Publication Search

Background: Stunting remains a major public health concern in Indonesia, affecting children’s growth, development, and long-term health outcomes (Sari, 2020). Early childhood stunting can lead to cognitive delays, poor school performance, reduced productivity in adulthood, and increased risk of chronic diseases (Hapsari & Wulandari, 2019). Understanding the interaction of demographic, socioeconomic, and health-related factors is essential to reduce stunting prevalence and improve child health outcomes (Yuliani, 2019). Objective: This study aimed to analyze the factors influencing the incidence of stunting among children attending the Sagerat Public Health Center in Bitung City. Methods: A quantitative, observational study with a cross-sectional design was conducted. Thirty children were selected using purposive sampling based on inclusion criteria such as age under five years and available anthropometric data. The Wilcoxon signed-rank test was applied to examine the relationship between these factors and stunting incidence, with a significance level set at p < 0.05. Results: The analysis showed that 30 children (100%) in the study were at risk of stunting based on height-for-age measurements, with 20 children (90%) confirmed as stunted and 10 children (10%) having normal height. These results suggest that maternal education, socioeconomic status, nutrition, and access to healthcare are strongly associated with stunting in this population. Conclusion: Factors such as maternal education, socioeconomic status, dietary intake, and access to healthcare significantly influence the incidence of stunting among children at the Sagerat Public Health Center. Comprehensive community-based programs are recommended to reduce stunting prevalence and improve long-term health outcomes for children.

The emergence of ChatGPT and other large language models has introduced unprecedented opportunities and challenges in educational settings. This study examines the dual nature of ChatGPT's integration into classroom environments, analyzing both its potential as an educational tool and the ethical concerns it raises regarding academic integrity. Through a mixed-methods approach combining surveys, interviews, and content analysis, this research investigates how educators and students perceive and utilize ChatGPT, the implications for learning outcomes, and the effectiveness of current academic integrity policies. Results indicate that while ChatGPT offers significant pedagogical benefits including personalized learning support and enhanced accessibility, it simultaneously poses risks to traditional assessment methods and raises questions about authorship and original thought. The study proposes a framework for responsible AI integration in education that balances innovation with academic integrity, emphasizing the need for updated policies, enhanced digital literacy, and redesigned assessment strategies. These findings contribute to the ongoing discourse on AI in education and provide practical recommendations for educators, administrators, and policymakers navigating this transformative technology.

This article explores legal accountability for flood disasters occurring in several regions of Sumatra, which are widely alleged to result from large-scale logging activities. The analysis is conducted within the framework of the Indonesian legal system, with particular emphasis on contract law. This study adopts a literature-based research method by examining statutory provisions, legal doctrines, and relevant scholarly publications. The analysis demonstrates that flood events should not be understood solely as natural occurrences, but rather as ecological consequences arising from the failure to fulfill contractual obligations embedded in forest utilization permits. From a contract law perspective, forestry concessions establish binding legal relationships that impose environmental protection duties on permit holders in accordance with the principle of pacta sunt servanda. Logging activities that exceed authorized limits may therefore be classified as contractual default (wanprestasi) and, at the same time, constitute unlawful acts that cause harm to the state and affected communities. Accordingly, this study underscores the necessity of strengthening environmental protection clauses within concession agreements and applying strict liability principles to enhance legal responsibility and prevent recurring environmental harm.

