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Risk factors for melasma have been identified at several points such as the use of birth control pills, cosmetics, sun exposure, estrogen therapy, decreased thyroid and ovarian function, ovarian tumors, nutrition, drugs that are classified as phototoxic or photoallergic, and epilepsy drugs. This article aims to determine the incidence of melasma and its risk factors. This study uses articles collected from the Google Scholar database. The data collection step uses the keyword "Risk Factor Melasma" according to the title and keyword criteria. The articles used are publications in the 2018-2023 year range. The stages of analysis are carried out using VOSviewer software for visualization and trend analysis in the form of bibliometric maps. The bibliometric approach was applied to identify publication patterns, research developments, and relationships among topics related to melasma risk factors. Based on the visualization results, 100 selected documents were published within 5 years, from 2018 to 2023. The findings provide an overview of the main research trends and contribute to a better understanding of the incidence of melasma and the factors associated with its occurrence

Dengue Hemorrhagic Fever (DHF) remains a major public health problem in Indonesia, particularly in densely populated areas. Control efforts require accurate data and spatial analysis to understand disease distribution patterns. Geographic Information System (GIS) is an effective tool for visualizing case distribution and supporting surveillance and planning of control programs at the primary healthcare level. This study aims to describe the spatial distribution of Dengue cases based on medical record data and produce a geographic distribution map to support Dengue control efforts at the Puskesmas level. This study used a quantitative descriptive design with secondary data from medical records at Karangsari Health Center. The sample consisted of 255 DHF patients in 2025, selected using a total sampling technique. Data were processed through editing, geocoding patient addresses, and spatial analysis using QGIS software.The results showed 255 Dengue  cases in 2025 with fluctuating monthly trends, peaking in April and lowest in December. Case distribution was uneven and tended to cluster. High-risk areas accounted for 15.7%–21.2%, moderate-risk areas 9.8%–15.7%, and low-risk areas 7.1%–9.8%. Megu Cilik Village had the highest proportion of cases, while other villages were categorized as moderate and low risk. This pattern indicates that Dengue incidence is influenced by environmental conditions, vector density, host factors, rainfall, and Aedes aegypti presence. GIS provides clearer spatial visualization, helping identify high-risk areas and supporting targeted public health interventions.

Background: Automatic segmentation of the thyroid gland in ultrasonography (USG) images using deep learning requires a user-friendly interface to support diagnostic and educational processes. Purpose: This study aims to develop and implement a Graphical User Interface (GUI) that integrates a deep learning U-Net model for interactive and efficient segmentation and visualization of thyroid USG images. Method: The development method employed the Rapid Application Development (RAD) approach using MATLAB programming language. The GUI is designed to load transverse and sagittal USG images, display automatic segmentation results, and calculate thyroid gland volume based on dimensions measured automatically from the segmentation output. Testing was conducted using USG image data from 15 volunteers, and GUI functionality was evaluated using black box testing. Result: The GUI successfully displayed USG images and segmentation results with a responsive 4-panel interface; zoom, pan, and image navigation features functioned well. Automatic segmentation occurred in real-time after image input, and volume measurement results appeared automatically. Black box testing evaluation showed all GUI features operated as expected. The average Dice Similarity Coefficient (DSC) of 0.91 indicates high performance of the U-Net model in thyroid segmentation, consistent with previous findings. Statistical testing confirmed no significant difference between volume measurements using the application and manual methods (p = 0.953). Conclusion: This GUI implementation facilitates users in performing deep learning-based segmentation and visualization of thyroid USG images, improving efficiency and accuracy in thyroid volume measurement. The GUI has potential applications in clinical practice and radiology education.

