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Student dropout remains a significant challenge for higher education institutions as it impacts academic quality, educational management efficiency, and students' success in completing their studies. Therefore, an approach that can identify students at risk of dropping out is necessary so that timely academic interventions can be made. This study aims to develop a dropout detection model using an Artificial Neural Network (ANN). The data used come from a publicly available higher education dataset, ensuring research reproducibility. Data preprocessing steps were carried out to improve data quality before modeling, and the Synthetic Minority Over-Sampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) was applied to address class imbalance issues. The ANN model's performance was evaluated using accuracy, precision, recall, F1-score, and area under the ROC curve (ROC-AUC). The test results show that the ANN model can provide excellent predictive performance in detecting at-risk students. The application of SMOTE-ENN also proved to enhance the model’s sensitivity toward the minority class, as indicated by improvements in recall and F1-score. These findings indicate that the developed ANN model has the potential to be used as a student dropout detection system to support data-driven decision-making and strategy development within higher education institutions.

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 transformation has significantly reshaped human resource management (HRM) through the adoption of Human Resource Information Systems (HRIS), artificial intelligence (AI), big data analytics, e-learning platforms, and remote work technologies. Although these innovations improve efficiency and decision-making, they also generate ethical challenges related to data privacy, algorithmic bias, transparency, and employee monitoring. This article examines the role of professional ethics in HRM within the context of digital transformation, highlighting both emerging challenges and potential opportunities. This study employs a conceptual research approach supported by a comprehensive literature review of scholarly works on HRM, professional ethics, and digitalization. The analysis focuses on core ethical principles such as integrity, fairness, responsibility, professionalism, and confidentiality, and evaluates their implementation in digital HR practices. The findings indicate that unethical use of digital technologies may lead to discrimination, reduced employee trust, and violations of individual rights, particularly through biased AI-based recruitment systems and opaque performance evaluation mechanisms. However, digital transformation also offers opportunities to strengthen ethical HR governance. The use of ethical data management, algorithmic audits, digital transparency, and e-learning-based ethics training can enhance accountability and fairness in HR processes. The study concludes that integrating professional ethics with digital HRM is essential for developing human-centered, sustainable, and trustworthy organizations in the digital era.

Primary Healthcare Facilities (Fasilitas Kesehatan Tingkat Pertama, FKTP) represent the first level of contact in the healthcare system and play a central role in infection prevention and control. Despite mandatory Infection Prevention and Control (IPC) training in Indonesia, evidence regarding its effectiveness in improving cognitive abilities among primary healthcare workers remains limited. This study aimed to evaluate the effectiveness of IPC training in enhancing the cognitive abilities of healthcare workers in FKTP. A quasi-experimental study with a one-group pretest–posttest design was conducted involving 91 healthcare workers who participated in IPC training across three cohorts in 2024. The training was delivered online through a Learning Management System and consisted of structured learning modules accompanied by a pre-test and a final quiz. Cognitive improvement was assessed using paired samples t-tests, while the magnitude of training impact was evaluated using Cohen’s dz effect size. The results showed statistically significant improvements in cognitive scores across all cohorts (p < 0.001), with mean score increases ranging from 16.10 to 23.35 points. Effect size analysis revealed large to very large effects, with an overall Cohen’s dz of 1.19, indicating substantial and practically meaningful cognitive gains. In conclusion, IPC training was effective in improving cognitive competence among FKTP healthcare workers. These results reinforce the value of well-structured training programs as an essential component of efforts to strengthen infection prevention capacity in primary healthcare settings.

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

Artificial intelligence (AI) is currently a rapidly developing technology in all fields, particularly in finance and the military. This study aims to examine the application of Artificial Intelligence (AI) technology to support financial report analysis and internal control within Indonesian Navy (TNI AL) work units. Along with the development of information technology, AI has the potential to provide innovative solutions to improve efficiency, accuracy, and transparency in state financial management, particularly in a military environment that demands high accountability. The research method used was descriptive qualitative with a case study approach in several work units within the Indonesian Navy. Data were obtained through interviews, observations, and a review of relevant documents and literature. The results indicate that the use of AI, such as machine learning and data analytics, can identify unusual financial transaction patterns, predict potential irregularities, and improve the effectiveness of internal oversight. However, the implementation of this technology still faces challenges, such as limited digital infrastructure, the need for human resource training, and the need for policies that support sustainable digital transformation. This study recommends the gradual and strategic integration of AI as part of the reform of the Indonesian Navy's financial management system.