Background: In the domain of corporate governance, the separation of ownership and control generates significant agency conflicts, primarily manifesting as Earnings Management (EM). Traditional reactive auditing methods fail to detect manipulation concealed within unstructured data, leading to high agency costs and diminished stakeholder trust. Objective: This study proposes an "AI Proactive Monitoring Model" utilizing Generative Artificial Intelligence to fundamentally enhance the monitoring mechanisms of Agency Theory. Methods: The research employs a qualitative conceptual framework analysis. It synthesizes Agency Theory with the Technology Acceptance Model (TAM) and Systemic Risk Theory to construct a novel strategic governance model. Results: The proposed model shifts governance from periodic sampling to real-time, continuous analysis of total data populations. By cross-referencing structured financial data with unstructured communications (e.g., emails, contracts), the system generates "Risk Narratives" that contextualize anomalies and flag opportunistic behavior immediately. Conclusion: The integration of AI significantly reduces information asymmetry and moral hazard by creating a "panopticon" effect. However, successful implementation requires distinct regulatory frameworks to manage the systemic risks associated with algorithmic reliance.

This study analyzes the economic phenomenon known as the "Purbaya Effect" in the Indonesian capital market during the second half of 2025. This phenomenon is characterized by a significant surge in the Jakarta Composite Index (IHSG), which broke the All-Time High (ATH) record 21 times within four months following the appointment of Purbaya Yudhi Sadewa as Minister of Finance. Using a mixed-methods approach combining quantitative market data analysis and qualitative policy review, this research finds that the "Purbaya Effect" is driven by aggressive liquidity injection policies (Rp 200 trillion), institutional trust built during his tenure at LPS, and strong narrative economics. However, this study also identifies significant risks related to exchange rate volatility and potential economic overheating. The findings suggest that while the "Purbaya Effect" successfully restored short-term investor confidence, long-term sustainability depends on the balance between growth acceleration and macroeconomic stability.

This study examines the influence of income inequality and poverty on the prevalence of stunting in ten provinces across Sumatra Island during the 2016–2024 period. Using a panel dataset of 90 observations and applying a Fixed Effect Model, the results indicate that both income inequality—measured by the Gini Ratio—and poverty have a positive and significant effect on stunting. The Gini Ratio shows a coefficient of 1.46 (p = 0.0002), while poverty records a coefficient of 6.28 (p = 0.0140), jointly explaining 52% of the variation in stunting prevalence. Spatial analysis further supports these findings, with Moran’s I values exceeding 0.40, suggesting strong spatial autocorrelation and clustering of high-stunting regions. High-risk clusters—Aceh, Jambi, and Bengkulu—are characterized by Gini Ratios above 0.33 and poverty levels exceeding 12%, reinforcing the existence of an intergenerational poverty–stunting trap, particularly influenced by urban–rural disparities (rural 53.3% vs urban 34.9%). The study highlights that specific nutrition interventions such as supplementary feeding, micronutrient programs, and breastfeeding promotion are insufficient without accompanying structural reforms addressing economic inequality. Therefore, multisectoral convergence strategies are required, including expanded conditional cash transfers, progressive local taxation reforms, nutrition-focused social assistance, and universal basic infrastructure to accelerate stunting reduction toward the 14.2% target by 2029.

Increased production in the lubricant industry has become a significant issue because it generates production waste in the form of packaging, plastic, and paper waste as a direct consequence of manufacturing activities. Proper and systematic waste management is essential to reduce environmental impacts, comply with regulations, and support responsible and sustainable business practices. Lubricant production waste can damage the ecosystem and surrounding environment if not managed properly, so continuous innovation is needed to minimize environmental risks and reduce potential economic losses for the company. This study aims to analyze and implement effective waste management strategies using a combination of qualitative and quantitative approaches, including in-depth interviews with relevant stakeholders and analysis of secondary data obtained from company records. The waste management process begins with several waste reduction programs applied during the production stage to limit waste generation. Furthermore, the waste storage facility (TPS) available on the company’s premises, covering an area of 59 m², is capable of accommodating approximately 60% of the total waste that must be processed internally before further treatment or disposal.