Health issues related to free radicals remain a serious concern in Indonesia as they can trigger oxidative stress and degenerative diseases. Red ginger (Alpinia purpurata) is a rhizome plant with potential as a source of natural antioxidants due to its secondary metabolite content; however, its effectiveness is highly influenced by extraction techniques. Although numerous experimental studies have been conducted, a systematic research mapping on this topic is still lacking. This study aims to perform a bibliometric analysis of scientific publications regarding the antioxidant potential of red ginger, focusing on extraction techniques and free radical scavenging activity. The research method employs a quantitative analysis using data sourced from the Scopus database for the 2015–2025 period. Through specific inclusion and exclusion criteria, 38 relevant articles were obtained and analyzed using VOSviewer 1.6.20 software. The results indicate that publication trends have fluctuated, reaching a peak in 2024. Research distribution is dominated by Asian countries, with India, Thailand, and Indonesia as the primary contributors. Network visualization reveals three main clusters focusing on bioactivity, phytochemistry, chemical analysis, antimicrobial activity, and extraction techniques. A research gap was identified for the Alpinia purpurata species compared to Alpinia galanga, as well as opportunities for developing advanced instruments such as LC-MS and other complex analytical techniques. The implications of this study highlight the need for further exploration into 'nanoemulsion' and 'green extraction' to enhance the bioavailability of red ginger's antioxidant compounds in the development of future innovative pharmaceutical products

Decoction is a traditional extraction method rooted in ethnobotany; however, meeting quality standards in modern pharmaceutical research remains a major challenge. This study aims to map global research trends regarding phenolic and flavonoid compounds in decoctions over the 2015–2025 period through bibliometric analysis. Data were retrieved from the Scopus database and analyzed using VOSviewer 1.6.20 software, employing the fractional counting method to ensure a more proportional weighting of keyword relationships. The results indicate a fluctuating trend that significantly increased toward the end of the period, peaking at 78 documents in 2025, with India and China emerging as the primary contributors. Network visualization and research density analysis reveal that the global research focus remains centered on antioxidant capacity (DPPH, TPC, and TFC), while decoction itself occupies a supporting position within the research map. This study concludes that decoction has not yet become a central focus in modern pharmaceutical research but serves primarily as a vehicle for presenting active compounds. There remains a significant gap between traditional decoction use and the application of advanced analytical technologies such as HPLC and antibacterial testing, representing a substantial opportunity for future research to validate the safety and efficacy of decoctions more scientifically and through standardized approaches.

Radiographic examination plays a crucial role in visualizing internal body structures for diagnostic purposes. One of the radiographic assessments frequently performed is the Acromioclavicular (AC) joint projection, which is used to evaluate abnormalities such as joint widening, subluxation, and dislocation. This study aimed to compare the image quality of the AC joint using the Anteroposterior (AP) projection with a 3-kg load and without load. The study was conducted in the Radiology Laboratory of STIKES Borneo Nusantara using a conventional X-ray system with a quantitative descriptive case-study approach. Data were collected through observation and questionnaires administered to 10 research subjects, including radiographers and patient participants. The findings demonstrated that the AP projection with a 3-kg load produced clearer visualization of the AC joint, particularly in widening of the joint space and separation between the humeral head and glenoid cavity. The average image quality score using load was 3.5 (good), compared with 2.9 (poor) for the projection without load. The study concludes that applying a 3-kg load improves anatomical visualization of the AC joint and is recommended for cooperative patients to enhance diagnostic accuracy.

The integration of green marketing into corporate strategies represents a paradigmatic shift in responding to global sustainability issues while enhancing competitiveness and socio-ecological commitment. With the rapid growth of literature in this field, a systematic analysis is required to map its conceptual evolution and strategic directions. This study presents a bibliometric analysis of global research trends in green marketing, aiming to identify the intellectual structure, thematic evolution, and knowledge development over the period 2017–2024. Using the Scopus database and the Biblioshiny and VOSviewer tools, 275 peer-reviewed articles were examined through quantitative bibliometric mapping techniques. The analysis reveals an annual publication growth rate of 15.27%, with dominant themes such as “sustainability,” “green marketing,” and “sustainable development.” Thematic mapping visualizations indicate a shift in research focus from product-related technical issues to strategic, social, and consumer behaviour dimensions. Five major thematic clusters were identified, reflecting interdisciplinary approaches encompassing digital marketing strategies, consumer behaviour, corporate social responsibility, supply chain management, and environmental ethics. These findings underscore the significance of integrating green marketing as a sustainable business strategy, not only to enhance corporate image but also to foster more environmentally responsible consumer behaviour. The study provides a conceptual foundation for future inquiries and strategic implications for business practices, affirming the role of green marketing as a catalyst for organizational transformation toward environmental and social sustainability.

