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Smart industry has become an important trend in the development of Industry 4.0, especially in promoting the creation of efficient systems in the manufacturing sector. Various countries and studies are encouraging the application of technologies such as IoT, digital twins, artificial intelligence, and smart factories to improve industrial efficiency and sustainability. Therefore, studies related to smart industry are important and necessary especially on the context of smart manufacturing in order to see the direction of future research trends. This study uses a qualitative approach with literature data from the Scopus database covering the period 2020 to 2025. Research trend analysis was conducted through data processing using Bibliometric analysis in R Studio and the VOSviewer applications. To identify the latest research trends regarding smart industry, particularly in the context of Industry 4.0 and smart manufacturing, this analysis can provide a comprehensive picture of future research developments and directions within a global context.

The integration of big data in the financial sector has increasingly attracted scholarly attention, particularly in areas such as risk management, fraud detection, algorithmic trading, and investment optimization. Given the rapid development of this field, it is essential to map research trends and identify emerging directions that shape the future of financial innovation. This study applies a bibliometric approach using 3,829 articles retrieved from the Scopus database from 1981 to 2025, with data processed through R Studio and the Bibliometrix-Biblioshiny application. The objective is to explore the intellectual landscape of big data finance and reveal research frontiers as well as thematic evolution. The results show a sharp increase in publications after 2015, alongside the growth of fintech and artificial intelligence applications, with dominant themes including blockchain integration, risk analytics, and predictive modelling. Cross-disciplinary and cross-regional collaborations continue to expand. These findings provide a comprehensive overview of how big data has shaped financial studies and offer insights for potential future research directions.

A wall follower robot is a type of autonomous robot that is designed to move by following a wall at a certain distance. This research aims to design and build a Wall follower robot equipped with a Fuzzy-PID control system to improve navigation performance. The robot uses five HC-SR04 ultrasonic sensors to detect the distance to the wall and the surrounding obstacles. The data from the sensor is then processed by a Fuzzy-PID algorithm that combines the advantages of conventional PID control with fuzzy logic, resulting in a more adaptive response to environmental conditions. The test results showed that the robot with Fuzzy-PID control was able to maintain the stability of the distance to the wall more consistently compared to the pure PID control. In addition, the system exhibits better adaptability to complex environmental conditions, such as sharp turns, uneven wall surfaces, and the presence of resistance variations. The application of Fuzzy-PID control has been shown to improve the stability, response speed, and accuracy of the robot's navigation. These findings are expected to contribute to the development of robotic navigation systems for a wide range of practical applications, including automated cleaning robots, environmental exploration, and industrial systems that require reliable autonomous mobility.

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.

Traffic congestion is a persistent challenge in urban areas in Indonesia, where increasing vehicle density creates the need for intelligent traffic monitoring systems. This study aims to develop a real-time vehicle parking system using the YOLOv8 object detection model to provide efficient traffic analysis from live CCTV broadcasts and recorded videos. This study uses a quantitative experimental approach with the implementation of the YOLOv8m model using the Ultralytics library in Python, tested on data collected from CCTV cameras A TCS Dishub Medan and additional footage from mobile devices. Vehicles are detected and counted in two directions up (Up) and down (Down) using virtual detection lines on the video frame. The system performance is evaluated by automatic detection counting with manually recorded ground truth data. The results show that on live CCTV broadcasts, the YOLOv8m model achieves an average precision of 98.96%, a recall of 96.59%, and an F1 score of 97.74% for upstream traffic, while for downstream traffic it achieves 100% precision, 95.64% recall, and an F1 score of 97.730/0. On the other hand, on high-quality recorded videos, all performance metrics achieve 100%, indicating perfect detection accuracy. These findings confirm the effectiveness of YOLOv8 in real-time traffic monitoring, but also indicate that video quality and stream stability affect detection performance. In conclusion, the developed system shows strong potential to support smart city traffic management solutions. Future research should focus on performance optimization under low-resolution live streaming conditions to improve accuracy in practical applications.  

Artificial Intelligence (AI) has become a significant driver of innovation in the banking sector, especially in the context of post-pandemic digital transformation. AI is widely utilized in areas such as fraud detection, credit evaluation, risk management, and customer interaction, attracting considerable interest from both academics and industry professionals. This research explores the recent advancements in AI within the banking industry, focusing on studies published between 2020 and 2025. A bibliometric approach is employed, using data from the Scopus database and bibliometric tools like VOSviewer and R Studio to visualize keyword networks and track emerging trends. The study aims to identify influential authors, journals, and countries contributing to AI research in banking. By analyzing these developments, the research highlights the contributions of AI to improving operational efficiency, data security, and financial inclusion, particularly in the Indonesian context. This work offers valuable insights into the ongoing integration of AI in the banking sector and its potential to shape future financial services, emphasizing its relevance to both global and regional markets.