This study aims to analyze the internal and external conditions of Karel Workshop in Wairhubing, Sikka Regency, using a SWOT approach (Strengths, Weaknesses, Opportunities, and Threats) as a basis for formulating business development strategies. The research employed a qualitative descriptive method with data collected through in-depth interviews with the owner, employees, and customers, as well as secondary data from documents and official statistics. The findings indicate that Karel Workshop possesses several strengths, including meticulous service quality, strong interpersonal relationships with customers, and mechanic skills acquired through self-learning. However, the workshop also faces weaknesses such as limited equipment, the absence of a structured management system, and a lack of digital promotion. Opportunities that can be utilized include high public interest in motorcycle modification, potential motorcycle painting services, a strategic location, and possible partnerships with spare-part suppliers. Meanwhile, the main threats include rising spare-part prices, competition among workshops, and occupational safety risks. Overall, the study recommends strengthening digital promotion, improving business management, enhancing workshop facilities, and implementing safety standards to support the sustainable development of Karel Workshop. 

This study examines the impact of industrial activities in Bontang City, a major center for natural gas processing and fertilizer production, which potentially increases heavy metal contamination in coastal waters. Specifically, the research focuses on the accumulation of lead (Pb) and copper (Cu) in Anadara granosa, a benthic bivalve selected as a bioindicator due to its capacity to accumulate pollutants. The study aims to analyze Pb and Cu concentrations in Anadara granosa tissues collected from the waters of Selangan Hamlet, Bontang City, as an indicator of local environmental quality. The research was conducted over a six-month period, involving shellfish and water sampling from four observation stations. Heavy metal analysis was performed using Atomic Absorption Spectrophotometry, and the results were evaluated against relevant environmental quality standards, including Government Regulation of the Republic of Indonesia Number 22 of 2021. The findings revealed the presence of Pb and Cu at all sampling stations, with varying concentrations. The highest Pb and Cu levels were recorded at the South Station, while the lowest concentrations were observed at the East and North Stations. Overall, the measured concentrations of both metals significantly exceeded established safety limits, indicating serious risks to aquatic ecosystems and human health. These results confirm Anadara granosa as an effective bioindicator of heavy metal pollution and highlight the urgent need for further studies and mitigation efforts.

This study aims to analyze the internal and external conditions of Karel Workshop in Wairhubing, Sikka Regency, using a SWOT approach (Strengths, Weaknesses, Opportunities, and Threats) as a basis for formulating business development strategies. The research employed a qualitative descriptive method with data collected through in-depth interviews with the owner, employees, and customers, as well as secondary data from documents and official statistics. The findings indicate that Karel Workshop possesses several strengths, including meticulous service quality, strong interpersonal relationships with customers, and mechanic skills acquired through self-learning. However, the workshop also faces weaknesses such as limited equipment, the absence of a structured management system, and a lack of digital promotion. Opportunities that can be utilized include high public interest in motorcycle modification, potential motorcycle painting services, a strategic location, and possible partnerships with spare-part suppliers. Meanwhile, the main threats include rising spare-part prices, competition among workshops, and occupational safety risks. Overall, the study recommends strengthening digital promotion, improving business management, enhancing workshop facilities, and implementing safety standards to support the sustainable development of Karel Workshop. 

Sleep disorders are a common complaint among pregnant women, especially in the third trimester, and have the potential to negatively impact maternal well-being and pregnancy outcomes. Physiological, hormonal, and psychological changes during pregnancy increase the risk of decreased sleep quality, while pharmacological therapy options are limited due to safety considerations. Therefore, safe and easy-to-apply non-pharmacological interventions are needed. This study aimed to analyze the effectiveness of lavender aromatherapy in overcoming sleep disorders in third trimester pregnant women at the Bongo Nol Community Health Center. The study used a quasi-experimental design with a one-group pretest–posttest approach. The sample consisted of 30 third trimester pregnant women selected purposively. Sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI) before and after the administration of lavender aromatherapy through inhalation for seven consecutive days. Data analysis was performed using the Wilcoxon signed-rank test. The results showed a significant decrease in PSQI scores after the intervention, indicating an improvement in sleep quality among the respondents. Lavender aromatherapy proved to be effective as a non-pharmacological intervention in reducing sleep disturbances in pregnant women in their third trimester. In conclusion, lavender aromatherapy can be considered a safe, simple, and applicable complementary therapy to improve the sleep quality of pregnant women in primary health care.