This study maps the regulatory landscape of financial technology (fintech), focusing on cryptocurrency regulation at both global and Indonesian levels. Cryptocurrency, one of the fastest-growing fintech instruments, functions as a virtual currency secured by cryptography. Despite lacking physical form, it is widely used for investment, transactions, and speculation, with trust supported by blockchain’s transparency and immutability. However, regulatory frameworks remain fragmented across countries. The research applies a bibliometric approach, using Bibliometrix (R Studio) for descriptive analysis and VosViewer for keyword network visualization. Data were retrieved from Scopus with the keywords “cryptocurrency regulation” and “fintech regulation,” covering 2016–2025. Findings reveal 1,178 documents from 484 sources, contributed by 4,693 authors, with an average of 7.43 authors per document and an international collaboration rate of 24.79%. The annual growth rate reaches 44.31%, with an average of 14.01 citations per document. Keyword analysis identifies four main clusters: financial regulation, green finance and sustainability, decentralized finance (DeFi), and blockchain cybersecurity. This study provides a knowledge map of regulatory evolution from conventional finance to blockchain-based fintech, offering insights for academics, regulators, and industry to balance innovation, consumer protection, and financial stability.

Sleep is a fundamental human need that plays a crucial role in maintaining both physical and mental health. Poor sleep quality can trigger a variety of health problems, ranging from decreased concentration to an increased risk of chronic diseases. The complexity of factors influencing sleep quality—such as stress levels, heart rate, blood pressure, physical activity, and lifestyle—makes its assessment difficult through direct observation alone. Therefore, data mining approaches are increasingly utilized to identify relevant patterns in sleep-related data. This study aims to compare the performance of the C4.5 (Decision Tree) algorithm and the Naïve Bayes algorithm in predicting sleep quality using the Sleep Health and Lifestyle dataset, which contains information from 374 respondents. The research method applied is a quantitative comparative approach employing classification techniques with 10-fold cross-validation to ensure robust evaluation. Model performance is assessed using accuracy, precision, and recall metrics to provide a comprehensive understanding of the effectiveness of each algorithm. The findings indicate that the C4.5 algorithm achieves an accuracy of 96.26% and offers advantages in terms of interpretability through its decision tree visualization, enabling easier understanding of variable relationships. In contrast, the Naïve Bayes algorithm demonstrates superior predictive performance, achieving an accuracy of 98.66% along with consistently high precision and recall across nearly all classes. These results suggest that Naïve Bayes is more effective for predictive tasks involving sleep quality, while C4.5 remains highly valuable when the goal is to interpret variable interactions and decision rules. Overall, this research highlights the potential of data mining techniques in health informatics, particularly in improving the understanding and prediction of sleep quality, which in turn can contribute to better prevention and management of sleep-related health issues.

A lopography or barium enema colostomy is a radiographic examination of the colon using a contrast medium inserted through a colostomy. This examination aims to evaluate the condition of the colon after the colostomy, including detecting the presence of obstructions or masses in the gastrointestinal tract. The contrast medium used is a type of non-ionic soluble water that is inserted through the stoma and anus orifice until it fills the entire colon, characterized by the exit of contrast through the stoma. The examination was carried out using several radiographic projections, including plain photographs of the AP abdomen, as well as post-contrast projections of the Antero Posterior (AP), Right Posterior Oblique (RPO), Left Posterior Oblique (LPO), and lateral. This study aims to find out the procedure for the Lopography examination at the Radiology Installation of Caruban Hospital and the medical reasons for the use of two channels of input of contrast media, namely through the stoma and anus. The method used is qualitative descriptive research with a case study approach. The study was carried out from November to May 2025, with one post-colostomy patient as a sample. Data collection was carried out through direct observation, interviews with medical personnel, and documentation of radiographic procedures. Data analysis includes data reduction, data presentation, and conclusion drawn. The results of the study showed that the procedure of Robotic examination did not require any special preparation before the procedure. The non-ionic contrast medium used is mixed with aquades at a ratio of 1:3 (about 200 cc), and inserted through both the stoma and the anus to obtain a thorough visualization of the colon. The selection of two input paths aims to ensure that there are no blockages or masses that block the contrast flow. The projections used are adjusted to provide maximum information about the structure of the colon after colostomy

