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This research conducted a systematic literature review of studies related to analyzing service quality based on user reviews with a machine learning approach. A total of 15 international and national journals were analyzed to identify challenges, methods, and trends in research in this aspect. The review results show that Natural Language Processing (NLP) and Sentiment Analysis techniques are the dominant approaches, with machine learning models such as Deep Learning, Naive Bayes, and Support Vector Machine (SVM) being commonly used. The review also identifies research gaps and provides recommendations for future research directions.

This study meticulously analyzes the digital business transformation undertaken by Ash Scarf, a representative Small and Medium Enterprise (SME). The research meticulously highlights the strategic adoption and multifaceted utilization of digital marketing by Ash Scarf as a pivotal mechanism for business expansion, enhanced consumer outreach, and significant sales augmentation. To fortify these findings and contextualize them within broader academic discourse, a comprehensive bibliometric analysis of extant scholarly literature concerning digital marketing strategies pertinent to SMEs will be integrated, providing a robust understanding of prevailing trends and recognized best practices in this dynamic field.

This article discusses the impact of Environmental, Social, and Governance (ESG) integration on the general insurance underwriting model. This study used a Narrative Literature Review (NLR) to review relevant literature. The NLR review was conducted on scientific publications, industry reports, regulatory documents related to ESG, and general insurance underwriting from 2019 to 2025. The reviewed literature shows that adopting ESG factors in the underwriting process changes risk assessment and creates more sustainable practices in the insurance industry. The analysis results show that companies with higher ESG scores have better underwriting performance and attract more investment while increasing stakeholder trust, including an increasing trend of ESG factor integration into the underwriting process, especially in risk assessment and premium determination. Regulatory changes encourage insurance companies to adopt underwriting practices that consider more environmental and social aspects. This study also identifies challenges in implementation, including the limited standardized ESG data and the need to increase human resource capacity. The implications of this study provide insights for regulators and industry players in developing underwriting policies and strategies that are more responsive to ESG issues, as well as highlighting opportunities for ESG-based insurance product innovation in the future.

This study examines Topten Printing, a printing company based in Metro City that offers diverse printing services characterized by competitive pricing, high-quality output, and efficient service. The research addresses the challenge of sustaining competitiveness amid rising market competition and evolving customer preferences. The objective is to analyze the company’s existing competitive strategies and propose viable alternatives for future growth. Employing a qualitative approach through a case study method, the research utilizes SWOT analysis and the Quantitative Strategic Planning Matrix (QSPM) to assess internal and external factors influencing the business. The results reveal that Topten Printing’s key strengths lie in its affordable pricing, reliable print quality, and prompt service delivery. However, the company also faces notable weaknesses, including a limited workforce and insufficient engagement on social media platforms. Opportunities exist in the growing demand for printed materials and government initiatives supporting micro, small, and medium enterprises (MSMEs), whereas significant threats stem from digital transformation trends and raw material price volatility. The synthesis of findings indicates that to enhance its market position, Topten Printing should implement a growth-oriented strategy focused on increasing production capacity by hiring freelance workers and intensifying social media marketing efforts. In conclusion, strengthening human resources and expanding digital marketing activities are essential steps for Topten Printing to maintain competitiveness and seize market opportunities in an increasingly dynamic business environment.  

International Micro, Small and Medium Enterprises (MSMEs) face significant challenges in improving global competitiveness due to limited resources and access to effective market analysis, despite contributing 45% to the global economy (OECD, 2025). This research aims to develop an integrated machine learning (ML) model with a mixed-methods approach to optimise cross-border MSME market analysis. A combination of quantitative (transaction data analysis of 500 Indonesian export MSMEs 2020-2024 using XGBoost and SEM-AMOS) and qualitative (interviews with 15 MSME players) methods revealed that the XGBoost model achieved 89% accuracy in predicting market trends, with key variables including social media sentiment (28%) and exchange rate fluctuations (19%). Qualitative results show that 65% of MSMEs face cross-border regulatory barriers that ML models do not detect. The findings extend the Resource-Based View theory by validating AI-driven market intelligence as a strategic asset (β = 0.67, p 0.7. This research highlights the importance of technology integration and contextual adaptation in the digital transformation of MSMEs.

