payload features, enhancing model efficiency without sacrificing accuracy. These findings confirm that the combination of Information Gain and XGBoost is highly effective for developing a fast, accurate, and efficient intrusion detection system in IoV networks.

This study aims to determine the role of Islamic scholars in developing Islamic education, then analyze the contribution of the Islamic organization Nahdlatul Ulama (NU). This research method is a library study (library research) with historical documents (historical document research) with a qualitative approach. Data obtained from two search sources, “Google Scholar” and “ScienceDirect.” The discussion in this article identifies, first, the role of Islamic scholars in developing Islamic education, namely: managing Islamic boarding schools as centers of Islamic education; maintaining Islamic identity and nationalism; establishing and managing madrasas; teaching Islam informally; and helping maintain social stability. Second, the contribution of the Islamic organization Nahdlatul Ulama (NU) includes maintaining Islamic boarding schools as centers of Islamic education, developing a curriculum based on Islamic tradition, instilling a spirit of nationalism through education, establishing madrasas and non-formal educational institutions, and maintaining traditional Islamic educational networks. In addition to preserving religious heritage, these ulama and the NU organization taught the next generation about knowledge and a sense of nationalism, strengthening the foundation of Indonesian society during the colonial period.

Green innovation has become a crucial approach to addressing sustainability challenges within global economic and environmental contexts. This study maps the development of green innovation research through a bibliometric analysis using data from the Scopus database covering the period 2021–2024. Bibliometric techniques were applied with VOSviewer and R Studio to examine publication trends, citation patterns, author collaboration, and keyword networks. The results show a significant growth of publications in the last five years, with major themes focusing on sustainable development, environmental technology, and economic implications of green innovation, while leading contributions come from China. Influential journals in innovation and environmental management are identified as key publication outlets, and keyword analysis reveals the integration of green innovation into sustainability strategies and economic policy discussions. This study contributes to a clearer understanding of the intellectual structure and emerging directions of green innovation research, offering insights for scholars, business practitioners, and policymakers in advancing sustainable innovation practices.

Global concern on climate change has encouraged policymakers and central banks to adopt green financial instruments such as green bonds within sustainable monetary frameworks. Research on the integration of green bonds and monetary policy has grown rapidly in recent years, reflecting wider trends in sustainable finance, climate risk management, and central bank policy innovation. Green bonds play an important role in supporting low-carbon transitions and can influence monetary operations through asset purchases and collateral policies. This study applies a bibliometric analysis of publications on green bonds and monetary policy indexed in Scopus from 2021 to 2025. Using bibliometric methods with VOSviewer and R Studio, the analysis maps dominant themes, co-authorship networks, and the evolution of green monetary studies. The results show strong growth in research output, high levels of international collaboration, and a concentration on sustainable development and green finance. However, fewer studies address climate policy uncertainty and geopolitical risk, even though these factors are highly relevant to financial stability and the effectiveness of monetary policy. Future research in these underexplored areas could provide stronger scientific foundations for building more adaptive and resilient monetary systems in both developed and emerging economies.

The digital era has transformed the financial sector through the integration of FinTech, making it more susceptible to increasingly complex cyber threats. As these risks rise, there has been a significant increase in academic research to better understand the cybersecurity challenges within the financial sector. This study aims to explore the development of cybersecurity research globally within this field. By utilizing bibliometrics, the research analyzes literature data collected from the Scopus database over the last five years. The analysis was conducted using VOSviewer and RStudio to identify dominant clusters, with cybersecurity and network security as the central themes linking various sub-fields, including artificial intelligence, cyberattacks, and phishing. The findings reveal areas of extensive research and highlight gaps that require further exploration. This study provides valuable insights for researchers and professionals in the cybersecurity field, offering a roadmap for future investigations and the identification of underexplored areas that need attention. Ultimately, this research contributes to advancing knowledge in the financial sector’s cybersecurity landscape and assists in shaping future research directions.