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This study examines the implementation of the Minister of Marine Affairs and Fisheries Regulation Number 7 of 2024 concerning the Management of Lobster, Crab, and Blue Swimming Crab in East Kalimantan. As a strategic policy, the regulation aims to ensure sustainable fisheries management through provisions on minimum catch sizes, restocking mechanisms, traceability systems, and law enforcement against illegal fishing and trade. Using an empirical juridical approach, this research collected primary data through questionnaires distributed to fisheries officers, coastal communities, fishers, and aquaculture business actors in East Kalimantan. The results show that the implementation of Regulation No. 7/2024 is generally categorized as moderately effective, with an average Likert score indicating limited but positive compliance. Respondents acknowledged the importance of sustainability principles embedded in the regulation; however, significant challenges persist, including insufficient formal socialization, weak field monitoring, lack of supporting facilities, and inconsistent coordination among implementing agencies (BARANTIN, PSDKP, and local fisheries officers). Qualitative responses highlight that many stakeholders still lack understanding of technical provisions such as minimum size requirements and restocking obligations. The study concludes that while the regulatory framework is normatively comprehensive and aligned with sustainable fisheries principles, its practical effectiveness remains constrained by institutional and socio-cultural factors. Strengthening community engagement, increasing inter-agency coordination, and enhancing enforcement capacity are crucial to optimizing the policy’s impact on the sustainable management of lobster, crab, and blue swimming crab resources in East Kalimantan.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

This study investigates the long-term impact of adaptive digital learning ecosystems on students' self-regulated learning (SRL) behaviors and academic persistence. Adaptive learning systems personalize the learning experience by adjusting content and feedback to meet individual students' needs, preferences, and performance. These systems enhance engagement, motivation, and learning outcomes through real-time adjustments and continuous feedback. The research aims to explore how adaptive learning systems influence SRL and academic persistence in university courses over time. Using a longitudinal quantitative design, the study tracks SRL behaviors and academic persistence at multiple points during the semester. Results show significant improvements in SRL behaviors such as goal setting, planning, self-monitoring, and reflection among students engaged with adaptive learning environments. These students exhibited greater autonomy, improved metacognitive awareness, and higher motivation. Additionally, students in adaptive systems demonstrated greater academic persistence, as indicated by more time spent on tasks, higher assignment completion rates, and sustained engagement. The findings suggest that adaptive learning platforms promote SRL and academic persistence by offering personalized, responsive learning experiences. Unlike static, non-adaptive environments, adaptive systems provide dynamic support, enhancing students' ability to regulate their learning and remain engaged despite challenges. The study concludes that adaptive learning systems are vital for long-term academic success, though further research is needed to assess the sustainability of these effects in various educational settings and among diverse student populations.

To improve the competitiveness and well-being of local communities, the growth of community-based village tourism requires the assistance of digital technology. Although Sugihen Village has significant potential for natural and cultural tourism, the use of digital technology in tourism administration and promotion is still minimal. The objective of this community service project is to utilize digital technology to transform community-based tourism in Sugihen Village. The approach used was Participatory Action Research (PAR), which included the stages of needs analysis, digital literacy training and capacity building, implementation of digital tourism technology, mentoring, monitoring, and evaluation. The results of the community service included increased community digital literacy, a stronger position of the Pokdarwis institution in tourism management, and a shift in community perspectives on the use of digital technology as a consequence of the community service activities. In addition to being a marketing tool, digital technology also empowers the community in independent and sustainable tourism management. This activity concluded that the independence of the Sugihen Village community can be strengthened and community-based tourism can be transformed through the use of digital technology accompanied by a participatory approach.

The rapid growth of e-commerce has increased the strategic role of freight forwarding and delivery service companies in maintaining the efficiency of supply chains. However, the increasing complexity of delivery processes also raises various operational risks, such as package damage, loss, delivery delays, and distribution errors. This study aims to analyze the implementation of risk management in freight forwarding companies operating in Brebes Regency and to assess the effectiveness of the risk management practices applied. This research employs a descriptive qualitative approach, with data collected through in-depth interviews, observations, documentation, and questionnaires. Informants were selected using purposive sampling, involving company management, operational staff, couriers, and customer service personnel. Data analysis was conducted using the Miles and Huberman interactive model and supported by average risk score analysis. The results show that most aspects of risk management fall into the high and very good categories, with an overall average score of 3.89. This indicates that the company has implemented risk management effectively, particularly in incident evaluation, risk measurement, and cooperation with insurance providers. Nevertheless, improvements are still needed in reducing the frequency of operational risks and enhancing the use of digital technology for risk monitoring to ensure a more comprehensive and sustainable risk management system.