This community service activity aims to increase the understanding of students at State Senior High School 2 Mataram regarding information security by introducing the concepts of coding and cryptography in digital messages. The rapid use of messaging applications among teenagers makes students increasingly vulnerable to cyber threats, necessitating education on how data protection works in online communication. This program is implemented using a descriptive method consisting of planning, implementation, and evaluation stages. The material covered includes basic cryptography concepts, end-to-end encryption mechanisms, and the practical process of the Diffie–Hellman key exchange thru the interactive simulation "Alice and Bob." Learning is designed contextually and participatively so that students can connect theory with the digital applications they use every day. The evaluation results showed an improvement in students' understanding, reflected in their active participation, ability to answer questions, and adequate post-test scores. This activity not only strengthens digital security literacy but also raises students' awareness of the importance of protecting personal data in online communication. This program is expected to be the beginning of more sustainable digital security learning development in the school environment.

This community service activity aims to increase the understanding of students at State Senior High School 2 Mataram regarding information security by introducing the concepts of coding and cryptography in digital messages. The rapid use of messaging applications among teenagers makes students increasingly vulnerable to cyber threats, necessitating education on how data protection works in online communication. This program is implemented using a descriptive method consisting of planning, implementation, and evaluation stages. The material covered includes basic cryptography concepts, end-to-end encryption mechanisms, and the practical process of the Diffie–Hellman key exchange thru the interactive simulation "Alice and Bob." Learning is designed contextually and participatively so that students can connect theory with the digital applications they use every day. The evaluation results showed an improvement in students' understanding, reflected in their active participation, ability to answer questions, and adequate post-test scores. This activity not only strengthens digital security literacy but also raises students' awareness of the importance of protecting personal data in online communication. This program is expected to be the beginning of more sustainable digital security learning development in the school environment.

As a high-value crop, potatoes necessitate balanced nutrient management for optimal growth and yield. This research aimed to assess how varying applications of NPK 20-20-10 fertilizer influenced potato growth, yield, tuber quality, agronomic efficiency, and economic viability within tropical climates. The experimental setup involved a randomized complete block design, incorporating four replications across seven distinct treatments: a control, a standard inorganic fertilization regimen, and NPK 20-20-10 applied at 0.50, 0.75, 1.00, 1.25, and 1.50 times the suggested dosage. The findings indicated that applying NPK 20-20-10 significantly enhanced several parameters, including plant height, branch count, tuber count, tuber weight, and overall yield components, when contrasted with the control group. Notably, the 1.25 times recommended dose demonstrated superior performance, leading to a 34.9% increase in tuber number and a 68.6% rise in tuber weight compared to the control. Agronomic effectiveness scores surpassed 100 for dosages ranging from 0.75 to 1.50, with the 1.25 dose registering the peak value. Economic evaluations confirmed the profitability of all NPK treatments, and the 1.25 dose yielded the most favorable R/C ratio and a net profit of IDR 29,053,400. Consequently, the recommended application for potato cultivation is 675 kg/ha of NPK 20-20-10, distributed in three equal parts at planting, four weeks post-planting, and six weeks post-planting. Thus, these results underscore that NPK 20-20-10, when applied at 1.25 times the recommended rate, presents an agronomically effective and economically sound strategy for sustainable potato farming in tropical settings.

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

End-to-end autonomous driving has emerged as a promising paradigm in which deep neural networks directly map raw visual inputs to continuous control actions. Despite its effectiveness, this approach suffers from limited transparency, posing significant challenges for deployment in safety-critical driving scenarios. This study addresses the lack of interpretability in vision-based end-to-end autonomous driving systems and aims to analyze model decision-making behavior under critical conditions such as sharp steering maneuvers and abrupt control transitions. To this end, an explainable end-to-end autonomous driving framework is proposed, combining a convolutional neural network trained via imitation learning with gradient-based visual attribution techniques, including Grad-CAM. The model predicts continuous steering, throttle, and braking commands directly from front-facing camera images, while explainability mechanisms are applied to reveal input regions influencing each control decision. Model performance is evaluated using both prediction accuracy and safety-oriented behavioral metrics. Experimental results show that the proposed explainable model achieves lower control prediction errors compared to a baseline end-to-end CNN, reducing steering mean squared error from 0.034 to 0.031, throttle error from 0.021 to 0.019, and brake error from 0.018 to 0.016. Moreover, safety-oriented analysis indicates improved driving stability, with steering variance reduced from 0.087 to 0.072 and abrupt control changes decreased from 14.6 to 10.3 events. Visual explanations consistently highlight road surfaces and lane-related structures during complex maneuvers, indicating reliance on semantically meaningful cues. In conclusion, the results demonstrate that integrating explainability into end-to-end autonomous driving not only preserves predictive performance but also correlates with smoother and more stable driving behavior. This framework contributes to the development of transparent and trustworthy autonomous driving systems suitable for safety-critical applications

