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Queues are a common phenomenon and are often encountered in various aspects of daily life, especially in places that provide public services. One of the places that is inseparable from the queue problem is Indomaret Jimbaran, Bali. The large number of buyers who send data to Indomaret Jimbaran during peak hours causes the queue rate to be high. This is due to the limited number of service facilities and the time provided to serve each visitor is limited so that the cashier cannot serve buyers optimally. Therefore, the researcher is interested in researching whether the service facilities provided by Indomaret Jimbaran are effective and efficient so that they can satisfy customers.Using the Kolmogorov-Smirnov test, it was found that the appropriate distribution for the interarrival time of customers is the Gamma distribution, while the service time follows the 3-parameter Weibull distribution. The queueing model used is . The results show that having three cashiers is sufficient to handle the level of customer traffic at Indomaret Jimbaran retail store.

One of the digital marketing strategies commonly used by companies and Micro, Small, and Medium Enterprises (MSMEs) to introduce their products and achieve optimal profits is through the use of social media (Hidayat et al., 2022). The rapid development of information technology, particularly the internet and social media, has significantly changed consumer behavior and the way businesses operate. Digital marketing provides strategic opportunities for business actors, including lower promotional costs, increased brand awareness, and the potential to boost sales (Dwivedi et al., 2020). However, many MSMEs have not yet fully utilized digital marketing strategies through social media. The lack of digital literacy and limited understanding of effective marketing strategies remain major challenges in business development. To address these issues, a community service team from the Faculty of Economics, Universitas Islam Majapahit, implemented a mentoring program for the MSME “DnD Donat” located in Dlanggu District, Mojokerto Regency. The objective of this program was to provide the partner with the knowledge and practical skills necessary to design and implement an effective digital marketing strategy using social media as the primary promotional channel. Through this mentoring activity, the partner received education on the fundamentals of digital marketing, how to create engaging promotional content, and how to effectively use social media platforms such as Instagram and WhatsApp Business. The results of the program showed that the partner gained an understanding of digital marketing strategies and began to implement them in their business activities. The positive impact of the program was reflected in a noticeable increase in both sales volume and income over a specific period following the implementation of the new marketing approach. This program is expected to serve as a model for developing digital capabilities among other MSMEs and to support the broader digital transformation in the micro and small business sector in a sustainable manner.

Through seminars and leadership training, the goal of this community service project is to identify an effective leadership model that will enhance the work ethic of Jumantik employees in Setu Village, East Jakarta. Participating in the Effective Leadership program in enhancing work ethics were 100 Jumantik cadres from the village, the heads of the Development of Family Welfare of Setu Village, Cipayung District, and RW in the Setu Village neighborhood of East Jakarta. Lecture and coaching techniques are used in the training process. As a result of this training, a leader must employ effective leadership techniques to enhance work ethic when overseeing Jumantik cadres. To improve the motivation, accountability, and discipline of Jumantik cadres, one successful leadership model employs a transformational and participatory leadership style. This approach encourages open communication and fosters a sense of belonging among the cadres. It has been demonstrated that cadres who regularly monitor mosquito larvae perform better under leadership that offers clear guidance, emotional support, and active involvement in decision-making. Furthermore, the training emphasizes the importance of collaboration and teamwork, which are essential for achieving common goals. Jumantik cadre supervisors are advised to participate in leadership training since effective leadership would facilitate the development of a more inclusive and cooperative leadership model. Thus, it is anticipated that the effective sub-district eradication of Dengue Hemorrhagic Fever (DHF) will be facilitated by the adoption of the appropriate leadership paradigm, ultimately leading to healthier communities and improved public health outcomes. This is expected to make the public more vigilant and protected from potential online fraud in the future. Furthermore, increased legal literacy is expected to create a safer and more trustworthy digital transaction ecosystem for all parties.