Government expenditure is a great reason in economic stability and its impact on the balance of payments is dire. In this light, this paper seeks to use the time series analysis method and the ARDL model to investigate the association between the balance of payments of Iraq and the public spending within the 2004-2023 period. The Eviews 13 software was used to analyse it. The findings show that there is a positive association between spending by the people and balance of payment especially at the short run. The latter findings indicate that the efficiency of government expenditure reform is a necessary tool to accomplish the expansion and close the balance of payments deficit. This study highlights the importance of strategic fiscal policies and government spending in achieving a balanced economy and sustainable growth. Additionally, it emphasizes the need for continuous monitoring and adjustment of public spending to ensure its alignment with national economic objectives. The findings contribute to the understanding of fiscal policy implications in developing economies, especially in the context of Iraq’s economic challenges.  

This study was conducted to address contamination risks in Refill Drinking Water Depot (DAMIU) operations when sanitation, equipment maintenance, and quality control are not consistently implemented. This emphasizes the critical importance of systematic risk monitoring and review processes to ensure water quality and safety. The research aims to analyze the implementation of risk monitoring procedures, identify operational risks at each processing stage, and evaluate the effectiveness of risk mitigation strategies at Depot Isi Ulang Air Galon Barokah. A descriptive qualitative approach was employed, utilizing data collection techniques including in-depth interviews, direct field observations, and documentation analysis. The findings reveal that monitoring and risk review processes are conducted routinely, encompassing raw water quality checks, equipment sanitation protocols, and customer gallon container inspections. No significant risks were identified during the study period, as potential hazards such as microbiological contamination and equipment malfunction were effectively prevented through timely filter replacement and daily equipment cleaning procedures. Overall, the depot's risk management system operates effectively and consistently maintains water quality standards, ensuring product safety for consumers.

This article aims to provide a comprehensive literature review on how professional ethics can serve as an effective strategy to prevent fund misuse within organizational financial management. Professional ethics is viewed as a set of moral values, behavioral norms, and professional standards that guide financial managers to perform their duties with honesty, responsibility, and without conflicts of interest. In the context of financial management, these duties include recording, budgeting, monitoring, and reporting financial activities, all of which require accuracy and transparency. The study highlights five main principles of professional ethics: integrity, objectivity, professional competence, confidentiality, and professional behavior. These principles clarify rules, strengthen accountability, and ensure that financial processes comply with established standards. The literature review shows that applying professional ethics not only encourages individuals to act correctly but also enhances responsibility, improves performance, and strengthens financial oversight. Integrity and objectivity play a crucial role in preventing report manipulation, budget inflation, and fund misuse, as these principles demand moral courage and fair decision-making. Professional competence ensures that every financial process is carried out accurately and in accordance with regulations, while confidentiality protects sensitive information from misuse. Professional behavior emphasizes adherence to laws, organizational policies, and professional standards. The article also identifies several supporting factors that enable the effective implementation of professional ethics, such as strong internal policies, leadership commitment to integrity, an ethical workplace culture, layered supervision systems, and continuous ethics training. Conversely, common challenges include weak internal controls, limited understanding of ethics, organizational pressure, conflicts of interest, and inconsistent application of ethical standards. Therefore, this article underscores that integrating professional ethics into organizational financial policies, procedures, and management systems is a key step in preventing fund misuse and strengthening stakeholder trust in the organization’s transparency and accountability.

This paper discusses the role of the global minimum tax in addressing tax avoidance by multinational corporations in Indonesia. This policy is the result of an agreement between the OECD/G20 (Organization for Economic Co-operation and Development) in the Base Erosion and Profit Shifting (BEPS) 2.0 project, which aims to reduce global tax avoidance practices by multinational corporations (MNEs). With a minimum rate of 15%, the GMT is expected to create fiscal justice and strengthen the tax base in developing countries like Indonesia. This research uses a qualitative approach based on a review of literature from the OECD, IMF, and academic journals. The analysis shows that the implementation of the GMT has positive potential in increasing state revenues, but also poses administrative challenges and the risk of reducing investment competitiveness. The Indonesian government needs to adjust tax regulations and strengthen fiscal administration capacity to optimize the benefits of this policy. This study also confirms the importance of international cooperation in the successful implementation of the GMT and reducing the potential for tax avoidance by multinational corporations. Furthermore, regular monitoring and evaluation are needed to assess the impact of this policy on the Indonesian economy and to ensure that the implementation of the GMT does not hinder economic growth and investment in strategic sectors.

