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This study aims to map the intellectual structure and research trends in entrepreneurial innovation using bibliometric analysis based on Scopus data. A total of 891 documents published between 1972-2025 were analyzed through Bibliometrix and Biblioshiny, employing techniques such as bibliographic coupling, co-authorship, and thematic mapping. The results reveal four major clusters: (1) innovation theory and entrepreneurial development, (2) business model innovation and digital transformation, (3) regional innovation systems and policy frameworks, and (4) sustainability and green entrepreneurship. Emerging themes include artificial intelligence (AI), generative AI applications, and digital entrepreneurship education, indicating a shift toward multi-level and interdisciplinary integration. Influential documents and authors were identified, highlighting their role in shaping the knowledge base. The findings suggest that entrepreneurial innovation research is evolving toward digitalization, sustainability, and policy-driven ecosystems, offering opportunities for longitudinal and mixed-method studies. This study contributes by providing a comprehensive overview of the field, identifying gaps, and proposing future research directions to strengthen theoretical and practical advancements.

This study aims to determine the effect of digital media use on children's academic achievement. Rapid technological developments have made digital media a crucial part of the modern educational process. Using a qualitative descriptive approach, this study provides an in-depth description of how digital media is used in learning activities and its impact on student learning outcomes. Data were collected through observations, interviews, and documentation of students, teachers, and parents involved in the technology-based learning process. The results indicate that digital media plays a significant role in increasing children's interest and motivation in learning, enriching their knowledge base, and helping them understand the material more effectively. However, the study also found that excessive, unsupervised use of digital media can lead to decreased concentration, the development of procrastination, and decreased social interaction among students. Therefore, the use of digital media needs to be balanced with guidance and supervision from teachers and parents to ensure its use remains aligned with educational goals. In conclusion, digital media has significant potential to improve children's academic achievement when used wisely, purposefully, and in accordance with each student's individual learning needs.

The performance of investigators in resolving criminal cases is one of the key indicators of police effectiveness. However, the Semendawai Suku III Police Sector still faces challenges in monitoring case resolution due to the lack of an integrated reporting system and minimal documentation of investigators’ knowledge. This study aims to develop an interactive dashboard system based on Knowledge Management to assist in monitoring case resolution performance and support data- and knowledge-based decision-making processes. The research employs the Prototype method, involving several stages: needs analysis, system design, system development, testing, and refinement. The system was developed using the PHP programming language and MySQL database. The implementation results show that the dashboard can display data on criminal reports, case resolutions, and pending cases in an informative and integrated manner. In addition, the knowledge base feature functions as a medium for storing and sharing investigators’ experiences (lesson learned), allowing field knowledge to be reused by other officers when handling similar cases. Overall, the implementation of the interactive dashboard system based on Knowledge Management at the Semendawai Suku III Police Sector successfully improves work efficiency, strengthens the transparency of investigative performance, and builds a foundation for sustainable organizational learning within the police environment.

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus and transmitted through the bite of Aedes aegypti mosquitoes. In 2023, 48 cases of DHF were reported in the Kebun Lada Public Health Center area, reflecting a high incidence rate and limited medical resources in managing the cases. This situation emphasizes the need for an alternative solution that can support a fast and accurate diagnostic process. This study aims to develop an expert system for diagnosing DHF using the Case-Based Reasoning (CBR) method. CBR functions by comparing the symptoms experienced by patients with previous cases stored in the knowledge base, thereby producing relevant diagnostic recommendations. The proposed system is implemented as a web-based application using PHP as the programming language and MySQL as the database management system. The expected outcomes of this study are to assist medical personnel in reducing diagnostic time, improving the accuracy of decision-making, and increasing the effectiveness of health services in primary healthcare facilities. In addition, the system is designed to provide wider access for the community to recognize early symptoms of DHF, which can contribute to preventive actions and reduce the risk of severe complications. Thus, the developed expert system has the potential to become a practical solution to overcome the shortage of medical personnel and enhance public health awareness.

This study aims to design and develop an expert system to assist in diagnosing diseases in dairy cattle at Cibugary Farm using the Forward Chaining method. The background of this research lies in the limited knowledge of farmers in identifying early symptoms of diseases, which often leads to delays in medical treatment and negatively affects dairy cattle productivity. To address this issue, an expert system was designed to replicate the reasoning process of a human expert through a knowledge base containing diagnostic rules derived from observable symptoms. The Forward Chaining method was chosen because of its capability to trace facts from known symptoms toward a conclusion regarding the type of disease affecting the cattle. The system was developed by incorporating common disease symptoms, inference rules, and a decision-making mechanism that simulates expert analysis. Testing was carried out on several diagnostic scenarios to evaluate the accuracy and efficiency of the system. The results of the study indicate that the expert system can provide an initial diagnosis quickly and accurately, producing outputs consistent with expert assessments. This functionality assists farmers in making timely decisions regarding appropriate medical interventions, thereby reducing treatment delays and minimizing the risk of disease transmission within the herd. Consequently, the Forward Chaining-based expert system is expected to serve as an innovative solution to improve dairy cattle health management and support sustainable livestock productivity at Cibugary Farm.

