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The use of artificial intelligence technology in elementary education is becoming increasingly relevant as teachers demand to develop creative and efficient teaching materials. Problems faced by fifth-grade elementary school teachers include limited time, a variety of ideas, and reliance on conventional methods in developing teaching materials, resulting in suboptimal learning creativity. This study aims to describe the use of ChatGPT by fifth-grade elementary school teachers in developing creative teaching materials and to identify the benefits and constraints of its use in the learning process. The research method used is a descriptive qualitative approach with data collection techniques through literature studies and online questionnaires based on Google Forms. The research respondents were fifth-grade elementary school teachers who were familiar with and used ChatGPT in lesson planning. The data obtained were analyzed using qualitative descriptive analysis techniques through the stages of data reduction, data presentation, and drawing conclusions. The results of the study are expected to provide a comprehensive picture of the role of ChatGPT as a digital assistant for teachers in increasing the efficiency and creativity of teaching material development in elementary schools.

This study applies the Mean Variance model, which aims to form an optimal portfolio composition in the health, property, and cyclical consumer sectors and combine the three sectors into one portfolio, then visualize its efficient frontier. This study analyzes the return profiles and compares the risks of each portfolio using alternative risk measures such as the Coefficient of Variation (CV), Value at Risk (VaR), and Conditional Value at Risk (CVaR). Daily closing price data for the three sectors listed on the Indonesia Stock Exchange (IDX) from March 2, 2020, to March 3, 2025, were used in this study. Stock selection was conducted using purposive sampling, followed by selecting seven stocks for optimization based on the lowest Coefficient of Variation (CV) value. Portfolio optimization analysis was conducted using the Python programming language with Visual Studio Code software. The findings of this study indicate that the combined portfolio incorporating the three sectors is the most efficient, with an expected return of 0.104%, standard deviation of 0.007, and alternative risk measures such as Coefficient of Variation (CV) 6.9328, Value at Risk (VaR) of -0.99%, and Conditional Value at Risk (CVaR) of -1.44%, which are lower than those of single-sector portfolios. Visualization of the efficient frontier curve confirms that the combined portfolio offers better results in terms of risk and return. The results of this study indicate that cross-sector diversification can significantly reduce risk and prevent significant losses.

The rapid development of digital technology has significantly transformed financial transactions in Indonesia, particularly through the growing use of e-wallets as practical and efficient payment tools. In a country with a Muslim-majority population, ensuring that e-wallet services comply with Islamic principles—free from riba, gharar, and maysir—is essential. This study aims to analyze Indonesia’s legal politics in regulating the use of e-wallets within the Islamic financial system and to assess their alignment with sharia principles. This research employs a normative juridical method with a qualitative descriptive approach by examining laws, regulations, and fatwas related to sharia-based fintech. The findings indicate that the Indonesian regulatory framework—through the OJK, Bank Indonesia, and DSN-MUI—has attempted to harmonize policies to support sharia-compliant digital financial services. However, several challenges remain, including limited e-wallet platforms with sharia certification, low digital sharia literacy among users, and the absence of detailed technical regulations specific to sharia e-wallet operations. This study recommends strengthening regulatory guidelines, increasing public literacy, and enhancing collaboration between regulators and the fintech industry to promote the development of sharia-compliant e-wallets that are secure, innovative, and aligned with Islamic financial principles.

Magnetic resonance imaging (MRI) provides rich anatomical contrast for brain tumor assessment, yet routine interpretation remains time-intensive and demands high precision. This work develops a pipeline for four-class brain MRI image classification (glioma, meningioma, pituitary tumor, and no tumor) by combining automated brain-region cropping, data augmentation, and transfer learning with EfficientNetB1. Experimental results demonstrate exceptional performance, achieving an overall accuracy of 0.99 (99%) on the test set. Specifically, the model reached an F1-score of 1.00 for the no tumor class, 0.99 for pituitary, and 0.98 for both glioma and meningioma classes. Beyond reporting numerical performance, the study utilizes Grad-CAM heatmaps to verify that predictions rely on clinically plausible regions rather than spurious background cues. These results indicate that an efficiency-oriented backbone, paired with systematic preprocessing, can achieve reliable and interpretable performance for brain tumor classification tasks.

The production sector at both the district and city levels is crucial for fostering structural change and boosting economic growth in specific areas. Still, many regions struggle with issues such as linking supply chains, readiness for technology, quality of labor, and efficient policies. This research intends to examine the strategies of the manufacturing sector at the district and city levels to enhance regional competitiveness and promote sustainable economic growth. The study utilizes a descriptive qualitative method based on a review of literature from academic journals, policy papers, and official statistics related to manufacturing progress. Results reveal that several important factors strongly affect regional manufacturing growth. These include the connection of local supply chains, industry strategies focused on the market, the implementation of digital and smart manufacturing methods, innovation encouraged by educational institutions and organizations, and the influence of local governments in developing an effective industrial policy atmosphere. Furthermore, creating designated industrial areas and managing operations efficiently significantly helps attract investments and boost the manufacturing output of regions. The research concludes that a cohesive and tailored manufacturing strategy for each region is vital for improving local productivity, generating jobs, and enhancing economic stability at both district and city scales.

