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This community service activity was conducted to assist banking institutions in the Surabaya region in enhancing the effectiveness of marketing strategies for savings products in order to increase the number of customers. The main problems faced by banks include the relatively low level of public interest in saving and the suboptimal implementation of promotional strategies, particularly in the utilization of digital media. This activity employed a collaborative approach through training, mentoring, and joint evaluation with marketing staff of banks in the Surabaya area. This activity commenced with an observation and interview phase to identify the marketing strategies that had been previously implemented. Subsequently, material delivery, technical assistance, along with an evaluation of promotional effectiveness. The results indicate that participants were able to implement more adaptive marketing strategies by utilizing digital media such as social media, website, and banking applications. The implementation of these strategies was shown to increase public interest in opening saving accounts. Furthermore, this activity contributed to enhancing the capacity of marketing staff to design data-driven strategies and to strengthening public financial literacy regarding saving products.

The Information System registration module for the Regional Taxpayer Identification Number (NPWPD) was developed using the Laravel framework and implemented by the Taxpayer Identification Agency (BPPKAD) at Kediri City. The system was designed to digitize the NPWPD registration process, which was previously done manually. This traditional approach often led to long queues, extended processing times, and, at times, errors in data entry. The new system offers several key advantages, including an online registration form that allows taxpayers to upload required documents such as photos of ID cards, business locations, and other necessary paperwork. Data validation is performed by officers to ensure accuracy, and automatic notifications are sent to taxpayers, informing them of the status of their applications. The implementation of this system has had several positive impacts, such as significantly improving the efficiency of administrative processes, reducing the manual workload for officers, and increasing transparency and accountability in public services. Moreover, it has improved customer satisfaction by providing faster, more accurate, and more responsive services. This system supports the creation of a streamlined, user-friendly, and effective method for taxpayers to register for NPWPD online, enhancing the overall quality of public sector service delivery.

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 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.

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.

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 development of digital technology has led to an increase in the use of short video-based entertainment applications, including the Melolo application. However, the free version still has various complaints, such as inconsistent subtitles, unintuitive navigation, force close glitches, and unstable advertisements, so user satisfaction analysis is needed. This study aims to measure the level of satisfaction of users of the free version of the Melolo application using the End User Computing Satisfaction (EUCS) method, which covers five variables, namely content, accuracy, format, ease of use, and timeliness. Data was collected through an online questionnaire of 385 Melolo app users in Jambi City and analyzed using Structural Equation Modeling (SEM) with the help of SmartPLS 4. The results showed an R-Square value of 0.546, indicating that the model was able to explain 54.6% of the changes in user satisfaction levels. The variables of content and timeliness were found to have a significant effect on user satisfaction, while accuracy, format, and ease of use had no significant effect. These results indicate that content quality and system timeliness are the main factors in increasing user satisfaction. Therefore, Melolo app developers are advised to maintain content quality and improve system performance and stability to optimize the user experience.

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.  

Soil contamination by hydrocarbons, pesticides, heavy metals, and complex pollutants is rapidly increasing and degrading essential ecosystem functions. Physical or chemical treatments offer faster results, yet they are often costly, energy-intensive, and risk disrupting soil biological integrity without fully eliminating pollution sources. Microorganism-based bioremediation provides a more sustainable alternative by utilizing microbial metabolism to degrade or immobilize pollutants into less toxic and less mobile forms. This article presents a structured literature review on the roles and applications of microorganisms for bioremediation of contaminated soils, covering comparisons between single isolates and microbial consortia, dominant biological mechanisms, and ecological challenges in field application. A Systematic Literature Review approach was applied, using narrative synthesis and thematic clustering of national and international journals published between 2020 and 2025. The review indicates that single microbial isolates are commonly selected for specific pollutant targets, whereas microbial consortia are preferred for mixed or persistent contaminants due to metabolic synergy that enhances microbial adaptability and stepwise pollutant breakdown in highly polluted soils. Adaptive mechanisms such as EPS production and biofilm formation contribute to microbial resilience under stress and help retain contaminants within the soil matrix. Key challenges identified include inoculum stability under extreme conditions and limited microbial access to pollutants trapped in micro-soil pores. The findings highlight that microbial selection strategies must be tailored to pollutant characteristics and soil environmental conditions, while also emphasizing the potential of biofilm-based systems and organic carriers to support broader field implementation of microbial bioremediation.

This study aims to map the intellectual structure and research trends in entrepreneurial innovation using bibliometric analysis based on Scopus data. A total of 891 documents published between 1972-2025 were analyzed through Bibliometrix and Biblioshiny, employing techniques such as bibliographic coupling, co-authorship, and thematic mapping. The results reveal four major clusters: (1) innovation theory and entrepreneurial development, (2) business model innovation and digital transformation, (3) regional innovation systems and policy frameworks, and (4) sustainability and green entrepreneurship. Emerging themes include artificial intelligence (AI), generative AI applications, and digital entrepreneurship education, indicating a shift toward multi-level and interdisciplinary integration. Influential documents and authors were identified, highlighting their role in shaping the knowledge base. The findings suggest that entrepreneurial innovation research is evolving toward digitalization, sustainability, and policy-driven ecosystems, offering opportunities for longitudinal and mixed-method studies. This study contributes by providing a comprehensive overview of the field, identifying gaps, and proposing future research directions to strengthen theoretical and practical advancements.

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.

The increasing demand for interactive web applications has encouraged the adoption of server-driven approaches such as Livewire as an alternative to building Single Page Applications (SPAs) without complex client-side JavaScript. However, the performance implications of this approach compared to conventional methods remain insufficiently explored. This study presents an empirical comparison between Laravel Blade with AJAX and Livewire in an academic attendance system scenario. Performance evaluation was conducted using k6 on the same web server, complemented by manual browser-based testing to observe actual communication patterns. The results indicate that Livewire exhibits approximately 2.7× higher average response time and up to 6× greater bandwidth consumption than Laravel Blade, primarily due to its snapshot mechanism and state synchronization process. Conversely, Livewire demonstrates better stability, reflected by lower maximum response times and a 0% error rate. These findings highlight a clear trade-off between resource efficiency and development convenience, where Livewire favors stability and developer productivity, while Laravel Blade provides superior efficiency in terms of latency and bandwidth usage.

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.

Managing Regional Public Service Agency (Badan Layanan Umum Daerah/BLUD) hospitals requires planning and budgeting processes that are accountable, measurable, and aligned with service performance. In practice, BLUD planning is still constrained by fragmented applications (hospital information system/SIMRS, finance, human resources, e-office, and procurement), duplicate data entry, approval delays, and limited monitoring of process compliance. This study aims to analyze requirements and design a web-based BLUD planning information system using an Enterprise Application Integration (EAI) approach through middleware to improve cross-system interoperability, data consistency, and the timeliness of executive reporting. The study adopts the Design Science Research (DSR) framework, comprising problem identification, definition of solution objectives, artifact design and development, demonstration, evaluation, and communication/report writing. The proposed system includes a unit-based budget proposal module and item management, a role-based approval workflow (RBAC) with SLA tracking, a budget ceiling (pagu) master to benchmark proposals, audit trails and report exports, and an executive dashboard integrating budget perspectives, service indicators (e.g., bed occupancy rate/BOR and patient visits), and process compliance. It also provides an integration design via middleware (ESB/message broker) supported by a canonical data model (CDM) and traceable logging (trace_id/correlation_id). Evaluation using black-box testing and API contract testing indicates that the main planning workflow operates as intended and the integration interfaces are consistently defined, providing a foundation for staged implementation and further performance evaluation.

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.