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This study examines the determinants of green innovation and its impact on sustainable business performance among micro, small, and medium enterprises (MSMEs) in the Bandung Raya region of Indonesia. Specifically, the study analyzes the influence of environmental knowledge and market pressure on green innovation, as well as the effect of green innovation on sustainable performance. A quantitative approach was employed using data from 150 MSMEs, and structural relationships were tested through multiple regression analysis. All measurement instruments demonstrated high reliability (Cronbach’s Alpha 0.89–0.95) and validity (corrected item–total correlation > 0.80). The results reveal that environmental knowledge has the strongest positive and significant effect on green innovation (β = 0.728; p < 0.001), indicating that MSMEs with greater environmental awareness are more likely to adopt eco-friendly innovations. Market pressure also significantly influences green innovation (β = 0.257; p < 0.001), demonstrating the role of consumer expectations, competition, and green product trends in shaping sustainable business practices. Furthermore, green innovation has a very strong and significant impact on sustainable business performance (β = 0.847; p < 0.001), suggesting that eco-friendly practices enhance cost efficiency, customer satisfaction, firm reputation, and environmental outcomes. Overall, the study highlights the importance of combining internal awareness with external pressures to foster green innovation and strengthen sustainability among MSMEs. The findings provide theoretical contributions to green innovation and sustainability frameworks, while offering practical implications for MSMEs, policymakers, and business support institutions.

Tuberculosis (TB) is one of the infectious diseases that remains a public health problem in Indonesia, including in West Java Province, which has a large population and high mobility. This condition has the potential to increase the risk of transmission and cause variations in the distribution of cases between districts and cities. This study aims to map the distribution of TB cases across all districts and cities in West Java Province from 2022 to 2024 using a spatial analysis approach. This analysis was conducted to describe the geographical distribution of cases, identify patterns of spread, and determine areas with relatively high or low case rates. TB data was obtained from routine recording and reporting by health facilities in West Java, then integrated with population and administrative boundary data. The results of the analysis provide information on case distribution patterns between regions and trends in case changes from year to year. The findings of this study are expected to serve as a basis for local governments in formulating more targeted TB prevention and control strategies, through a focus on interventions in areas with a high case burden, as well as optimizing sustainable public health programs in West Java Province.  

Syphilis is one of the reproductive health problems that remains a challenge in Indonesia, with a trend of increasing cases, while the level of adolescent knowledge about this disease is still low. Educational efforts are needed to improve adolescent understanding, one of which is through video media which is considered more interesting because it combines visual and audio elements. This study aims to evaluate the effectiveness of video as an educational medium in improving adolescent knowledge about syphilis at SMKS Al Hikmah 2 Sirampog, Brebes Regency. This type of research is a pre-experimental study with a one group pretest-posttest design. The study sample consisted of 47 students selected using a purposive sampling technique. The research instrument was a syphilis knowledge questionnaire administered before and after the educational video intervention. Data analysis used the Wilcoxon Signed Rank Test and N-Gain calculation. The study showed that Results Before the intervention, the level of knowledge of respondents was in the good category for 24 students (51.1%), sufficient for 11 students (25.5%), and poor for 12 students (23.4%). After being given the educational video intervention, there was a significant increase with all respondents (100%) being in the good category. The Wilcoxon Signed Rank Test showed a significant difference between pretest and posttest scores (p-value = 0.000). N-Gain analysis categorized the increase in knowledge as high. The study concluded that video media has proven effective as an educational tool in increasing adolescent knowledge about syphilis, making it a viable alternative learning medium in reproductive health programs.

Rapid urbanization, especially in developing countries, has worsened spatial inequality and environmental degradation, impacting vulnerable urban communities living in informal settlements with limited access to services and increased environmental risks. This study examines the role of spatial planning law in promoting social justice and environmental protection in urban areas, particularly for marginalized populations. While spatial planning laws have the potential to promote equity and sustainability, inconsistent enforcement and inadequate evaluation mechanisms reduce their effectiveness. Using a qualitative legal analysis approach, the research reviews statutory frameworks, urban planning documents, and case studies to assess the practical application of these laws. Findings show that integrating spatial planning laws with social justice and environmental sustainability principles can enhance the living conditions of vulnerable communities by ensuring equitable resource access and reducing exposure to environmental hazards. However, fragmented legal frameworks, weak enforcement, and a lack of participatory planning hinder their impact. Comparing with traditional urban planning, social justice-oriented spatial planning provides more sustainable outcomes by prioritizing equity and environmental protection. The study recommends policy reforms to strengthen law enforcement, encourage inclusive decision-making, and integrate social justice and sustainability into urban planning frameworks. Further research is needed to assess the long-term impacts and role of these laws in achieving equitable urban development.