Despite their 45% contribution to the global economy, international micro, small, and medium-sized enterprises (MSMEs) face considerable obstacles in enhancing their global competitiveness because they lack the resources and access to efficient market analysis (OECD, 2025). In order to optimize cross-border MSME market analysis, this research attempts to construct a machine learning (ML) model coupled with a mixed-methods approach. A combination of quantitative (XGBoost and SEM-AMOS were used to analyze transaction data of 500 Indonesian export MSMEs 2020–2024) and qualitative (interviews with 15 MSME players) methods showed that the XGBoost model achieved 89% accuracy in predicting market trends, with key variables including exchange rate fluctuations (19%) and social media sentiment (28%). According to qualitative findings, the ML model does not identify cross-border regulatory constraints that 65% of MSMEs must deal with. These results validate market intelligence powered by AI as a strategic asset, extending the Resource-Based View paradigm. The significance of contextual adaptation and technological integration in the digital transformation of MSMEs is emphasized by this study.

This study examines trends in scientific publications on digitalization from 2014 to 2024 published in Scopus-indexed journals worldwide. Data were collected from the Scopus database on October 6, 2024, at 21:56 WIB using the keywords "Digitalization" and "Public Services." A total of 593 documents were found in various categories, including titles, abstracts, and keywords. To analyze these documents, the authors employed the VOSviewer program (version 1.6.17), which enables the visualization of relationships in research data. The search analysis on document results was used to complete the data collection process, followed by detailed data analysis. The research data was analyzed through infographic mapping, which visualized research developments and document mapping on various aspects such as affiliations, countries, subject areas, and types of publications. This analysis revealed significant correlations among studies focused on the digitalization of public services. The findings show that research on the digitalization of public services from 2014 to 2024 has consistently grown year after year, with 593 published documents discussing the issue across different countries. The Russian Presidential Academy of National Economy and Public Administration emerged as the most active affiliation, publishing 14 documents on the topic. The Russian Federation ranked first in scientific publications, contributing 83 documents indexed by Scopus. In terms of subject areas, the study highlighted the growing importance of digitalization in enhancing public services globally. The VOSviewer analysis revealed strong connections among studies in this field, further emphasizing the rising global focus on digitalization in public administration and governance. These trends reflect the growing significance of digital tools and technologies in improving the efficiency and accessibility of public services worldwide, signaling an ongoing and expanding area of academic and policy interest.

The development of vocational education requires Vocational High Schools (SMK) to align their competencies with student interests and industry needs. However, a mismatch between student interests and the competencies offered can result in low enrollment, requiring schools to consider closing certain programs. This study proposes the application of Artificial Neural Networks (ANNs) as a predictive method to determine the potential closure of vocational competencies based on an analysis of student interest patterns. The data used includes interest history, academic grades, and other preference indicators, which are then subjected to a preprocessing stage to ensure the quality of the model’s input. The ANN is trained to accurately recognize interest patterns, thus generating objective and adaptive decision-making recommendations. The results show that the ANN implementation provides high accuracy in predicting student interest trends and provides more precise The development of vocational education in Vocational High Schools (SMK) requires the ability to align skill competencies with students' interests and industry needs. A mismatch between students' interests and the competencies offered can lead to low interest in certain programs, which in turn may result in the decision to close those programs. This study proposes the application of Artificial Neural Networks (ANN) as a predictive method to determine the potential closure of skill competencies based on the analysis of students' interest patterns. The data used includes interest history, academic grades, and other preference indicators. This data is processed through a preprocessing stage to ensure the quality of input for the model. The ANN is trained to accurately recognize students' interest patterns, allowing it to generate more objective and adaptive decision recommendations. The results of the study show that the application of ANN has high accuracy in predicting students' interest trends and provides more precise recommendations compared to traditional methods. Therefore, this system can be an effective tool for schools to plan curriculum policies more strategically and sustainably, as well as support decisions regarding skill programs that align with students' interests and industry needs.  