Early Childhood Education (PAUD) holds a very important role in shaping children’s character, moral values, and foundational knowledge. As an effort to support the success of education, good quality learning outcomes are required as a benchmark for children’s holistic development. In this context, educator management becomes one of the keys to improving the quality of learning outcomes. PAUD Al-Qur’an (PAUDQU) is an early childhood education institution that prioritizes Al-Qur’an education and Al-Qur’an memorization (tahfidz) as its institutional uniqueness. The findings of this research show that the educator management implemented in PAUDQU in Setu Bekasi District is quite good, as almost all elements within the scope of educator management have been carried out by the school. However, there are several obstacles found in its implementation, such as the educators’ competencies that have not been maximized, particularly in Al-Qur’an recitation (tahsin) and tahfidz, as well as the promotion, compensation, and educator assessment systems that are not yet well-structured. KIPAS EVA, an acronym for Competence Implementation Evaluation Appreciation, was developed with the expectation of being easier to remember, thus supporting the process of applying educator management in the future. This management strategy emphasizes educator planning by strengthening competencies, professional learning implementation by educators, continuous educator evaluation, and providing appreciation to increase educator motivation.

Waste has become a serious environmental problem in Indonesia, which continues to increase along with population growth. The issue of waste management poses serious challenges for the environment, especially in the process of separating organic and inorganic waste. In the field of computer vision, recognising the type and shape of waste through camera images remains a challenge due to variations in shape, colour, and complex lighting conditions. Therefore, this problem utilises Deep Learning technology, which is expected to be widely applied in Indonesia, especially in large cities with high waste volumes. This study aims to distinguish between organic and inorganic waste using the Convolutional Neural Network (CNN) method based on digital images. The developed CNN model was trained to recognise the visual patterns of each type of waste and tested to measure its accuracy. The test results show that the CNN-based classification system is capable of achieving an accuracy rate of 95%, thus proving the effectiveness of this method in supporting artificial intelligence-based automatic waste sorting systems.

The quality of education is a crucial issue that continues to receive attention in facing the challenges of globalization, digitalization, and the demands of 21st century competencies. Quality improvement efforts are not only related to the quality of students' learning outcomes, but also concern the role of leadership, management effectiveness, and the implementation of a quality assurance system. This study aims to identify strategies to improve the quality of education that have been studied in the literature for the last five years (2020-2025) and answer two research questions, namely: (1) what quality strategies are predominantly discussed in the literature, and (2) how the role of leadership, strategy management, and quality assurance in supporting the quality of education. The method used is literature review with a qualitative approach. Data was obtained from Google Scholar and Publish or Perish (PoP) databases using the keyword "quality strategy" with a publication year limit of 2020–2025. The search yielded 30 articles, which then through a screening and selection process obtained 5 relevant articles for analysis. The results of the analysis show three main trends: (1) the strategic role of school principals in building a quality culture, (2) the importance of evaluation and implementation of quality management in a sustainable manner, and (3) the synergy of visionary leadership with the quality assurance system  as the basis for sustainable quality. This study concludes that the education quality strategy requires the integration of these three aspects. Going forward, further research is needed to develop integrative models across approaches and broader educational contexts.

The persistent issue of high youth unemployment among Senior High School (SHS) graduates necessitates cultivating an entrepreneurial mindset through formal education, making Entrepreneurship Education (EE) a national strategic imperative. This Systematic Literature Review (SLR) critically analyzed 15 empirical articles, selected via the strict PRISMA protocol, to evaluate the effectiveness of diverse EE curriculum models in enhancing students' Entrepreneurial Interest (Intention) and Attitude. The key finding is that EE implementation is substantially effective only when executed through active, experience-based pedagogical models such as Experiential Learning, Project-Based Learning (PjBL), and the Teaching Factory which are superior in fostering practical competency and significantly boosting Entrepreneurial Self-Efficacy and positive Attitude, consistent with the Theory of Planned Behavior. Successful implementation also critically depends on robust operational factors, including structured curriculum management (e.g., PDCA cycle) and the availability of competent teachers. In conclusion, the findings provide a strong evidence-based framework, recommending that policymakers prioritize experiential models and integrate modern elements like Digital Literacy and Non-cognitive Skills to produce SHS graduates with genuine entrepreneurial readiness.