Anxiety experienced by pregnant women is a common psychological problem and can have a significant negative impact on maternal health and fetal development. This anxiety can be caused by various factors, including physical changes, concerns about the delivery process, and new responsibilities as parents. Therefore, it is important to find effective ways to address this anxiety. One non-pharmacological approach that can be implemented is the use of lemon aromatherapy, which is known to have calming and relaxing properties. This activity aims to increase the knowledge and skills of pregnant women in utilizing lemon aromatherapy as a method to reduce their anxiety. This activity was held in June 2025 at PMB Lismarini, involving 20 pregnant women who actively participated in this program. The method used in this activity included several stages: education on the benefits of aromatherapy, demonstrations on how to use it, hands-on practice using lemon aromatherapy, and evaluation conducted through pre- and post-tests to measure changes in participants' anxiety levels. The results of this activity showed a significant decrease in participants' anxiety levels. Previously, many pregnant women were in the severe and moderate anxiety categories, but after participating in the activity, they shifted to mild anxiety and even no anxiety. Lemon aromatherapy has been proven effective in providing relaxation and reducing emotional tension experienced by pregnant women. Therefore, this activity is expected to become a promotional educational model in maternal health services. Through this approach, it is hoped that pregnant women will better understand and utilize aromatherapy as a way to maintain their mental health during pregnancy. Furthermore, this activity can also provide health workers with insight into the importance of non-pharmacological approaches in supporting the mental health of pregnant women.

Mental health is a crucial aspect of modern life, with stress and anxiety being among the most common and impactful psychological disorders. This research proposes a stress and anxiety monitoring system based on the Internet of Things (IoT), integrating biometric sensors and Deep Neural Networks (DNN) for early detection and in-depth analysis. The system is designed using MAX30102 (heart rate and SpO₂), GSR (Galvanic Skin Response), and DS18B20 (body temperature) sensors, managed by an ESP32 microcontroller and communicating through the MQTT protocol. Physiological data is collected in real-time, formatted in JSON, and transmitted to both Android and web-based applications for visualization. The DNN model is developed using the TensorFlow framework with a layered architecture and ReLU activation functions to classify four mental states: relaxed, calm, anxious, and highly stressed. The training dataset comprises both primary and secondary data, including the WESAD dataset. Model performance is evaluated through k-fold cross-validation, showing high accuracy and strong generalization capabilities. The results indicate that the integration of sensor technology and deep learning significantly improves the effectiveness of stress and anxiety detection compared to traditional methods. This system demonstrates great potential for the development of AI-based wearable devices for autonomous, real-time, and adaptive mental health monitoring. 

Digitalization has been a key catalyst for change in the global business landscape, driving the transformation of companies' strategies, organizational structures, and operational models. For small and medium enterprises (SMEs), digital technology opens up great opportunities to expand market reach and compete in the international arena more effectively. Technologies such as e-commerce, social media, big data analytics, and cloud-based platforms enable SMEs to break through geographical boundaries, facilitate access to global customers, and accelerate product distribution and marketing processes. This paper explores the current academic literature that discusses the influence of digital technology on the internationalization of SMEs. A total of five relevant studies were analyzed to identify common patterns, challenges, and successful strategies in the use of digital technology. The results of the study show that the adoption of digitalization not only improves operational cost efficiency, but also encourages business model innovation, such as digital platform-based marketing, cross-border virtual collaboration, and the development of a more adaptive global business ecosystem. In addition, the ability to utilize digital technology gives SMEs a competitive advantage through access to real-time market information, personalization of offers, and more targeted branding strategies. However, the research also found obstacles, such as limited human resources, technological infrastructure, and digital literacy among SMEs. Therefore, synergy is needed between business actors, the government, and academia to support the acceleration of digital technology adoption through training, mentoring, and policies that support innovation. The paper also highlights future research opportunities, such as the integration of artificial intelligence (AI) technologies, automation, and blockchain in supporting the global expansion of SMEs. These findings are expected to be a strategic reference in designing sustainable internationalization policies in the digital era.