Micro, Small, and Medium Enterprises (MSMEs) are the backbone of regional economies, including in the city of Banjarmasin, as they play a crucial role in job creation and income generation for local communities. However, many MSMEs continue to face challenges in the marketing aspect, particularly due to their reliance on conventional marketing methods that have limited reach and require relatively high costs. In response to these issues, this community service program aims to promote the digitalization of MSME marketing methods in Banjarmasin through intensive training and mentoring in the utilization of social media marketing. The implementation methods include an initial needs assessment survey to identify partners’ levels of understanding, workshops on digital marketing strategies, and hands-on mentoring for direct implementation on social media platforms such as Instagram and Facebook. The results of the program indicate a significant improvement in MSME actors’ knowledge and skills, particularly in managing business accounts, developing content strategies, and creating visually appealing content that aligns with target market characteristics. Furthermore, MSME partners were able to expand their market reach more effectively and efficiently, which is expected to enhance business competitiveness and sustainability in the digital era.

The Community Service (PkM) activity aimed to enhance the capacity of civil society to conduct effective public oversight of the East Lombok Regency Regional Revenue and Expenditure Budget (APBD) for fiscal year 2025. The “Budget School” program, organized by the Pimpinan Daerah Pemuda Muhammadiyah in collaboration with the Indonesian Forum for Budget Transparency (FITRA) NTB and ITSKes Muhammadiyah Selong, provided participants with a comprehensive and critical analysis of the regional budget structure and allocation patterns. The key findings highlighted notable fiscal inefficiencies, particularly the disproportionately high allocation for Employee Spending (Belanja Pegawai), which indicates an urgent need for budget reallocation toward increasing Capital Expenditure (Belanja Modal). Such realignment is essential to accelerate infrastructure development, enhance public service delivery, and ensure broader socio-economic benefits for the community. The event, conducted on September 25, 2025, successfully improved fiscal literacy among youth and civil society actors by strengthening their understanding of fiscal governance and legal oversight mechanisms. Overall, the activity fostered collective awareness and encouraged active participation in promoting sustainable, transparent, and efficient regional financial management.

The principle of comparative advantage explains that every country or society, like individuals, can gain benefits from their trade activities by exporting goods or services in which they have a major comparative advantage and importing goods or services in which they do not. Based on the law of comparative advantage, even though a country may be less efficient (having an absolute disadvantage) compared to other countries in the production process, the structure of industrial performance can be seen through the analysis of industrial sector behavior analyzed through various strategies such as Price, Product, and promotion. The theory of comparative advantage related to the exchange of goods is relevant as long as the traded goods are still useful. In other words, Performance is defined as the result of activities influenced by the structure and behavior within the industrial sector, where these results are often measured by the size of a company's market share or profitability in an industry. In more detail, performance can also be reflected in the form of efficiency, development (including market expansion), job creation, employee welfare, and a sense of group pride.

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.

This research examines the influence of road infrastructure development on local economic growth in Medan City, emphasizing its role in improving accessibility and supporting economic activities. Road infrastructure is a vital component of regional development, as it facilitates the movement of goods and services, reduces transportation costs, and enhances connectivity between economic centers. Well-developed road networks can stimulate business expansion and encourage investment by creating a more efficient economic environment. The study employed a descriptive quantitative approach using field surveys involving 20 respondents, including business actors, local residents, and government representatives. Data were collected through structured questionnaires and analyzed descriptively to capture perceptions regarding the economic impact of road infrastructure development. The results indicate that improvements in road quality, capacity, and connectivity have a positive influence on local economic growth. Respondents reported increased trade activities, improved access to markets, higher investment opportunities, and rising community income levels as direct benefits of improved road infrastructure. However, the study also identified several challenges that may hinder long-term economic outcomes. Traffic congestion, uneven infrastructure development across areas, and inadequate routine maintenance were highlighted as significant issues. These problems can reduce efficiency and limit the sustainability of economic gains. Therefore, continuous government commitment, effective maintenance strategies, and equitable distribution of infrastructure development are essential. Strengthening coordination among stakeholders is also necessary to ensure that road infrastructure development supports inclusive and sustainable local economic growth in Medan City.

Information technology (IT) governance plays a strategic role in supporting the achievement of organizational goals, especially in higher education institutions. Adiwangsa University Jambi, as a private university, is required to manage IT effectively, efficiently, and in line with the institution's vision. This study aims to analyze the level of IT governance capabilities at Adiwangsa Jambi University using the COBIT 2019 framework with a focus on the EDM (Evaluate, Direct, Monitor) domain. The research method used is a descriptive quantitative method through data collection using questionnaires, interviews, and documentation studies. The results show that the level of IT governance capability in the EDM domain is at level 2 (Managed Process), indicating that the IT governance process is running but still needs improvement in several aspects, especially in monitoring and controlling IT performance. This study is expected to provide recommendations for Adiwangsa University Jambi in improving IT governance in a sustainable manner.