This research is motivated by the importance of managerial skills in improving business productivity and efficiency, particularly in the entrepreneurial sector, which faces the challenges of competition, market changes, and limited resources. The objective of this study is to analyze the role of managerial skills development and its contribution to optimizing entrepreneurial performance. The method used is a Systematic Literature Review (SLR), which examines various relevant scientific publications and then categorizes the findings to obtain a comprehensive overview of effective strategies for improving managerial competency. The results indicate that technical skills, human relations skills, and conceptual skills play a significant role in supporting decision-making processes, resource management, and adapting business strategies to business dynamics. The findings also indicate that training, mentoring, continuous learning, and the use of technology are development strategies capable of significantly increasing productivity and efficiency. The implications of this research emphasize that improving managerial skills is a strategic necessity for entrepreneurs to strengthen competitiveness, increase operational effectiveness, and promote business sustainability. Furthermore, this research provides a basis for the development of more structured training programs and supporting policies to strengthen the managerial capacity of entrepreneurs.

Innovation is the process of optimizing various resources to produce more efficient and sustainable solutions. This study aims to examine the relationship between resources and innovation strategies in supporting sustainable entrepreneurship through a Systematic Literature Review (SLR) approach. A total of 25 scientific articles were analyzed using PRISMA guidelines to examine the role of resources such as human, technological, information, financial, and physical resources in the context of product, process, and managerial innovation. The research findings indicate that the success of innovation is strongly influenced by the organization's ability to utilize and synergize its resources, especially in the development of internal capabilities such as tacit knowledge, research and development activities, and digital technology. Human resource creativity is the main driver of product innovation, while process innovation is strengthened by the application of technology and a learning culture. Managerial innovation is heavily influenced by entrepreneurial orientation and dynamic capabilities in responding to change. The implementation of sustainable innovation strategies such as green, digital, and social innovations also increases business competitiveness through cross-sector collaboration. This study emphasizes the importance of integrated resource management and innovative strategies to achieve sustainability, especially in the small and medium enterprise sector.

The digital era demands that organizations be fast-moving, adaptable, and innovative. With the advancement of information technology, changes in work methods, global competition, and stakeholder demands are becoming increasingly complex. Knowledge Management (KM) plays an important role as a strategic mechanism for identifying, acquiring, storing, sharing, and utilizing knowledge to improve organizational effectiveness and efficiency. In this context, knowledge management becomes one of the important elements for organizations to enhance performance. Knowledge management is not just about collecting data or information, but how organizations can store, share, create, and utilize knowledge to gain a competitive advantage. This article aims to analyze the importance of knowledge management for organizational performance in the digital age, including how the digital era changes the dimensions of knowledge management, how knowledge management contributes to organizational performance, the challenges faced, and their implications. The data obtained for this study were gathered from observations thru interviews with relevant parties and a literature review study by examining the results of empirical research from the past five years (2020–2025). The method used was descriptive literature analysis of 15 scientific articles from accredited national journals. The analysis focuses on the relationship between knowledge management dimensions (knowledge creation, storage, sharing, and application) and organizational performance indicators (financial performance, innovation, productivity, and customer satisfaction). The study results show that the implementation of knowledge management significantly contributes to improving organizational performance, both directly thru increased efficiency and effectiveness of work processes, and indirectly thru strengthening a culture of innovation and organizational learning. This article asserts that an organization's success in the digital age is not solely determined by its ability to adopt technology, but also by its ability to manage and leverage knowledge as a strategic resource. Therefore, knowledge management needs to be systematically integrated into the organization's digital strategy, accompanied by strengthening a learning culture, human resource training, and adaptive information technology systems.