This research examines the use of blockchain technology to support energy sustainability in urban areas. Blockchain offers transparency, security, and efficiency in recording and distributing energy data, potentially optimizing renewable energy use and reducing carbon emissions. The research method involves literature analysis and simulations of blockchain applications in urban energy systems. The results show that blockchain implementation can increase energy distribution efficiency by up to 20%, reduce data reporting time by up to 99%, and reduce carbon emissions by 50%. In conclusion, blockchain technology can be a strategic innovation in supporting the transition to a sustainable and environmentally friendly energy system.

The rapid advancement of technology has made it easier for the public to access online loans (pinjol), but it has also increased the risk of misuse by illegal entities. The Financial Services Authority (OJK) recorded that from 2018 to 2022, it shut down 4,265 illegal online lending platforms. However, many cases continue to emerge due to the ease of creating applications and the use of overseas servers. Teachers are among the most affected victims due to low salaries and high living costs. The impacts of illegal online loans are highly detrimental, including the leakage of personal data, threats, intimidation, and excessively high interest rates. Education is crucial to prevent new victims. Isometric motion graphics are chosen as a medium because of their advantages in delivering information through engaging, clear, and easily understood visual and audio elements that are sustainable for audiences.

This study was motivated by the need for madrasahs to adapt to technological developments, particularly in document management, which was previously done manually. The purpose of this study was to describe the role of administration in digital archive management at MTsN 3 Jember, identify the obstacles that arose, and explain the efforts made to overcome these obstacles. The research used a descriptive qualitative approach with data collection through interviews, observation, and documentation. The results showed that administration played an important role as the main manager of digital documents through the use of applications such as SIMPEG, TTE, and SAKTI, as well as the use of cloud-based storage to maintain the security and ease of access to archives. The obstacles faced include the limited digital capabilities of employees, technical disruptions to applications, and suboptimal technological infrastructure. However, various efforts have been made, including performing regular backups, improving the organization of digital documents, and enhancing staff capabilities through independent learning. This study confirms that the success of digital archive management requires synergy between human resource readiness, technological support, and managerial policies that support the transformation of madrasah administration.

Plastic waste detection in indoor environments is an essential challenge in the development of intelligent cleaning systems and robotic automation. Small and medium-sized plastic debris is often difficult to identify using conventional methods due to variations in color, shape, and reflectance. This study proposes an image-processing-based approach that combines thresholding and contour detection techniques to improve the accuracy of detecting plastic objects on floor surfaces. The initial stage involves converting the image into a color space that is more stable under varying illumination, such as HSV or grayscale, to reduce the influence of lighting intensity. Subsequently, adaptive thresholding is applied to separate plastic objects from the background by using dynamic threshold values tailored to the image’s conditions. The segmentation results are refined through morphological operations such as opening and closing, enabling the removal of small noise and enhancing the clarity of object boundaries. The core stage of the system employs contour detection to extract object shapes and areas, allowing the identification of plastic waste based on size, perimeter, and specific geometric characteristics. Experiments were conducted under different lighting conditions and various floor types, and the results demonstrate that the proposed approach successfully detects plastic debris with satisfactory accuracy and relatively fast processing time. Therefore, this method is suitable for implementation in robotic cleaning systems, indoor cleanliness monitoring devices, and other computer vision applications requiring real-time and efficient object detection.

This study aims to investigate the effects of tamarind leaf extract (Tamarindus indica L.) on neuropharmacological activity in mice using calculated percentages of responses based on the parameters PSM, SSSP, DSSP, SL, RO, SM, PSL, and ANA. This research was conducted as a laboratory experiment using a completely randomized design (CRD) with three treatment concentrations: 1%, 2%, and 4%. Observations were performed to assess the percentage of activity produced by each sample concentration, followed by descriptive–quantitative analysis to determine the dose–response pattern. The results showed that tamarind leaf extract produced varying responses across concentrations. The SSSP, ANA, and RO effects demonstrated increased activity at the 2% concentration, whereas other parameters (PSM, PSL, SM, SL, and DSSP) showed decreased activity. Interestingly, the SSSP parameter exhibited a positive dose–response pattern with the highest activity of 55.84% at 2%. Overall, the effectiveness of tamarind leaf extract depends on the concentration level. The 2% concentration appears to be the optimal dose for several neuropharmacological effects, while the 4% concentration was most effective only for SSSP. These findings highlight the importance of multi-concentration testing to determine effective dosing of natural products for biological applications and the need for further investigation.