The role of Cargo Handling on the Loading and Unloading Process at PT Terminal Teluk Lamong is very important for the loading and unloading process at the port. This research aims to identify various technical and non technical obstacles in the operation of cargo handling equipment such as ASC, forklift, and reach stacker. The interview results showed that there are four main factors that become obstacles in the loading and unloading process, namely machines, methods, facilities, and manpower. Further analysis using fishbone diagrams reinforced these findings by outlining the causes of each factor, ranging from machine interference, wrong container placement methods, limited technological facilities such as Container Scanner Reader (CSR), to the driver’s lack of understanding of the automation system. Quantitatively, the efficiency of cargo handling was evaluated through the 2023 container throughput, which showed a positive trend with a significant increase in load activity, especially in October which recorded the highest number of 39.106 Boxes. The analysis concluded that although there are various obstacles in cargo handling practices, the system implemented by PT Terminal Teluk Lamong has generally been able to improve the efficiency of the loading and unloading process, as evidenced by the volume of container handling.

BBPVP Semarang is a leading center for Fashion Technology and includes a subunit dedicated to Instructor Development for both government and private sectors, particularly in the field of Fashion Technology with a focus on Fashion Design Programs. It is the only work unit that offers an Upgrading Program in Fashion Design. The purpose of the upgrading program is to enhance knowledge in Fashion Design, which evolves annually based on trend forecasting and aligns with the needs of the business and industrial sectors. This study aims to describe and analyze the management of the upgrading program implemented by the Balai Besar Pengembangan Vokasi dan Produktivitas (BBPVP) Semarang, focusing on the planning, implementation, and evaluation phases. This research employs a qualitative approach using a case study method. Data collection techniques include interviews, observations, and documentation. The research uses source triangulation, involving 2 echelon 3 and 4 officials, 2 administrative staff members from Intala, 2 instructors, and 20 upgrading participants. Data were analyzed using an interactive analysis model, which includes data collection, data presentation, and drawing conclusions. The results show that the planning of the upgrading program at BBPVP Semarang involves identifying training needs, determining the training program, system, location, schedule, and methods, participant recruitment and selection, preparing human resources, training facilities, training schedule, and organization. The implementation of the upgrading program includes preparation, execution, assessment, responsibilities, and the issuance of training and competency certificates. The evaluation of the upgrading program includes aspects such as training materials, instructors, facilities and infrastructure, training materials, job readiness, meals, and boarding. In conclusion, the program management is running effectively and involves all elements, receiving positive appreciation from the participants.

Technology-driven Islamic propagation (da’wah) has emerged as a transformative paradigm in disseminating religious teachings in the digital age. The rapid advancement of social media platforms and artificial intelligence (AI) tools has significantly expanded the reach and influence of preachers, enabling more personalized and interactive engagement with diverse audiences. This study aims to explore innovative strategies in planning digital da’wah and to examine the challenges that arise, including the proliferation of misinformation, regulatory constraints, and the rise of underqualified digital preachers. Employing a qualitative approach through literature review and critical analysis of prior studies, this research highlights the potential of integrating data analytics, AI-based personalization, and digital media to enhance the effectiveness of Islamic messaging. However, it also underscores the pressing need for ethical frameworks and regulatory compliance to safeguard the integrity of religious content. The findings advocate for a structured, research-informed approach to digital da’wah that aligns with Islamic ethical principles while remaining adaptive to contemporary technological trends. Such an approach is essential to ensure that technological innovations serve as tools for meaningful religious engagement rather than sources of distortion or misinterpretation

This study aims to examine the investment performance of the Financial Institution Pension Fund (DPLK) and the associated challenges, utilizing investment performance data analysis. The findings indicate that the aggregate return on investment for DPLK over the past six years (2019-2024) has reached 6.09%. This is lower than the industry average of 6.99% during the same period. The annual investment performance of DPLK shows the following: 6.18% in 2024, 5.88% in 2023, 3.41% in 2022, 4.06% in 2021, 8.89% in 2020, and 8.17% in 2019. Despite some fluctuations, these results are still considered suboptimal. Key challenges facing DPLK include market volatility, interest rate risk, longevity risk (longer participant lifespan), balancing return and risk, regulatory compliance, inflation, limited education and financial literacy, changing investment trends, and human resource competencies. To improve performance, DPLK must enhance its investment management quality by addressing these challenges and adopting strategies that optimize returns while managing risks.