Objective– This article aims to comprehensively examine the main types of food crop pests and their attack patterns through a systematic literature review approach. The research focuses on the dynamics of pest attacks, changes in ecological patterns due to climate change, and advances in modern identification technology that enable more accurate early detection. This study also highlights the significance of new paradigms of pest identification based on artificial intelligence (AI), genomics, and landscape mapping in supporting food security at the regional and national levels. Design/methodology/approach– This study used the Systematic Literature Review (SLR) method for scientific publications from 2015–2025 from reputable sources such as Scopus, Web of Science, PubMed, ScienceDirect, SpringerLink, Taylor & Francis, Wiley, AGRIS, and Google Scholar. Of the 326 articles identified in the initial stage, 30 articles in English and Indonesian were selected through a screening process based on strict inclusion–exclusion criteria. All articles were then analyzed using thematic coding techniques to produce an in-depth, evidence-based synthesis. Findings– The study produced four key findings: (1) there are five dominant pests in global food crops, namely Thrips tabaci, Spodoptera exigua/frugiperda, Helicoverpa armigera, Nilaparvata lugens and Sitophilus oryzae; (2) attack patterns are strongly influenced by temperature, humidity, pesticide resistance, and monoculture; (3) modern identification technology AI, drone imagery, multispectral sensors, and DNA Barcoding have increased detection accuracy to 94–98%; and (4) community-based early warning systems accelerate field response and reduce the risk of crop failure. Practical implications– These findings provide a scientific basis for local governments, agricultural extension workers, and farmers to gradually adopt pest identification technology and strengthen integrated monitoring systems at a regional scale. Authenticity/value– This article offers a new conceptual model of “Pest Identification Pyramid – Attack Pattern – Early Warning System” that integrates pest biology, digital technology, and community response to improve national food security.

This community service program was initiated in response to the need to strengthen collaboration between parents and schools in fostering character development and noble morals among elementary school students. The main issue addressed was the lack of structured and sustainable educational collaboration between parents and the school in supporting character-building practices. The program aimed to develop a participatory and reflective collaboration model through a systematic parenting initiative. A qualitative participatory approach based on community engagement was employed, consisting of needs assessment, joint discussion and planning, parenting program design, workshops and Focus Group Discussions (FGDs), mentoring and monitoring, as well as evaluation and reflection. The results highlight significant improvements in two-way communication between parents and teachers, increased collective awareness of the family’s strategic role in character education, and the establishment of a regular communication forum as a new social structure within the school community. Positive changes were also observed in parenting practices, including the implementation of positive discipline and empathetic communication at home aligned with school values. The emergence of local parent leaders further strengthened program sustainability. Overall, the participatory educational collaboration model proved effective in promoting social transformation and reinforcing sustainable character and moral development among students.

This study discusses the comparison of traditional and modern management accounting methods with the aim of analyzing the characteristics, advantages, and disadvantages of each method and providing practical guidance for corporate decision-making. The research method uses a literature review approach, collecting and synthesizing various recent studies related to Activity Based Costing (ABC), Target Costing, and Balanced Scorecard (BSC), and comparing them with traditional methods such as job order costing and process costing. The analysis results show that traditional methods are simpler and easier to implement, but less accurate in calculating costs and less relevant for strategic decision-making. In contrast, modern methods offer higher accuracy through detailed cost allocation, comprehensive performance monitoring, and data-based decision-making support, although they require greater implementation complexity and resources. This study concludes that the choice of method must be adjusted to the characteristics of the company and the complexity of business activities, so as to optimally improve cost efficiency and profitability.

This study aims to evaluate the public transportation service policy of Trans Jatim Bus Corridor 2, operating on the Mojokerto–Surabaya route, using the six evaluation indicators proposed by William N. Dunn: effectiveness, efficiency, adequacy, equity, responsiveness, and appropriateness. Employing a qualitative descriptive approach, data were collected through interviews, direct observations, and secondary data analysis. The findings reveal that the Trans Jatim Corridor 2 service has significantly contributed to improving accessibility and mobility for the people of East Java. In terms of effectiveness, the service meets transportation policy objectives by offering strategic routes, consistent schedules, and accessible bus stops. Efficiency is demonstrated through optimal utilization of limited fleets and operational costs, while still meeting high passenger demand. Regarding adequacy, the service is generally sufficient; however, overcrowding during peak hours indicates the need for capacity improvements. Equity is reflected in the widespread distribution of bus stops, although disparities remain in the availability of facilities and route information across several stops. The service shows high responsiveness through quick handling of passenger complaints via applications and social media. Additionally, service appropriateness is evident in its punctual operations supported by GPS-based monitoring and real-time information through the TRANSJATIM-AJAIB application. Overall, the evaluation shows that Trans Jatim Corridor 2 provides effective, efficient, and responsive public transport services, yet requires improvements in capacity and equitable distribution of facilities to achieve optimal service quality.