Education is a fundamental right that must be fought for by every child in the nation, and throughout its development, it continues to undergo innovations and evaluations toward better quality. One educational model that is growing in Indonesia is the boarding school system, which has its own characteristics, advantages, and challenges. This study aims to describe the social studies (IPS) teachers’ strategies in boarding school–based learning, the implementation of IPS learning within the boarding school environment, and the obstacles faced by IPS teachers at MTs Darul Hikmah. This research employs a qualitative approach with a case study design. The study was conducted at Pondok Modern Darul Hikmah Tulungagung, with seventh-grade students as research subjects. Data collection techniques included observation, interviews, and documentation. The results show several factors that influence the low effectiveness of IPS learning in boarding schools, including the dense institutional activity schedule that reduces students’ learning focus, teachers’ limited mastery of the subject matter and classroom management, and the dual curriculum implemented simultaneously within the institution. Efforts to improve IPS learning effectiveness include enhancing teacher discipline when entering and leaving the classroom, utilizing audio-visual learning media, and connecting learning materials with current issues to make the lessons more relevant and engaging. Furthermore, the study reveals several advantages of boarding school–based education, such as fostering students’ independence, developing social awareness, providing deeper religious instruction, and integrating both general and religious education. Thus, IPS learning in boarding schools has the potential to develop more optimally with the support of appropriate learning strategies and more structured institutional management.

The low effectiveness of student organizational performance in designing and managing structured work programs presents a major challenge to strengthening the leadership culture at MTsN 1 Probolinggo. This community service program aims to enhance students’ managerial and leadership capacities through performance mentoring for the Intra-Madrasah Student Organization (OSIM), based on innovations in learning management. The methods employed include training sessions, workshops, and continuous mentoring, all designed collaboratively with school stakeholders. The activity stages began with a needs analysis and problem mapping, followed by the development of training modules, the implementation of organizational management training, and interactive workshops aimed at developing adaptive and applicable work programs. The proposed solutions encompass strengthening foundational understanding of organizational management, enhancing the role of members in program planning, and facilitating the implementation of OSIM activities that support the creation of an active learning environment. Evaluation was carried out through pre-tests and post-tests to measure comprehension improvement, field activity observations, and assessments of work program achievement. The results indicate significant improvements in participants’ cognitive and practical skills, particularly in the planning, execution, and evaluation of OSIM programs that are now more structured and goal-oriented. In conclusion, this community service activity successfully reinforced student leadership competencies through an innovative approach to managing student organizations, while also promoting the development of a sustainable culture of collaborative work. Based on the outcomes, it is recommended that this mentoring model be replicated in other madrasahs and that OSIM leadership development programs be systematically integrated into extracurricular curricula to ensure the continuity and expansion of the positive impacts achieved

The purpose of this study is to analyze the strategic role of the Green Human Resource Information System (Green HRIS) in bridging the transformation of human resource management with the demands of Environmental, Social, and Governance (ESG) principles, specifically examining how Green HRIS contributes to sustainable HR practices, organizational performance, and digital HR transformation. Using a Systematic Literature Review (SLR) approach, this study identifies, evaluates, and synthesizes prior research by conducting a structured search across major academic databases—Scopus, Web of Science, Springer, Elsevier, and Wiley—for publications from 2020 to 2025 that address green HRM, HR digitalization, sustainable HR practices, and ESG integration. The review process includes screening titles, abstracts, and full texts, extracting key data, and categorizing findings into environmental, social, and governance dimensions. The results demonstrate that Green HRIS strengthens ESG implementation by reducing paper usage, lowering carbon emissions, and promoting sustainable HR practices such as digital recruitment and e-learning, while also improving governance through enhanced transparency, accountability, regulatory compliance, and real-time reporting. Empirical evidence indicates that Green HRIS fosters employee engagement, organizational innovation, and the development of green competitive advantages. Practically, the study highlights how organizations, policymakers, and HR managers can utilize Green HRIS to optimize digital transformation and meet ESG requirements, thereby reinforcing legitimacy and long-term competitiveness within the green economy. This research offers originality as one of the first systematic reviews addressing Green HRIS in the ESG era, integrating theories such as the Resource-Based View, Technology Acceptance Model, and organizational sustainability theory, while also mapping trends, best practices, and gaps for future research.

This comparative study analyzes the models of character education by comparing the implementation of Social and Emotional Learning (SEL) in Finland and the Akhlak (Moral) Curriculum within Islamic Religious Education (PAI) in Indonesia. Modern education demands not only cognitive intelligence but also emotional and moral maturity, highlighted by rising issues like bullying and mental health problems in adolescents. Finland's success is attributed to the strong integration of SEL, a psychological method focusing on five core competencies (self-awareness, self-management, social awareness, relationship skills, and responsible decision-making) that fosters a positive school climate. In contrast, Indonesia's PAI Akhlak Curriculum aims to form character rooted in theological-spiritual values (piety and obedience). Using a descriptive qualitative comparative method through literature review and content analysis, the study found that despite sharing the core objective of cultivating moral character , their foundations differ: SEL is secular-humanistic for well-being, while Akhlak is theological-spiritual. Furthermore, Finland's system employs a low-pressure, integrated, formative evaluation, supported by teacher autonomy, while Indonesia's PAI implementation faces challenges from a dominant focus on quantitative cognitive assessment. The study recommends that PAI adopt the structured SEL framework as a methodological bridge to translate Akhlak values into concrete student behavior and promote active, affect-focused learning.