Preeclampsia is one of the most serious complications in pregnancy, characterized by hypertension and proteinuria, and it poses a significant risk of maternal and fetal morbidity and mortality if not detected and managed promptly. Early detection is crucial, yet clinical diagnosis often faces challenges due to the variability of symptoms and uncertainty in medical decision-making. To address this issue, this study aims to develop an expert system for diagnosing preeclampsia by employing the Dempster-Shafer method, which is known for its ability to handle uncertainty and incomplete information in complex domains such as healthcare. A case study was conducted at Bidadari General Hospital, where data on clinical symptoms and patient medical records were collected and analyzed. The development process of the expert system followed systematic stages, including knowledge acquisition from obstetrics specialists, designing the knowledge base, constructing inference rules, and integrating the Dempster-Shafer algorithm for decision support. The system was subsequently tested using real-case scenarios of pregnant women suspected of having preeclampsia. Evaluation results demonstrated that the system achieved an accuracy rate of 92% in differentiating between preeclampsia and eclampsia, based on belief and plausibility measures combined with symptom analysis. These findings indicate that the proposed system can effectively support medical personnel by providing diagnostic recommendations with a high degree of reliability. In addition, the system offers efficiency in the clinical workflow by minimizing diagnostic errors and reducing delays in treatment initiation. Therefore, this expert system has the potential to become a valuable clinical decision support tool for early detection, risk assessment, and management of preeclampsia. Future development may focus on expanding the knowledge base, integrating real-time patient monitoring data, and enhancing usability to ensure broader applicability in diverse healthcare settings.

The background of this research is the importance of pursuing knowledge based on good morals. The formation of Islamic character based on good morals is an important aspect that needs to be emphasized in students' lives, both in the school environment and in society. This is because good character is a reflection of the success of education, especially education based on Islamic values. However, at SMP Ma'arif NU 2 Kemranjen, several student behavioral problems were still found that indicate the suboptimal formation of Islamic character. Some students appeared unprepared when submitting memorization targets (deposits), were still outside the classroom when learning began, and arrived late to participate in the 0-hour habituation program. These problems indicate the importance of evaluating the effectiveness of the 0-hour habituation program that has been implemented by the school as an effort to form Islamic character. Therefore, this study aims to determine whether there is an effect of the 0-hour habituation program on the formation of students' Islamic character. This study used a quantitative approach with a descriptive survey method. The population in this study were all 107 eighth-grade students of SMP Ma'arif NU 2 Kemranjen from three classes. Data collection techniques were conducted through documentation of the 0-hour habituation program scores given by teachers and the Islamic character formation questionnaire completed by students. The data analysis technique used was descriptive statistics, with the calculation of averages, percentages, and data categorization. The results of the study indicate that the 0-hour habituation program did not have a significant impact on the formation of students' Islamic character. Nevertheless, this program is still being implemented because it is believed to have various benefits that can support the long-term character education process. This research contributes to schools in evaluating and developing more effective strategies for forming students' Islamic character.

The process of seeking knowledge does not merely involve intellectual intelligence but also requires emotional and spiritual strength, one of which is cultivated through patience. In fact, in today's fast-paced, instant era, many knowledge seekers often overlook the aspect of patience. Therefore, this research aims to explore the meaning and urgency of patience in seeking knowledge based on Q.S. Al-Kahfi verses 65-70, which focus on the story of Prophet Musa and Khidr. The method used in this research is a literature review with a qualitative approach, analyzing the relevant verses as well as classical and contemporary interpretations that serve as primary sources. The analysis of this article demonstrates that patience in seeking knowledge is a prerequisite for attaining true knowledge, functioning as an inner strength to overcome obstacles, maintain consistency in learning, and shape the character of a genuine learner who is not only intellectually intelligent but also spiritually mature. These findings are relevant to the integration of manners, ethics, and spirituality in contemporary Islamic education.

Blepharitis or inflammation of the eyelids is one of the common eye diseases, characterized by inflammation of the edges of the eyelids that can cause discomfort, irritation, and even visual disturbances. This disease can be chronic with recurrent symptoms such as red eyes, itching, watering, and the appearance of crust on the eyelashes. Proper and prompt diagnosis is necessary so that medical treatment can be carried out effectively and further complications can be prevented. This study aims to design and build an expert system based on the Fuzzy Logic method in helping diagnose blepharitis. The fuzzy method was chosen because it is able to handle the uncertainty of symptom data that often arises in the medical diagnosis process. This system is developed through the identification of the common symptoms of blepharitis, then processed using the fuzzy membership function to determine the type of disease based on the degree of symptom onset. The output of the system is in the form of the results of the diagnosis of blepharitis along with initial treatment recommendations that can be used as a reference for users. The results of the system test show that the application of fuzzy logic is able to provide diagnosis results that are quite accurate, fast, and easy to understand both medical personnel and the general public. This system is expected to help increase public awareness about the importance of early detection of blepharitis, as well as being a tool in the initial medical decision-making process. However, the limitations of this study lie in the limited amount of data and coverage of the type of blepharitis, so further development is needed, both in expanding the knowledge base, increasing the variety of symptoms, and improving system interaction with users.

Health is important for every human being. Health, education and income of each individual are three important factors that greatly influence the quality of human resources. Anxiety disorders are a significant mental health problem and can affect an individual's quality of life. Early detection of anxiety disorders is important to provide appropriate intervention and prevent the development of more serious conditions. This research aims to develop an expert system that is able to detect anxiety disorders based on symptoms reported by penggunas. This system uses a forward chaining method and a knowledge base compiled from medical literature and consultations with mental health experts. Several stages of system creation include collecting data on symptoms of anxiety disorders, preparing a knowledge base, implementing a forward chaining inference algorithm, and kuatating the system using test data and expert consultation. The expert system developed in this research is able to provide accurate initial information regarding the symptoms of anxiety disorders in adolescents based on the symptoms input by the pengguna. By utilizing a knowledge base and appropriate diagnostic rules, the system can identify key symptoms that indicate the presence of an anxiety disorder.