This study examines the comparative volatility of gold and Bitcoin over the period January 2020 to August 2025, using monthly data and employing descriptive statistics, the Augmented Dickey-Fuller (ADF) test, GARCH (1,1), and the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model estimated with EViews 13. The results show that Bitcoin is characterized by extreme and persistent volatility, reflecting its speculative nature, whereas gold remains stable and functions as a conventional safe-haven asset. Correlation analysis indicates that the relationship between gold and Bitcoin is generally weak but dynamic, as the strength and direction of their co-movements change across different market conditions. These findings highlight the potential role of gold as a hedge and Bitcoin as a speculative diversifier, offering insights for portfolio diversification and risk management. These results also suggest that investors should carefully consider their risk tolerance and investment horizon when allocating assets between traditional and digital commodities.

Advances in accounting technology and economic decision-making have resulted in many important risk assessment tools that support the decision making process. In the past, risk assessment depended on personal opinions and manual analysis, which often resulted in inaccurate estimates and delays in decision-making. However, with these risk assessment tools, we can now identify, analyze, and mitigate various risks that can affect financial and economic outcomes in a more accurate and efficient manner. This study aims to discuss technology in the form of risk assessment tools as an aid in decision-making and identify trending topics related to the use of risk assessment tools from 2020-2025, particularly in Indonesia. This study uses a qualitative approach and collects data from the scopus database of 1.827 articles selected from 2020 to 2025. In understanding research from the scopus database, bibliometric analysis was used in this study and using analysis tools such as R Studio and Vos Viewer. The results of the study show a significant increase in publications related to risk assessment tools with a growing trend toward international collaboration. The dominant themes that emerged include decision making, artificial intelligence, climate change, health risks, and financial markets. In addition, the increasing number of publications with international collaboration trends shows that the use of risk assessment tools has become a global standard that Indonesia needs to adapt. The government can use these findings as a basis for formulating policies that are more adaptive to technological developments.

This study aims to test the effectiveness of the implementation of a simplified Mini-QHSE (Quality, Health, Safety, Environment) system in improving working conditions at the Syams Handicraft woven craft MSME in Pati Regency, Central Java. The study used a quantitative method with a one-group pretest–posttest design involving 47 workers as respondents. Data collection was conducted through questionnaires and observations before and after the twelve-week intervention. The results showed that the implementation of the Mini-QHSE system was able to significantly reduce the level of occupational safety and health risks and significantly improve worker welfare. Further analysis revealed that the Mini-QHSE system made a significant contribution to variations in OHS risks and worker welfare. The dimension of safe work procedures proved to be the most influential factor in reducing work risks, while the training aspect was the main factor in improving worker welfare. Based on these findings, it can be concluded that the Mini-QHSE system designed according to the capacity and characteristics of MSMEs is effective in creating a safer and more prosperous work environment. This model has the potential to be applied to similar MSMEs with contextual adjustments as a practical solution in managing quality, safety, and occupational health in the micro-business sector.

Toko Jahit SA’aminah is a business engaged in tailoring services and the sale of sewing supplies that still manages data manually using record books. This condition causes several problems, such as slow data recording, the risk of data loss or damage, difficulties in monitoring the status of tailoring work and inventory, as well as obstacles in preparing tailoring service and sales reports. This study aims to design and develop a web-based tailoring service and sales information system to optimize the effectiveness and efficiency of operational performance. The system development method used is the waterfall method, which includes the stages of requirements analysis, system design using UML (Use Case Diagram, Activity Diagram, and Class Diagram), implementation using the Laravel framework with the PHP programming language and MySQL database, as well as system testing using the Black Box Testing method. The results show that the developed system is able to facilitate the management of tailoring service and sales data, monitor the status of tailoring work, check the availability of sewing supplies, and accelerate the preparation of tailoring service and sales reports to be submitted to the owner of Toko Jahit SA’aminah.