Pneumonia detection from chest X-ray images is widely used in computer-aided diagnostic systems. However, effective clinical decision support requires not only accurate classification performance but also consideration of unequal error costs, since false negative predictions may lead to more severe consequences than false positives. In addition, prediction probabilities must be well calibrated to support threshold-based medical decisions such as triage and patient escalation. This research investigates asymmetric misclassification costs and probability calibration for binary classification (PNEUMONIA vs. NORMAL) using the Hugging Face dataset hf-vision/chest-xray-pneumonia. The proposed framework utilizes a ResNet-18 architecture integrated with cost-sensitive learning through weighted cross-entropy loss (FN:FP = 5:1), threshold optimization based on validation data to reduce expected cost, and post-hoc temperature scaling for improving probability calibration. Experimental results on the independent test set indicate that the cost-sensitive approach enhances specificity and decreases expected cost compared to the conventional cross-entropy baseline. Furthermore, temperature scaling improves the reliability of probabilistic predictions, as demonstrated by better negative log-likelihood and Brier score values. The study also explores selective prediction strategies to balance prediction coverage and risk reduction, complemented by Grad-CAM visualizations and structured failure-case analysis for qualitative assessment. Overall, the findings demonstrate that incorporating cost-aware decision thresholds and calibrated probability estimates can serve as lightweight yet effective enhancements for chest X-ray classification systems in clinical decision-support applications.

Colon In Loop examination is an important radiographic procedure in the evaluation of Hirschsprung cases in pediatric patients. At Brebes Regional Hospital, there are differences in procedures compared to existing literature, especially in the method of administering contrast media and the projections used. This study aims to review and examine the Colon In Loop examination procedure in pediatric patients with Hirschsprung cases at Brebes Regional Hospital. This study uses a qualitative descriptive approach with a case study method. Data were collected through observation, interviews, and documentation involving three radiographers and one radiologist as research subjects. Data analysis techniques were carried out through data reduction, data presentation, and drawing conclusions. Results Colon In Loop examination in pediatric patients at Brebes Regional Hospital was performed without any special preparation for the patient. Contrast media was used with a stepwise method, namely 50cc in the first stage and added 50cc in the second stage. The projections used included AP (Antero Posterior) and AP oblique to overcome visualization limitations. The Colon In Loop examination procedure at Brebes Regional Hospital differs from the literature regarding the method of contrast media insertion and additional projections. This provides better visualization for Hirschsprung's diagnosis in pediatric patients.

The role of administrative staff in Islamic boarding schools is very central in ensuring the smooth functioning of the institution. One of the important competencies that need to be possessed is statistical literacy, which includes the ability to understand, process, and present data systematically. The purpose of this PKM activity is to improve the knowledge and skills of administrators in applying statistics to administrative management. With an understanding of concepts, insight into the application of statistical concepts, accuracy of interpretation of statistical results, and visualization and communication skills of statistical analysis results. This community service activity is carried out through workshops and intensive mentoring. The results of this activity indicate that efforts to improve the knowledge and skills of partners in managing student administration through strengthening statistical literacy have gone well and provided quite optimal results, especially in the indicators of skills in calculating and visualizing data.