This qualitative literature review explores the transformative role of FinTech credit, particularly digital lending platforms, in fostering entrepreneurial growth by addressing information asymmetry. Drawing on recent empirical and conceptual studies, the review identifies how alternative data, algorithmic credit scoring, and AI-driven risk assessments enable more inclusive and efficient credit access for underserved entrepreneurs. It highlights the potential of FinTech to reduce traditional financing barriers, especially for small and medium-sized enterprises (SMEs) with limited collateral or credit histories. However, the review also underscores risks such as algorithmic bias, data privacy concerns, and regulatory gaps. By synthesizing findings across global contexts, this study provides a nuanced understanding of how digital lending shapes entrepreneurial ecosystems. The review concludes with implications for policy, practice, and future research

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.

SMP Negeri 5 Merlung is a public junior high school in Merlung Subdistrict that has utilized the DAPODIK system for online data processing management, enabling efficient sending and receiving of information to the government. This research analyzes IT governance on the DAPODIK system using the COBIT 5 framework, specifically the MEA01 domain (Monitor, Evaluate and Assess Performance and Conformance), which focuses on monitoring and evaluating performance and conformance. The research background is based on the need to maximize the utilization of the system at the school level. The main objectives are to determine the current and expected capability levels, as well as to provide improvement recommendations to achieve higher process maturity. The research method applies Assessment Process Activities, covering observation, interviews, identification of findings, gap analysis, and recommendations. The results show that the current capability level is at level 3 (established process), while the expected capability level is directed toward level 4 (predictable process). The implications of these findings provide practical recommendations such as routine monitoring enhancements, staff training, and integration of automation tools to bridge the capability gap, thereby improving the effectiveness of IT governance at SMP Negeri 5 Merlung sustainably.

This study aims to analyze the influence of brand image, word of mouth, and product quality on the purchase decision of Facetology skincare products. The study uses a quantitative method to obtain objective and generalizable results. The sample consisted of 100 respondents, who were individuals who had purchased or used Facetology skincare products. The sampling technique used was purposive sampling, ensuring that the selected respondents met criteria relevant to the research objectives. Data collection was conducted through the distribution of questionnaires to the selected respondents to gather the necessary information. The data analysis technique employed was Smart PLS 4.0, which efficiently and accurately measures the relationships between variables. This study emphasizes the importance of understanding the factors influencing purchase decisions in the skincare industry, particularly for the Facetology brand. The results of the study indicate that word of mouth has a positive and significant effect on the purchase decision of Facetology products. Additionally, brand image was found to have no significant effect on the purchase decision, while product quality had a positive and significant effect on the purchase decision of Facetology products. The implications of this study suggest that the company can leverage word of mouth as an effective marketing strategy and focus more on improving product quality to influence consumer purchase decisions.

Public complaint services are an essential part of public service delivery in supporting the government’s rapid response to various social issues and emergency situations. In West Tanjung Jabung Regency, public complaint services are provided through the HALO USTAD 112 Call Center managed by the Department of Communication and Informatics. However, the existing service still faces several limitations, including the lack of optimal integration in complaint data management, inadequate documentation of reports based on regional classifications, and limited capabilities in storing and retrieving complaint data. This study aims to optimize the HALO USTAD 112 Call Center service through the design of a mobile-based public complaint information system, so that the processes of receiving, managing, and monitoring reports can be carried out more effectively and in a structured manner. The system development applies the Waterfall method, which consists of requirement analysis, system design, implementation, and testing stages. The designed information system includes key features such as user and admin login, complaint submission, report management and verification, report monitoring, statistical visualization of complaint data, and regional-based report recapitulation. The application is developed using the Flutter framework with the Dart programming language, while Supabase is utilized as the backend integrated with a PostgreSQL database. The results of this study are in the form of a system design and prototype that are expected to improve the quality of public complaint services and support more accurate, integrated, and efficient data management.

This study aims to determine the ideal stocking density of catfish using the triple integral method. This mathematical method is applied to accurately calculate the volume of the cultivation pond and analyze the stocking amount and biomass projection at three different density levels, namely 50, 75, and 100 fish/m³. The calculation of the volume of the pond measuring 27 m x 11 m x 1.5 m produces a value of 445.5 m³. Based on the integral calculation, the optimal stocking amount is 22,275 fish, 33,413 fish, and 44,550 fish for each density, with the final biomass projection reaching 300.7 kg, 451.1 kg, and 600.4 kg, respectively. The analysis shows that the density of 100 fish/m³ produces the highest biomass, but its application must consider technical factors such as water quality, oxygen availability, and food competition. This method provides a solid and practical mathematical foundation for more efficient, scalable, and sustainable aquaculture planning.

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

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.

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.