This study aims to examine the effectiveness of using animated video media in improving elementary school students’ understanding of the material of changes in the form of objects, which includes the process of melting, freezing, evaporating, condensing, and sublimating. The material is often difficult for students to understand because it is abstract and cannot always be observed directly. This research uses a qualitative method with a library research approach, where data is collected and analyzed from various reliable sources such as accredited scientific journal articles, books, and previous research results. The analysis technique used refers to the Miles and Huberman model, namely data reduction, data presentation, and conclusion drawing. The results of the study show that the animated video media can improve students’ cognitive understanding through visualization of complex processes, strengthen information retention, and reduce common misconceptions. In addition, this media is also proven to increase students’ motivation, interest in learning, and participation in learning. The effectiveness of animated videos is greatly influenced by the quality of visual design, narration, appropriate duration, and their integration in learning activities such as discussions or experiments. Despite challenges such as limited facilities and teacher training needs, strategic solutions can be implemented to optimize the use of this media. Thus, animated videos can be an innovative and relevant strategic alternative in science learning in elementary schools, especially in conveying abstract concepts in a concrete and fun way.

Nephrolithiasis, commonly known as kidney stone disease, can be experimentally induced in animal models using ethylene glycol (EG) in combination with ammonium chloride, which closely mimics calcium oxalate stone formation in humans. This condition is associated with significant renal tissue injury, including glomerular atrophy, tubular necrosis, and hyaline droplet accumulation, ultimately impairing kidney function. The present study aimed to investigate the histopathological changes in the kidneys of EG-induced rats and evaluate the nephroprotective potential of combined avocado (Persea americana) and gooseberry (Physalis angulata) leaf extracts. A total of 30 male Sprague Dawley rats were randomly divided into five groups: a standard control (no induction), a negative control (EG-induced without treatment), a positive control (EG-induced and treated with a commercial nephroprotective agent), and two treatment groups receiving the combined extracts at different doses. Nephrolithiasis induction was performed over 28 days, followed by oral administration of the respective treatments. At the end of the experiment, kidney tissues were collected and processed for histological examination using hematoxylin-eosin staining to assess the extent of tissue damage. The negative control group exhibited the most severe histopathological alterations, with hyaline droplet formation reaching 15.0 ± 5.25%. In contrast, extract-treated groups demonstrated a marked reduction in tissue damage, with the most significant improvement observed in the group receiving 300 mg/kg avocado extract and 100 mg/kg gooseberry extract, which recorded hyaline droplet formation of only 5.27 ± 2.74%, a result comparable to that of the standard control group. These findings suggest that the combination of P. americana and P. angulata leaf extracts confers protective effects against EG-induced renal injury. Therefore, this herbal combination may represent a promising natural nephroprotective agent that warrants further investigation in preclinical and clinical settings.

Alzheimer’s disease is the most common form of neurodegenerative disorder, characterized by cognitive decline, β-amyloid plaque accumulation, and oxidative stress in brain tissue. One promising alternative therapy is the use of anthocyanins, flavonoid compounds found in various red, purple, and blue-colored plants. This study aims to review the effectiveness of anthocyanins in preventing and slowing the progression of Alzheimer’s disease through a literature review. Articles published between 2014 and 2024 were selected from PubMed, Scopus, and Google Scholar databases, with inclusion criteria focusing on preclinical animal studies evaluating the effects of anthocyanins on neurodegenerative markers. The results show that anthocyanins, particularly pelargonidin and delphinidin, play a role in improving memory, reducing oxidative stress, and inhibiting β-amyloid plaque formation in animal models. Anthocyanins also enhance antioxidant enzyme activity and reduce neuronal apoptosis. These findings support the potential of anthocyanins as neuroprotective agents in Alzheimer’s therapy, although further clinical research is required to validate their efficacy in humans.

System Development Life Cycle (SDLC) is a common methodology used to develop information systems. The purpose of this study is to determine system development between the Waterfall and Rapid Application Development (RAD) models The advantage of using the waterfall method is that the quality of the resulting system will be good because the implementation is carried out in stages. The disadvantage of using the waterfall method is that the system development process takes a long time. The advantage of using the RAD Model method is that the integration of sensors/other applications can be processed faster and more effectively. The disadvantage of using the RAD method is the limitations of payment gateways, and chat bots.