This study aims to analyze the implementation of the Economic Entity Principle in the micro, small, and medium enterprise (MSME) Kedai Kita, which still relies on a simple financial recording system. The principle emphasizes the importance of separating personal and business finances to ensure that financial statements accurately and objectively reflect the entity's economic condition. The research employs a qualitative method with a descriptive approach through direct interviews with the business owner to gain an in-depth understanding of the financial management practices implemented. The findings indicate that the application of the Economic Entity Principle at Kedai Kita has not been fully realized, as evidenced by the ongoing mixing of personal and business funds, the absence of a formal bookkeeping system, and inconsistent recording of cash flows and expenditures. The main factors hindering the implementation of this principle include limited accounting literacy, lack of time for bookkeeping, and the absence of a structured accounting system. Nevertheless, the business owner has begun to recognize the importance of separating finances as a foundation for more accountable business management. These findings imply the need for the adoption of simple recording applications, the provision of basic accounting training, and increased understanding among MSME actors regarding the benefits of structured financial statements. This study provides practical contributions for other MSMEs by demonstrating that the implementation of the Economic Entity Principle is a fundamental step in enhancing financial transparency and accountability, as well as strengthening opportunities for access to formal financing.

This study investigates strategies for improving social rehabilitation services for people with physical disabilities through technology-based approaches and family involvement at UPT Social Rehabilitation Bina Laras Pasuruan, East Java. Employing a qualitative case study design, data were collected through in-depth interviews, focus group discussions, observation, and documentation with rehabilitation staff, persons with disabilities, family members, and technology specialists. Findings reveal that integrating digital technologies—including mobile health applications, telemedicine platforms, and assistive devices—significantly enhances service accessibility and monitoring capabilities. Structured family involvement programs, encompassing psychoeducation sessions, home-based care training, and support groups, demonstrate substantial improvements in rehabilitation outcomes. Critical success factors include adequate technological infrastructure, staff digital literacy, family commitment, and inter-agency coordination. Challenges include the digital divide, resistance to technological adoption, and sustainability concerns. The research concludes that a hybrid model combining technology-enabled services with intensive family participation offers the most promising approach and recommends policy frameworks that institutionalise these innovations while ensuring equitable access across diverse beneficiary populations

The development of Islamic banking in Indonesia faces challenges in attracting customers, especially younger generations who are more familiar with digital technology. Digital marketing has become an important strategy for reaching a wider market segment through social media, websites, and mobile applications. This study uses a literature review method to analyze the effectiveness of digital marketing in increasing customer interest in Islamic banking. The results of the study show that educational, interactive, and transparent digital marketing can increase customer awareness, trust, and emotional attachment to Islamic products. The factors for the success of this strategy include content quality, ease of access to digital services, content segmentation according to customer profiles, and multichannel communication integration. However, the effectiveness of digital marketing is still limited by the scarcity of educational content, human resource capacity, and the level of digital literacy among the public. This study concludes that optimizing creative, personalized, and educational digital marketing strategies is key for Islamic banking to increase customer interest and loyalty.

This study aims to analyze the importance of regulating property division agreements as a requirement in polygamy permit applications, using the approach of Aristotle's theory of justice, as well as maqā ṣ id al- syarī'ah in Islamic law. The type of research is normative, which includes conceptual and philosophical analysis of law, as well as comparative research on property regulation practices in the family law system. The study shows that the practice of polygamy tends to result in economic inequality, neglect of the rights of the first wife and children, and legal conflicts related to the ownership and division of joint property. There are two approaches to property division: equal distribution of property and division of property based on contribution. The results indicate that the contribution-based approach is fairer and more relevant to modern society, especially since women are increasingly involved in household expenses. The principle of sadd al-dzarī'ah also states that this agreement is very important as a preventive measure against possible damage that occurs in the household. Practically, this study proposes the implementation of regulations in state law to require property division agreements as a formal requirement in polygamy permit applications. Combining western and Islamic values of justice within a socially just family law framework is the main focus of this research.