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Radiological examination is a diagnostic supporting procedure aimed at visualizing the internal structures of the human body to assist in establishing a diagnosis. One of the examinations performed is the Acromioclavicular Joint examination, which is used to identify abnormalities of the acromioclavicular joint. This study aims to compare the imaging results of the Acromioclavicular Joint using the anteroposterior (AP) projection with a 3 kg weight and the AP projection without a weight. The study was conducted at the Radiology Laboratory of STIKES Boneo Nusantara, Diploma III Radiology Study Program, using a conventional radiography unit and a quantitative descriptive method with a case study approach. Data were collected through observation and questionnaires. The results showed that the examination using a 3 kg weight produced clearer Acromioclavicular Joint images than the examination performed without a weight. The difference was reflected in the improved visualization of the anatomical structures, thereby facilitating a more accurate assessment of the joint. This study concludes that the use of a 3 kg weight in the AP projection provides superior imaging results by enhancing the visualization of the anatomy of the shoulder joint, thereby potentially improving the accuracy of the radiological evaluation of the Acromioclavicular Joint.

Radiological examination is a diagnostic supporting procedure aimed at visualizing the internal structures of the human body to assist in establishing a diagnosis. One of the examinations performed is the Acromioclavicular Joint examination, which is used to identify abnormalities of the acromioclavicular joint. This study aims to compare the imaging results of the Acromioclavicular Joint using the anteroposterior (AP) projection with a 3 kg weight and the AP projection without a weight. The study was conducted at the Radiology Laboratory of STIKES Boneo Nusantara, Diploma III Radiology Study Program, using a conventional radiography unit and a quantitative descriptive method with a case study approach. Data were collected through observation and questionnaires. The results showed that the examination using a 3 kg weight produced clearer Acromioclavicular Joint images than the examination performed without a weight. The difference was reflected in the improved visualization of the anatomical structures, thereby facilitating a more accurate assessment of the joint. This study concludes that the use of a 3 kg weight in the AP projection provides superior imaging results by enhancing the visualization of the anatomy of the shoulder joint, thereby potentially improving the accuracy of the radiological evaluation of the Acromioclavicular Joint.

Background: Digital transformation in arts education has introduced new paradigms in pedagogical tools, yet the specific role of artificial intelligence in fostering creativity within dance education remains under-explored. Objective: This study aims to analyze the contribution of AI Motion technology to enhancing the creative thinking skills of junior high school students in dance learning. Method: A systematic literature review was conducted following the PRISMA 2020 guidelines. A total of 30 peer-reviewed articles published between 2023 and 2025 were selected from databases including Scopus, ScienceDirect, ERIC, Google Scholar, and Garuda. Data were analyzed using the qualitative descriptive framework of Miles and Huberman. Results: The findings demonstrate that AI Motion technology significantly improves movement accuracy by up to 41% and fosters four key dimensions of creativity: fluency, flexibility, originality, and elaboration. The technology provides real-time feedback and visualization, enabling students to transition from passive imitation to autonomous movement exploration. Conclusion: The integration of AI Motion is strategically aligned with the Indonesian Merdeka Curriculum, which emphasizes differentiated learning and 21st-century skills. However, successful implementation is contingent upon overcoming challenges related to teacher digital competency and infrastructure accessibility. This study provides a conceptual framework for modernizing arts pedagogy through AI-assisted creative exploration.

This study aims to develop a sentiment classification method for YouTube user comments related to the game Love and Deepspace using the Naïve Bayes algorithm, focusing on improving the text data processing and understanding user perceptions. Comment data were collected through scraping from YouTube videos, followed by preprocessing including text cleaning, normalization, stopword removal, stemming, and translation into English. Initial labeling was conducted using TextBlob, then the data were randomly sampled for training the Naïve Bayes model. Evaluation involved comparing sentiment distributions and visualization using Word Cloud and bar charts. The Naïve Bayes model achieved an accuracy of 77.36% in sentiment classification. The sentiment distribution shows differences between TextBlob (positive: 1,011, neutral: 1,312, negative: 575) and Naïve Bayes (positive: 901, neutral: 1,627, negative: 370), with Naïve Bayes being more conservative. The Word Cloud visualization identifies dominant words such as "bang," "game," and "main," while the bar chart shows the largest proportion of neutral sentiment. Naïve Bayes is effective for sentiment classification on informal comment data, with significant differences from rule-based methods like TextBlob. This research contributes to the development of text data processing techniques and user perception analysis, as well as opening up optimization opportunities with other algorithms like SVM for better accuracy.