The rapid development of digital technology, especially after the COVID-19 pandemic, has brought changes to people's behavior and lifestyle with the trend of digital payments. One of the advances in digital financial services technology is PayLater, a Fintech innovation that offers easy transactions in the form of loans and installments without a credit card. This study aims to analyze the influence of transaction convenience and risk perception on purchasing decisions using the PayLater feature on e-commerce platforms among Generation Z in Samarinda City. This study uses a quantitative approach with associative methods and multiple linear regression analysis using SPSS 27. The method used is purposive sampling to obtain a sample of 100 respondents from Generation Z in Samarinda City, aged 18-28 years, who have used PayLater on e-commerce platforms. The findings show that transaction convenience (X1) has a positive and significant effect on purchasing decisions, but risk perception (X2) does not have a significant effect on purchasing decisions. At the same time, both variables have a significant effect on purchasing decisions. The findings of this study are expected to provide valuable insights for the PayLater feature to design more effective strategies to improve customer satisfaction and transaction experience.

The digital economy and e-commerce are rapidly transforming global markets, driving efficiency, inclusivity, and innovation. However, these developments also produce unintended consequences, particularly regarding environmental sustainability. This study aims to examine the relationship between digital transformation, the expansion of e-commerce, and their impact on carbon emissions and socio-economic outcomes. Using bibliometric analysis and VOS Viewer to map and analyze research trends from leading academic databases, this paper identifies key themes, knowledge clusters, and research gaps in the intersection of digital economy, logistics, and sustainability. The findings indicate that technological advances foster economic growth and greater accessibility but simultaneously contribute to rising energy consumption, logistics intensity, and carbon footprints. These results highlight the dual nature of digitalization as both a catalyst for inclusive development and a driver of environmental pressures. The study argues that an integrated policy framework is crucial to leverage the benefits of digital transformation while mitigating its environmental costs. It emphasizes the importance of green innovation, sustainable infrastructure investment, and inclusive e-commerce practices as key strategies for ensuring long-term socio-economic resilience. Ultimately, the paper contributes to the policy discourse by positioning innovation, inclusivity, and environmental stewardship as complementary rather than competing forces, thereby offering a pathway for future digital economy development that is both equitable and sustainable.

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.

The rise of financial technology (fintech) has significantly reshaped global investment over the last decade. Fintech innovations are increasingly applied in areas such as digital investment platforms, robo-advisors, blockchain-based assets, and cryptocurrency trading. The adoption of fintech in investment continues to grow due to the rising demand for accessibility, transparency, and efficiency in financial markets. Fintech has the potential to democratize investment by lowering entry barriers, expanding financial inclusion, and offering diverse investment instruments for retail investors. Therefore, research on fintech and investment has become an essential topic in recent years. This study uses a qualitative approach with data obtained from the Scopus database, which includes a total of 4,794 articles on fintech and investment published in the last decade (2020–2025). In addition, several software tools such as R Studio, VOSViewer, and Publish or Perish were used for data processing and bibliometric visualization. This study aims to analyze the development of research trends in fintech-driven investment, explore how technology is changing investor behavior, and provide insights for policymakers and practitioners in strengthening a sustainable and inclusive investment ecosystem.

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

Digital entrepreneurship has become a transformative phenomenon in the modern economy, changing the way businesses are developed and run through digital technology. Although the literature on digital entrepreneurship is growing rapidly, comprehensive mapping of the evolution of knowledge and research patterns is still limited. This study analysis the development of digital entrepreneurship in academic literature through bibliometric analysis of the Scopus database to identify research trends, knowledge structures, and future directions. The analysis was conducted on 1,815 publications from 681 sources (1988-2025) written by 8,023 researchers, using R Studio (bibliometrix) and VOSviewer. The results show exponential growth with an annual growth rate of 17.22%, as well as five main theme clusters: digital business models, digital innovation, social entrepreneurship, e-commerce, and fintech. Thematic evolution shifted from traditional entrepreneurship (1988–2010) to digital transformation (2011-2018) to new technology integration (2019-2025). The level of international collaboration reached 26.39%, with the United States, China, and the United Kingdom as the main contributors. These findings provide a research roadmap for identifying research gaps, collaboration opportunities, and trending topics in digital entrepreneurship.

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.