This study aims to analyze the effect of Gross Regional Domestic Product (GRDP) and Minimum Wage on the realization of FDI in five districts / cities in East Kalimantan during the period 2017-2023. The method used is a quantitative approach with panel data and multiple linear regression analysis using Eviews 9 software. The test results show that the best model is the Common Effect Model (CEM). Partially, the GRDP variable has a positive and significant effect on FDI realization, while the minimum wage variable has no significant effect. Simultaneously, both independent variables have a significant effect on FDI. The coefficient of determination (Adjusted R²) of 82.5% indicates that variations in FDI can be explained by GRDP and MSE. This finding confirms the importance of encouraging regional economic growth to attract more foreign investment, as well as considering the balance between labor cost incentives and labor welfare.

This study aims to determine the influence of tourist arrivals, the number of tourist attractions, and the room occupancy rate on the regional original revenue (PAD) of regencies/municipalities in Bali Province from 2018 to 2023. The analysis is conducted using panel data regression with the Common Effects Model, Fixed Effects Model, and Random Effects Model approaches. Model selection is based on the Chow test, Hausman test, and Lagrange Multiplier test. The results show that tourist arrivals, the number of tourist attractions, and the room occupancy rate simultaneously affect the regional original revenue of regencies/municipalities in Bali Province. Partially, the variables of tourist arrivals, number of tourist attractions, and room occupancy rate have a positive and significant effect on the regional original revenue of regencies/municipalities in Bali Province.  

his research aims to identifiy various of attitude scales and psychomotor assessment techniques within the context of learning. The method used is a quantitative approach through library research. The data sources were obtained through a review of literature from academic journals, reference books, and various instructional documents that present numerical data related to the practice of assessing attittudes and skills. The objects of study in this research include types of attitude scales, such as the Likert scale, Guttman scale, and Semantic Differential scale, as well as psychomotor assessment techniques, including performance observation, performance appraisal, and the use of rubrics. Data analysis was carried out using descriptive quantitative methods by presenting how frequently each model is used and the tendency of its effectiveness as reflected in various literature findings. Based on the research resulth, the Likert scale is the most commonly used to evaluate students attitudes, while in the assessmentnof psychomotor aspects, performance observation is the primary technique of choice.    

Thyroid cancer is the most common endocrine malignancy, with a steadily increasing incidence rate. Although the overall survival rate is relatively high, the risk of recurrence after definitive treatment such as Radioactive Iodine (RAI) therapy remains a significant clinical challenge. Predicting recurrence risk is crucial for optimizing monitoring strategies and interventions. With advances in technology, machine learning (ML) approaches are increasingly utilized to support medical predictions, including the recurrence of thyroid cancer. This study aims to evaluate the performance of four classification algorithms—Logistic Regression, XGBClassifier, Random Forest Classifier, and Voting Classifier—in predicting thyroid cancer recurrence using the Thyroid Cancer Recurrence After RAI Therapy dataset, which consists of 383 patient records and 13 key clinical attributes. The evaluation was conducted using accuracy, precision, recall, F1-score, and area under the curve (AUC) metrics. The results show that the XGBClassifier is the best-performing model with an accuracy of 97.4% and an AUC of 0.95, demonstrating superior performance in handling the minority class. This research is expected to contribute to the development of more effective machine learning–based clinical decision support systems for predicting thyroid cancer recurrence after therapy.

This paper aims to overcome the problem of manual financial recording that is still commonly used by MSMEs, which often causes data loss and inefficiency in financial management. To answer this challenge, researchers developed a mobile-based financial recording application using Flutter and a number of available supporting packages. This application is designed to be able to record and store income and expenditure data directly on the user's mobile device, making it easier for MSMEs to monitor their financial condition in real time. The development was carried out using a waterfall model approach, which includes the stages of analysis, design, implementation, and testing. To test the functional performance of the application, the Black Box testing method is used to assess the accuracy and reliability of the features without looking at the internal code structure. The test results show that all features can function as they should and the application is considered effective in supporting digital MSME financial recording.