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.

This study aims to design and develop an Augmented Reality (AR)-based application for real-time data monitoring of an automatic weather station at PT. XYZ. The system was developed using a prototyping method, involving five key stages: sensor data acquisition via CBOX devices and the Modbus RTU protocol, data transmission through a Flask-based Web API, storage in a MySQL database, QR code-based target image creation using the Vuforia Engine, and a 3D visualization interface developed with Unity integrated with the Vuforia SDK on Android devices. The system successfully received and stored weather data—such as temperature, humidity, and wind speed—into the MySQL database. The AR application also displayed a stable 3D interface panel over QR code markers, providing real-time data updates through an HTTP-based mechanism. This research demonstrates the successful integration of SCADA, Flask, MySQL, and Unity, enhancing both the functionality and commercial appeal of PT. XYZ’s weather station products. Future research should focus on field testing, cloud network integration, and device compatibility.

The digestive system is one of the most abstract and complex topics in high school biology because it involves physiological processes that occur inside the body and cannot be observed directly. This situation calls for the use of instructional media that can help students understand concepts in a more concrete, systematic, and engaging way. This study aims to analyze the effect of interactive multimedia on student learning outcomes regarding the digestive system and to identify the most effective type of multimedia. This study employs a meta-analysis method using secondary data from 20 relevant scientific articles published between 2021 and 2026. Data were collected through a literature review and systematic review, then analyzed using effect size calculations with Cohen’s and Hedges’ g formulas to determine the effectiveness level of each medium. The results indicate that the use of interactive multimedia generally has a positive effect on student learning outcomes, with most media falling into the high to very high effect size category. Interactive multimedia based on Articulate Storyline was the most effective medium with the highest effect size value of 2.730, followed by Augmented Reality-based E-Magazine at 2.634. These findings indicate that interactive multimedia is effective in facilitating the visualization of concepts and increasing student engagement.  

This study aims to analyze the development of studies on the use of biomass as a renewable energy source to support national energy security using a bibliometric approach. Research data were obtained from the Scopus, Web of Science, and Google Scholar databases with a publication range of 2015-2025. The analysis was conducted using VOSviewer and Biblioshiny. The results show a significant increase in publication trends in the last decade, especially in the period 2016-2024, reflecting the increasing academic attention to biomass as a solution in the energy transition. Keyword visualization shows that biomass is closely related to concepts such as combustion, thermal efficiency, calorific value, and pelletizing. China is the country with the highest publication contribution, while Indonesia is strategically positioned due to its abundant biomass waste potential. Overall, biomass has great potential to support energy diversification, reduce dependence on fossil fuels, and strengthen national energy security in a sustainable manner.

Financial reporting integrity is heavily reliant on audit quality. This research explores the effect of auditor attributes, specifically integrity and professional background, on audit standards in Timor-Leste. Quantitative data was collected from a survey of 60 auditors and analyzed using descriptive statistics, correlation analysis, and multiple linear regression techniques. The findings suggest that auditor ethics has a substantial positive impact on audit quality, indicating that conformity to professional ethical guidelines is vital for enhancing audit results. In contrast, experience in auditing does not demonstrate a statistically significant impact on the quality of audits. Visualization through scatter plots further supports the notion that the relationship between ethics and audit quality is more robust than that of other auditor characteristics. Strengthening ethical standards in the auditing profession is crucial to enhancing audit quality, as these findings demonstrate. This study contributes to the body of research on auditing behavior by presenting empirical findings from a developing institutional setting. The findings also have practical implications for policymakers and auditing bodies in Timor-Leste.

This study aims to analyze students’ mathematical reasoning abilities in quadratic function material through the use of Desmos. The research employed a qualitative descriptive approach using a case study and usability testing method. Data were collected through post-tests and interviews involving students’ responses in solving quadratic function problems. The analysis focused on several indicators of mathematical reasoning, including procedural skills, conceptual understanding, and analytical ability. The results show that students generally demonstrate adequate procedural reasoning, particularly in substitution and computation tasks. They are also able to relate algebraic representations to geometric interpretations, such as determining intercept points and analyzing the direction of parabolas. However, limitations were found in conceptual understanding, especially in identifying graph characteristics without relying on procedural steps. The use of Desmos significantly supports the development of students’ mathematical reasoning by providing real-time visualization, facilitating exploration of function parameters, and enabling verification of results. Overall, Desmos contributes to enhancing procedural, conceptual, and analytical dimensions of mathematical reasoning, although its effectiveness depends on proper instructional design.