Financial inclusion has become a key driver in promoting sustainable development, especially in the era of Industry 4.0, which is characterized by rapid digitalization, technological innovation, and the transformation of financial services. Although academic interest in this topic continues to grow, research in this field has not been systematically mapped, resulting in limited understanding of global trends and thematic evolution. This study uses bibliometric analysis (BA) to explore developments, intellectual structures, and key research focuses in financial inclusion research. Data were collected from the Scopus database for the period 2015–2025, using keywords related to financial inclusion, thus ensuring a comprehensive dataset for analysis. Bibliometric methods were applied using analytical tools such as VOSviewer and R Studio to support the assessment. The results of the analysis show a consistent increase in the number of publications over the last decade, reflecting growing academic attention. The main contributions came from India, China, and the United States, with increasing participation from universities in Africa and Southeast Asia through international collaboration. The main research focus has shifted from microfinance and poverty alleviation to more digital-oriented themes, including fintech, digital finance, blockchain, and green finance. This study contributes by mapping the structure and trends of financial inclusion research and providing insights for policymakers and academics in developing inclusive financial systems that support national strategies such as the SNKI, MSME digitalization, and financial literacy programs in Indonesia to achieve sustainable development goals.

Economic forecasting is becoming increasingly important year after year, especially during crises such as the pandemic of COVID-19 and the Russia-Ukraine war. Its development can be seen from the use of basic statistical models to the increasingly widespread use of machine learning technology. Economic forecasting plays an important role in helping to formulate policies and is also a reliable tool for researchers in dealing with uncertainty. Global crises, such as inflationary pressures due to the pandemic and supply chain disruptions from the Russia-Ukraine conflict, have prompted increased research in this field in an effort to anticipate economic shocks and emphasize the urgency of forecasting to prepare strategies for dealing with future uncertainty. This literature review uses the Scopus database with 2561 publications from 2020 to 2025, analyzed using R Studio with a bibliometrix approach (specifically biblioshiny) and VOSviewer to map relevant thematic connections. This analysis shows that economic forecasting is greatly influenced by market uncertainty and geopolitical factors, and at the same time influences public policy formulation and financial stability. Research contributions from Indonesia are still limited, with only 40 documents, thus emphasizing the need to strengthen economic forecasting studies in Indonesia to support monetary policy and national financial stability.

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.

The concept of green business performance has been an interesting topic for research among well-known scholars. Green business has become crucial for advancing sustainability and fostering innovation. Green business performance has become the global requirement due to the recent economic disaster, and green measures have been considered the leading solution for it. This study aims to explore how sustainability intersects with strategic business practices, offering actionable insights for aligning environmental sustainability with economic growth. To find worldwide research trends, bibliometric analysis was performed using R studio and VOSviewer software on 3007 scientific publications from Scopus from 2021 to 2024. Bibliometric analysis uses key words to enable the study of specific details, within the main topics of research, within a domain, and relationships at the micro-level. These methods facilitated an in-depth exploration of the evolution and impact of green business for sustainability. This study bridges existing gaps by providing actionable insights into how green business drive sustainable development, offering value to researchers, policymakers, and business leaders seeking to integrate environmental sustainability with economic growth.

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.

Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.

Climate risk finance has emerged as an increasingly important field of research along with the growing urgency to address climate change and its impacts on the global financial system. Climate change poses real risks to the stability of the international economy and financial systems. Climate risk finance represents an approach that encompasses various financial instruments in supporting climate change mitigation and adaptation. Although the term climate risk finance has not been widely used explicitly as a single keyword, the concept that integrates climate risk and financing is reflected in related keywords such as climate risk, climate finance, and climate change. This study employs a bibliometric analysis method using the Scopus database, supported by analytical tools such as VOSviewer and R Studio, to explore the development of research on climate risk finance. The study identifies publication patterns, international collaborations, and emerging themes within the related literature. The findings show that the publication rate on climate risk finance is relatively moderate each year, but has experienced growth in the last decade. The evolving understanding in this field is expected to strengthen the resilience of financial systems and support sustainable strategies to address long-term climate risks.

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.

Global climate change has prompted analysis of emission control policies, including carbon trading mechanisms and carbon taxes, to achieve a low-carbon economy. This study utilizes a bibliometric approach (2020–2025) with the Scopus database, which is processed using the RStudio application with Bibliometrix and Biblioshiny analysis and also uses the VOSviewer application, to identify publication trends and the effectiveness of emissions policies in different economic contexts, such as G20 countries or developed countries with developing countries. Thematic analysis results show dominant topics such as emissions trading, carbon capture, renewable energy, and a focus on economic development and technological innovation. The reviewed literature confirms that higher carbon tax rates are more effective in reducing emissions in G20 countries, while in developing countries, the implementation of a combination of strict carbon taxes and emissions trading mechanisms can promote substantial decarbonization. Overall, this bibliometric study confirms the growing research interest in green economy solutions and supports the urgency of adaptive, evidence-based emissions policies in various economic contexts.