Male infertility is a growing concern in reproductive health, affecting millions worldwide. Despite its significant impact on couples' ability to conceive, it remains underexplored compared to female infertility. This study offers a comprehensive bibliometric analysis of male infertility research, identifying trends, key contributors, and emerging areas of focus. By analyzing publication patterns, leading journals, influential authors, and collaborative networks, we aim to provide a nuanced understanding of the current state and future directions of this research field. The insights gained are expected to guide future investigations, enhance collaborations, and inform policymakers and clinicians about this evolving area. A systematic literature search was conducted in Scopus and Web of Science databases to retrieve relevant publications on male infertility. ScientoPy was used and bibliometric indicators, such as publication output, citation analysis, author collaboration networks, and keyword co-occurrence analysis, were used to explore the intellectual structure and research trends in this domain. Visualization tools, including VOSviewer, were employed to present the findings. The analysis included 2726 publications spanning from 2014 to 2024. The results reveal the most productive authors, influential journals, and highly cited publications in the field. Key research themes, emerging topics, and the evolution of the research landscape are discussed. Collaborations among authors, institutions, and countries are also examined to identify the intellectual structure and global research networks. This analysis provides valuable insights into the current state of research on male infertility The findings highlight the critical areas of focus, influential studies, and collaborative patterns that have shaped the field. Implications for future research directions, evidence-based practice, and potential areas for interdisciplinary collaboration are discussed.

Hospitals worldwide are increasingly concerned about the performance of outpatient services, which contribute significantly to their revenue and are projected to surpass inpatient services in importance. As the demand for medical care continues to grow, outpatient services could generate even greater financial returns for hospitals. This study aims to evaluate patient service wait times at Stella Maris Hospital Makassar's outpatient department using the lean methodology. A mixed-methods approach was employed, involving the measurement of wait times for 100 patients and interviews with eight informants, including the head of the outpatient department (1 person), admission officers (3 individuals), the hospital director (1 person), and nurses (3 individuals). The interview data were analyzed through processes of data reduction, visualization, and conclusion formulation. A patient care process flowchart was created to assess data quality. Findings indicate the absence of an integrated hospital information system (SIMRS), leading to issues such as long queues at the pick-up counter, delays in doctor availability, and inefficiencies in order processing. Recommendations include integrating SIMRS across the hospital, implementing system improvements, and quantifying outcomes for effective decision-making.  

In the current era of digitalization, Microsoft Excel has become one of the most popular and essential software in various fields, from business, finance, to science. The ability to process data effectively and be efficient is the key to success in various jobs. Excel formulas and functions are very important features in processing this data. By understanding and mastering Excel formulas and functions, someone can perform calculations, data analysis and data visualization more accurately and quickly.

In this research, a machine learning approach, especially a decision tree model, is implemented to improve the analysis and visualization of weighbridge data in Indonesia. The evaluation results show that the decision tree model provides better insight in predicting the carrying capacity, dimensions and loading procedures of vehicles. The advantage of this model lies in its combination of low Mean Squared Error (MSE) and high R-squared, indicating its effectiveness in capturing data variance and providing accurate predictions. The use of decision tree models can be a valuable tool in improving the visualization of bridge weighing data, allowing users to gain additional insights and understand the complex dynamics within the data. In addition, the model's adaptability to various types of data makes it a versatile analysis tool. The positive implications of using this model open up opportunities to understand more deeply the logic of predictions and make more informed decisions. As a suggestion, increasing the number and quality of weighing equipment, wider application of information and communication technology, human resource training, and cross-sector collaboration can further strengthen weighbridge management in Indonesia.

The Community Service Program (PKM) has successfully conducted a training on Dashboard Development and TikTok Business for the Small and Medium Enterprise (MSMEs) Shofi Group. This training was facilitated by the Informatics Engineering Department of Trisakti University and has received an excellent rating based on feedback from the participants. As part of this training, a visualization dashboard was successfully integrated into the Shofi Group's website, allowing them to monitor sales data more efficiently and effectively. In addition, participants also learned about the use of TikTok Business as a tool for marketing their products. Positive feedback from Shofi Group indicates the significant benefits of this training, in terms of assisting MSMEs to adapt and utilize digital technology in their business development. These results underline the importance of similar programs in supporting SMEs to compete in the digital era. It is hoped that the success of this PKM can lay the foundation for similar initiatives in the future, supporting more MSMEs in leveraging the potential of digital technology.