Tangerang City has the most applications in Indonesia, with 222 applications. All of these applications are supported by more than 100 servers located in the data center of the Tangerang City Communication and Information Agency. The large number of servers and applications that are managed brings up new problems in the midst of increasing complex cyber threats, especially in government data centers. One of them is how to monitor and respond quickly when there is an attack on the existing system. The implementation of a cyber security system based on Wazuh, Shuffle, and YARA is able to monitor threats in realtime and automate responses against attacks. Wazuh acts as a log-based monitoring and detection platform and behavior analysis, Shuffle is used to automate incident response through integrated workflow, and YARA is applied for signature-based malware identification. The PPDIOO (Prepare, Plan, Design, Implement, Operate, Optimize) method used in this research is used as a framework in designing and evaluating the system. From the research conducted, it is expected that Wazuh successfully monitors anomalies that occur on the server which will then be forwarded to Shuffle to automate the next steps to be taken. YARA integrated with Wazuh also successfully detects and quarantines malicious files that enter the server automatically based on the available signature list.

Traffic performance on urban road segments is strongly affected by vehicle volume and travel time, particularly during peak periods. This study analyzes the relationship between travel duration and the total number of vehicles passing along Otto Iskandar Road as an illustration of urban traffic conditions. Data were collected through field surveys, focusing on two main variables: average vehicle travel time and total traffic volume. Statistical analysis was performed using IBM SPSS Statistics, including normality testing and the Wilcoxon Signed Rank Test to identify potential differences between the observed variables. The results show a difference in average values between travel duration and vehicle volume; however, this difference is not statistically significant at the 95% confidence level (p = 0.180). These findings indicate that increases in traffic volume do not always lead to proportional increases in travel time, although they can still influence the stability and efficiency of traffic flow. The results are consistent with previous studies, such as Halim (2021), who reported that U-turn movements affect speed and traffic performance, and Handayani et al. (2024), who found that parking activities and vehicle maneuvers reduce road capacity. Other studies also highlight the impact of side friction and traffic flow variations on speed and saturation levels. Overall, this study emphasizes the importance of managing vehicle flow and monitoring travel time in urban transportation planning and traffic management.

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.

The development of electronic commerce through marketplace platforms presents new challenges in civil law enforcement, particularly concerning the platform's liability for the circulation of counterfeit goods. This study aims to analyze Shopee's legal responsibility as a provider of electronic transaction services regarding the sale of counterfeit goods, based on Article 1365 of the Civil Code concerning Unlawful Acts and its relation to consumer protection principles as regulated in the Republic of Indonesia Law Number 8 of 1999 concerning Consumer Protection. The research uses a normative juridical method with a legislative approach and literature study, including provisions in the Law on Electronic Information and Transactions and Government Regulation Number 71 of 2019 on the Implementation of Electronic Systems and Transactions. The research findings indicate that although Shopee does not act as a direct seller, the platform still has a legal obligation to provide a reliable, secure, and responsible electronic system. Negligence in verifying sellers and monitoring products has the potential to fulfill the elements of unlawful acts, particularly the elements of fault and causal relationship with consumer losses. This study emphasizes that Shopee qualifies as a business actor in the context of consumer protection, thereby bearing both preventive and repressive responsibilities to ensure transaction security and prevent the circulation of counterfeit goods on its platform.

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..

Stunting and wasting are nutritional problems in toddlers that remain a double burden of malnutrition in Indonesia and have an impact on the quality of health and future human resource development. Monitoring the nutritional status of toddlers is generally carried out using anthropometric indicators, but the use of this data is still limited to descriptive analysis. This study aims to apply the K-Means algorithm in clustering infants vulnerable to stunting and wasting based on anthropometric indicators, so that groups of infants with different levels of nutritional vulnerability can be identified. The dataset used consists of infant data with variables of gender, age (months), height (cm), and weight (kg). The research stages included data preprocessing, encoding categorical variables, data normalization, determining the optimal number of clusters using the Elbow and Silhouette Score methods, and analyzing the characteristics of each cluster. The evaluation results showed that the optimal number of clusters was four. Each cluster has different anthropometric characteristics and distributions of stunting and wasting status, ranging from groups with relatively normal nutritional conditions, groups with a tendency toward overnutrition, to groups that are vulnerable to acute and chronic malnutrition. These clustering results provide a more comprehensive and segmented mapping of toddlers, which can be used as a basis for formulating more targeted and data-driven nutrition policies and interventions.