Anesthesiology and emergency care require rapid and accurate clinical decision-making. Artificial intelligence (AI) offers substantial potential to support triage, monitoring, and decision-making in critical and emergency anesthesiology settings. This scoping review maps the use of AI in clinical decision-making and emergency patient management in anesthesiology and identifies existing research gaps. A literature search was conducted in ScienceDirect, PubMed, Cochrane Library, and Google Scholar for articles in Indonesian or English published between 2020 and 2025. Study selection followed Tricco’s scoping review framework, and methodological quality was assessed using Joanna Briggs Institute (JBI) tools. Ten articles met the inclusion criteria. AI was shown to improve triage accuracy and efficiency (predictive accuracy up to 99.1% and reductions in waiting time of around 30%). Machine learning models effectively predicted critical care needs and emergency risk, while AI-based clinical decision support systems (CDSS) enhanced the speed and quality of clinical decisions. Key challenges include data bias, ethical and privacy issues, clinician readiness, and integration with hospital information systems. AI and CDSS have strong potential to improve patient safety and clinical decision-making in emergency anesthesiology. Strengthening AI literacy, supportive regulation, and transparent, context-appropriate predictive models are needed for safe and sustainable implementation.

The purpose of this study is to investigate the strategic role of air transportation management in Military Operations Other Than War (MOOTW), particularly in archipelagic contexts such as Indonesia, where rapid humanitarian response, territorial surveillance, and civil–military cooperation are essential for resilience. By applying a Systematic Literature Review (SLR), this article synthesizes findings on humanitarian logistics, technological transformation, and policy frameworks for strengthening national defense readiness. Design/methodology/approach – This study employs a qualitative Systematic Literature Review (SLR) methodology guided by PRISMA principles, analyzing 30 scholarly contributions from 2009–2025, including international peer-reviewed journals, Routledge and Springer volumes, arXiv preprints, and Indonesian academic publications.Results highlight that strategic air  transportation is indispensable for disaster relief, medical evacuation, and supply delivery in archipelagic nations. The adoption of AI, machine learning, UAVs, and reinforcement learning has enhanced responsiveness and equity in humanitarian supply chains. However, persistent challenges include aging fleets, interoperability constraints, and fragmented civil–military coordination. The study underscores the need for modernization of air assets, institutionalized civil–military collaboration, and integration of AI-based routing and command systems. Strengthening these aspects can enhance Indonesia’s resilience and preparedness in MOOTW scenarios. This article uniquely bridges global research on data-driven air power with Indonesian defense perspectives, proposing a scalable strategic framework for air transportation management that advances archipelagic resilience.

Electronic Medical Records (EMR) are crucial for the quality of healthcare services, but compliance remains a challenge. This study analyzed factors influencing compliance among healthcare workers at a type B private hospital in North Jakarta using a quantitative cross-sectional design with 58 respondents through total sampling. Data were obtained through questionnaires related to individual factors (age, length of service, knowledge), psychological factors (attitude, motivation), and organizational factors (leadership, work design, rewards). The results showed that 72.4% of respondents were compliant. The chi-square test revealed a significant relationship between compliance and age (p=0.042), length of service (p=0.000), knowledge (p=0.001), attitude (p=0.017), motivation (p=0.002), leadership (p=0.046), and rewards (p=0.010), while work design was not significant (p>0.05). Multivariate analysis found age, length of service, knowledge, and leadership as the dominant factors. Healthcare workers with younger age, shorter tenure, good knowledge, positive attitudes, high motivation, good leadership, supportive work designs, and adequate reward systems tend to have higher compliance rates. Improving compliance in completing EMRs depends not only on individual factors but also requires organizational support through effective leadership and management systems. Recommended interventions include improving digital literacy, regular training, strengthening a work culture that emphasizes the importance of medical documentation, and implementing peer learning strategies among healthcare workers to accelerate adaptation and share best practices in completing EMRs.