In today’s global economy, accurately predicting foreign exchange rates or estimating their trends correctly is crucial for informed investment decisions. Despite the success of standalone models like ARIMA and deep learning models like LSTM, challenges persist in capturing both linear and nonlinear dynamics in highly volatile exchange rate environments. Motivated by the limitations of these individual models and the need for more robust forecasting tools, this study proposes a hybrid ARIMA-LSTM model that integrates ARIMA’s strength in modeling linear trends with LSTM’s capability to capture nonlinear dependencies, using historical USD/NGN exchange rate data from the Central Bank of Nigeria (CBN) spanning 2001 to 2024. The research hypothesis posits that the hybrid ARIMA-LSTM model will significantly outperform standalone models in forecasting accuracy. By comparing these models against state-of-the-art approaches, the study highlights the advantages of hybridizing statistical and deep learning methods. The findings demonstrate that the hybrid model achieved the lowest Root Mean Squared Error (RMSE) of 2.216 and the highest R² of 0.998, indicating superior forecasting performance. This study fills a critical research gap by demonstrating the effectiveness of hybrid deep learning in financial time series forecasting, providing valuable insights for investors, policymakers, and financial analysts. Future research will extend this work by incorporating the latest dataset and evaluating model robustness during the recent surge in the Naira/Dollar exchange rate from 2023 to 2024.

Bali Province has a high population density, which has the potential to affect the distribution of infectious diseases such as diarrhea. However, mapping the distribution of diarrhea cases based on population density is still limited and does not meet good cartographic principles. Therefore, this study aims to present data on the distribution of diarrhea cases based on population density in Bali Province during the 2020-2022 period. This study used an analytic observational method with a cross-sectional design. The data used were secondary data from the Bali Province Health Profile and the Central Statistics Agency (BPS) for 2020-2022. Data analysis was carried out using geographic mapping with Geographic Information Systems (GIS) and statistical tests in the form of multiple linear regression and Spearman correlation. The results showed an increase in the strength of the relationship between population density and the number of diarrhea cases, as indicated by the rho value which increased from 0.1833 in 2020 to 0.6000 in 2022. However, this relationship was not statistically significant (p-value > 0.05), indicating that population density is not the only factor contributing to the increase in diarrhea cases. Other factors such as sanitation, access to clean water, and public awareness in maintaining hygiene also play a role in the spread of this disease. Although there is a trend that an increase in population density in Bali goes hand in hand with an increase in diarrhea cases, this relationship is not statistically strong enough. Therefore, a multidisciplinary approach is needed to address diarrhea cases, including improved sanitation facilities, public health education, as well as strengthening the spatial data-based disease monitoring system.

This research aims to develop an interactive web dashboard to support data analysis for vending machine sales. The dashboard is designed to facilitate the management of large datasets through intuitive visualizations and interactive features such as filtering, searching, and pagination. The development process involves several stages, including data collection, data cleaning, analysis, visualization, web design, implementation, and deployment using GitHub Pages. Technologies like HTML, CSS, JavaScript, Chart.js, and Grid.js are utilized to ensure efficiency and accessibility. The results of the research show that the dashboard effectively presents key information, such as sales trends, best-selling products, and payment method preferences, thereby supporting more accurate and data-driven strategic decision-making. However, the research has limitations in integrating predictive analytics. Future development is recommended to include predictive algorithms and test system performance on large-scale data. This solution is expected to contribute significantly to optimizing vending machine management and serve as a development model for similar applications in other business sectors.

This study aims to analyze the Pareto distribution of stock quality in 16 Apotek Kimia Farma outlets in Southeast Sulawesi during the period of January to October 2024, with a focus on inventory management based on ABC analysis. The research employed a descriptive quantitative design with a longitudinal (time series) approach, involving descriptive, trend, comparative, and correlation analyses to evaluate stock distribution and its stability. The findings show that, overall, the stock distribution in Kimia Farma outlets follows the Pareto principle, with category A accounting for an average of 73-86% of total sales value. However, significant variations were found across outlets, with outlets such as KF 004 and KF Hasanuddin Kendari showing high stability in category A distribution, while outlets like KF BULOG and KF Kolaka exhibited greater fluctuations. Seasonal patterns were also identified, with a decrease in category A percentages in April-May and October. Based on these findings, optimizing inventory management strategies based on Pareto distribution can enhance stock management efficiency, with recommendations to focus on category A, conduct regular evaluations of categories B and C, and adjust for seasonal patterns. This study suggests the implementation of more flexible inventory management strategies for each outlet to improve operational efficiency and customer service.