The management of scientific works at the Faculty of Law, Universitas Jambi, is still carried out manually through physical archives and simple digital storage without an integrated information system. This condition causes limited access, slow information retrieval, and a high risk of document loss and damage. This study aims to develop a web-based scientific repository system to improve the efficiency, accessibility, and security of managing academic works. The system was developed using the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and evaluation. Data collection was conducted through interviews, observations, and questionnaires involving librarians, lecturers, and students. The system was implemented using the Laravel framework with a MySQL relational database to support document storage, metadata management, access control, and search functionality. The results show that the developed repository system is able to facilitate the submission, verification, publication, and retrieval of scientific works effectively. User acceptance testing indicates that the system meets user needs in terms of usability, functionality, and accessibility. The implementation of this system contributes to improving academic information management and supports digital transformation at the Faculty of Law, Universitas Jambi.

The digital era has transformed the financial sector through the integration of FinTech, making it more susceptible to increasingly complex cyber threats. As these risks rise, there has been a significant increase in academic research to better understand the cybersecurity challenges within the financial sector. This study aims to explore the development of cybersecurity research globally within this field. By utilizing bibliometrics, the research analyzes literature data collected from the Scopus database over the last five years. The analysis was conducted using VOSviewer and RStudio to identify dominant clusters, with cybersecurity and network security as the central themes linking various sub-fields, including artificial intelligence, cyberattacks, and phishing. The findings reveal areas of extensive research and highlight gaps that require further exploration. This study provides valuable insights for researchers and professionals in the cybersecurity field, offering a roadmap for future investigations and the identification of underexplored areas that need attention. Ultimately, this research contributes to advancing knowledge in the financial sector’s cybersecurity landscape and assists in shaping future research directions.

This study investigates the relationship between corporate governance characteristics, financial structure, and Enterprise Risk Management (ERM) disclosure in Indonesian non-financial firms. Focusing on manufacturing companies listed on the Indonesia Stock Exchange in 2023, the analysis examines whether board size, the proportion of independent commissioners, and leverage influence the extent of ERM disclosure. Using a quantitative approach, multiple linear regression is applied to secondary data obtained from firms’ annual reports. The findings indicate that board size and the proportion of independent commissioners do not have a significant effect on ERM disclosure, while leverage exhibits a positive and significant relationship. This result suggests that firms with higher debt levels are more inclined to enhance risk disclosure as a mechanism to address information asymmetry and demonstrate accountability to investors and creditors. The study contributes to the ERM and corporate governance literature by providing evidence from an emerging market setting and highlighting the practical importance of financial structure in shaping risk transparency, offering relevant insights for corporate decision-makers and regulators to strengthen sustainable risk management practices.

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.

This study develops an interpretable machine learning model to predict the settlement status of Hajj fees in Jambi Province, Indonesia. Utilizing the XGBoost algorithm on a dataset of 4,332 prospective pilgrims from 2025, the research addresses the critical challenge of class imbalance where only 28.5% of samples are labeled "Unsettled". The baseline XGBoost model achieved a ROC-AUC of 0.7778, with a recall of 0.3482 for the minority class. SHAP (SHapley Additive exPlanations) analysis was employed to interpret model predictions, revealing that financial features specifically NILAI_VA (Virtual Account Value), JML_SETORAN (Deposit Amount), and JML_PELUNASAN (Settlement Amount) are the most significant factors influencing repayment risk, with negative SHAP values indicating increased default probability. The findings demonstrate that an interpretable XGBoost framework can provide both predictive accuracy and actionable insights for policymakers, enabling targeted interventions such as flexible payment schemes and enhanced financial monitoring for high-risk pilgrims..