Rainfall potential analysis plays a critical role in the management of air resources, mitigation of hydrometeorological disasters, and agricultural activity planning. Accurate estimation of rainfall patterns is essential to ensure effective decision-making in irrigation systems, water resource management, and disaster risk reduction strategies. This study aims to model the probability of rainfall occurrence using a statistical approach based on historical data obtained from the Bureau of Meteorology. The data spans a multi-year period and captures seasonal and regional variability in rainfall events. To characterize rainfall patterns, various probability distributions are tested, including the exponential distribution and the Weibull distribution, which are commonly applied in hydrological studies. Furthermore, the Markov chain method is employed to assess the likelihood of rainfall occurrence on a given day based on the conditions of the preceding day, thereby capturing temporal dependencies. Parameter estimation is conducted using Maximum Likelihood Estimation (MLE), a robust statistical method that enhances the precision of the model. The suitability of each probability distribution in representing the observed rainfall data is evaluated through goodness-of-fit tests such as the Kolmogorov-Smirnov test. The findings reveal that certain distributions align more closely with the local rainfall characteristics, demonstrating the importance of regional analysis in climate modeling. The combination of probabilistic modeling, Markov analysis, and rigorous statistical testing provides a reliable framework for forecasting rainfall. These results are expected to serve as a scientific basis for stakeholders in agriculture, environmental planning, and disaster preparedness, offering insights that support sustainable water resource utilization and risk management.

This research aims to identify various challenges in teaching Citizenship Education (PKn) in elementary schools and propose solutions to overcome common obstacles in the learning process. The study employs a literature review approach, analyzing various sources such as scholarly articles, journals, and relevant documents. Data collection methods include literature review, listening techniques, and note-taking. The findings indicate several issues in PKn learning at the elementary level, including: (1) teachers’ limited understanding of appropriate learning models aligned with 21st-century education demands, (2) insufficient use of instructional media tailored to students’ characteristics, (3) inadequate supporting facilities such as books and other learning resources, and (4) student learning difficulties influenced by both internal and external factors. This study concludes that implementing innovative learning models, integrating technology into the teaching and learning process, and adopting more flexible and interactive strategies are essential to enhancing the effectiveness of PKn instruction in elementary schools. Moreover, effective communication between teachers, students, and schools plays a crucial role in improving learning quality.

Science and technology have a big influence on teachers and students in the learning process. The use of digital-based learning media is also commonly used to support the learning process. The use of animated videos in learning is one of the uses of technology in the current era, animated video media provides enormous benefits in the process of delivering learning by teachers and student learning outcomes. In learning Indonesian, learning using animated videos greatly increases students' interest in carrying out learning. In this research the author uses a library study model, by analyzing several trusted articles. This research is suitable to be used because it does not only examine it from one point of view but from several valid points of view. The aim of this study is to find out how effective the use of animated videos is on students' listening skills.

Science and technology have a big influence on teachers and students in the learning process. The use of digital-based learning media is also commonly used to support the learning process. The use of animated videos in learning is one of the uses of technology in the current era, animated video media provides enormous benefits in the process of delivering learning by teachers and student learning outcomes. In learning Indonesian, learning using animated videos greatly increases students' interest in carrying out learning. In this research the author uses a library study model, by analyzing several trusted articles. This research is suitable to be used because it does not only examine it from one point of view but from several valid points of view. The aim of this study is to find out how effective the use of animated videos is on students' listening skills.

Learning boredom is one of the common problems faced by students, especially at the Madrasah Aliyah level. This issue negatively impacts students' motivation and learning outcomes, which in turn can affect the quality of education. This study aims to implement an innovation in learning through the Problem Based Learning (PBL) approach as an effort to reduce learning boredom among students at MA Zainul Hasan 2 during the 2024-2025 academic year. The method used is Classroom Action Research (CAR) with the Kemmis and McTaggart model, which consists of two cycles. Each cycle includes four stages: planning, action, observation, and reflection. Data collection techniques include observation, questionnaires, and interviews. The results of the study indicate a significant decrease in students' learning boredom levels after the implementation of PBL. The average level of learning boredom decreased from 65% in the first cycle to 45% in the second cycle. This decrease shows that the application of PBL-based learning can have a positive effect in reducing learning boredom and increasing student engagement in the learning process. The PBL-based learning innovation has proven to be effective in creating a more engaging and interactive learning environment, which ultimately can improve students' motivation and enhance their learning outcomes. Thus, this study concludes that the implementation of Problem Based Learning can be an effective solution to overcome learning boredom among Madrasah Aliyah students.