Global climate change has caused changes in rainfall patterns in various regions, including North Jayapura District, Papua Province. This study aims to analyze changes in rainfall characteristics in the region over the past two decades (2004–2023) using GSMaP satellite data. The analysis was carried out on the average monthly rainfall, annual rainfall, decadal trends, hourly rainfall intensity frequency, and daily rainfall duration. The results showed a decrease in annual rainfall accumulation in the 2014–2023 decades compared to the previous decade (2004–2013), with a difference of around 5,000 mm. The seasonal rainfall pattern still shows concentration in January to March, but with a significant decrease in these wet months. In addition, there was an increase in the frequency of short-duration rain (<1 hour) and moderate to very heavy rain, although the frequency was still relatively small. The high variability of annual rainfall also indicates an increasingly large climate. These findings indicate that North Jayapura District faces the risk of increasing extreme rainfall events and decreasing water availability in the long term. Therefore, adaptation efforts are needed through improving early warning systems, conservation of air catchment areas, and integration of satellite data and field observations in air resource management planning.

The rapid growth of financial technology (fintech) has significantly transformed the funding landscape for Small and Medium Enterprises (SMEs), offering innovative financial solutions beyond traditional banking institutions. This study presents a bibliometric analysis of fintech’s role in SME financing, identifying emerging trends and research gaps. Utilizing bibliographic coupling and co-occurrence network analysis, data from Scopus were analyzed to uncover the intellectual structure and evolution of research in this field. The results highlight key themes, including the integration of blockchain, peer-to-peer lending, financial inclusion, and crowdfunding in SME financing. A particular focus on Islamic finance and Islamic crowdfunding indicates a growing interest in alternative financing mechanisms that align with Sharia principles. Additionally, the study reveals an increasing academic focus on fintech adoption in developing economies, particularly in Indonesia and Nigeria, where access to capital remains a critical challenge. The findings underscore fintech’s role in democratizing financial access for SMEs, bridging funding gaps, and fostering economic growth. Future research should investigate regulatory frameworks, risk management strategies, and technological adoption models to optimize the impact of fintech on sustainability in SME financing.

Social media has become an effective marketing tool for young artists to introduce and promote their work to a broader audience. This study aims to analyze social media marketing strategies that can enhance the visibility and sales of artistic works. The research employs a literature review approach with content analysis of relevant studies published between 2018 and 2024. The findings indicate that effective social media marketing strategies for young artists consist of three main aspects: personal branding, algorithm optimization, and monetization strategies. Strong personal branding through visual consistency and storytelling has been proven to increase audience engagement and strengthen artists’ competitiveness on digital platforms. In terms of algorithm optimization, the use of short-form videos, consistent posting frequency, and SEO implementation play a vital role in expanding the reach of artistic content. Meanwhile, monetization strategies such as the utilization of e-commerce platforms, subscription-based models, and Non-Fungible Tokens (NFTs) have proven effective in converting audience engagement into a more stable source of income. This study contributes to the development of digital marketing literature within the creative industry and serves as a practical guide for young artists in optimizing social media as a marketing tool. The findings also provide a foundation for the development of more effective and adaptive digital marketing strategies in response to the dynamic and evolving trends of the creative industry.

Geopolitical tensions have evolved from peripheral risks to central drivers of global capital flows, disproportionately affecting Emerging Markets (EMs). This study provides a comprehensive bibliometric mapping of the academic landscape linking geopolitical dynamics, international finance, and investment in EMs. This study uses a dataset of 1,039 documents extracted from high-impact databases to analyze performance and conduct science mapping with R-Bibliometrix. The analysis covers publication trends, citation structures, and conceptual evolution over the last century, with a focus on the surge in literature post-2018. Results indicate an exponential growth in scientific production, peaking in 2024. The thematic structure reveals a shift from traditional debt crisis narratives (1990s) to contemporary concerns regarding sanctions, protectionism, and trade policy (2020s). Network analysis identifies three distinct clusters: (1) International finance and market mechanisms, (2) Political economy and development in the Global South, and (3) Institutional governance (IMF/World Bank). This paper bridges the gap between political science and financial economics by visualizing how international finance serves as the dominant anchor connecting developed economies (the USA and the UK) with key emerging markets (China and Indonesia) amid rising global fragmentation.