The phenomenon of radical transparency in the Bottled Drinking Water (AMDK) industry in 2025 triggered a reputation crisis and a massive erosion of customer loyalty. This crisis began with negative sentiment on social media that led to surprise inspections by regulatory authorities of the industry leader's "Mountain Spring" claim. The unpreparedness of the communication infrastructure in presenting technical data in real time led to a decline in brand image and a significant correction in market share to a critical figure of 47.4%. This article aims to formulate a conceptual framework for adaptive and data-driven PR presentation techniques through the integration of real-time data visualization and narrative reframing strategies. The method used is a descriptive conceptual analysis by evaluating the chronology of incidents and PR responses during the crisis. The analysis results show that unidirectional communication patterns and static presentation materials are deemed to fail to face viral and spontaneous public audits in the digital space. As a theoretical and practical contribution, this article proposes a new model, The Spontaneous Technical Presentation Model, and recommendations for the development of a Crisis Presentation Kit for the FMCG industry. Through a theoretical approach that expands the theory of Image Restoration, this study confirms that corporate image restoration in the digital era is highly dependent on the precision and transparency of visual data presented during field qualification

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.

This study aims to describe the concept of Immersive Commerce based on Augmented Reality (AR) in increasing consumer confidence in E-Commerce transactions and to design a conceptual prototype design framework for an AR E-Commerce system with a clear technical flow. The research method uses a literature study through a comprehensive review of AR-based E-Commerce implementations and needs analysis from the perspective of consumer problems and business opportunities. The main problem identified is consumer hesitation in purchasing products online due to limited visualization that relies solely on product photos, causing fears about differences in shape, size, and quality of goods. The research results produced a conceptual framework that includes an integrated system architecture with a frontend layer (AR visualization engine), backend layer (product database and 3D asset management), and integration layer, as well as a systematic user flow design from the discovery to the decision phase. Based on simulations using literature data, the designed framework has the potential to increase customer engagement by up to 169%, conversion rates by up to 11 times, and reduce product return rates by up to 50%. The benefits of this research include digital dimensions through E-Commerce technology innovation, social dimensions by increasing consumer trust and reducing product fraud, and environmental dimensions through the potential reduction of product returns. This conceptual prototype provides a foundation for digital innovation, social trust building, and environmental sustainability through reduced product returns.

Higher education management at the master's level currently faces urgent challenges, namely learning fatigue and low engagement among professional students, especially in Prior Learning Recognition (RPL) classes. Currently, lecturers still tend to apply conventional learning methods based on static presentations that fail to accommodate andragogical characteristics due to a lack of dynamic interaction. Therefore, this study aims to evaluate the effectiveness of the “Humanistic Digital Andragogy” approach through the integration of gamification (Kahoot) and visual thinking (Whimsical) in the Strategic Human Resource Management course. The researchers used a descriptive qualitative design with thematic analysis and collected data through feedback from students in the Master of Management Program (Semarang and Sorong classes). The results revealed that technology served as a double catalyst: Whimsical visualization effectively reduced the cognitive load of complex strategy material, while competition in Kahoot triggered positive adrenaline (eustress) that increased attention. These findings confirm that the success of technology is highly dependent on the role of lecturers as humanistic facilitators (high-touch). This synergy has been proven to change students' perceptions of HRM from merely administrative to strategic partners, as well as creating learning satisfaction that is relevant to the world of work.

This study aims to analyze the impact of immersive commerce and augmented reality (AR) on consumer trust in e-commerce transactions using secondary industry data from the BrandXR 2025 Research on Augmented Reality in Retail & E-Commerce. Quantitative data was analyzed descriptively to evaluate the adoption rate of immersive technologies (immersive commerce and AR) and their relationship with consumer trust perceptions. The results show that immersive commerce, characterized by interactive and realistic user experiences, has a positive impact on consumer trust. Similarly, AR which offers realistic product visualization, has a significant impact on strengthening consumer trust in purchasing decisions. Simultaneously, the integration of immersive commerce and AR enhance the digital shopping experience, which can reduce risk perception and increase trust in online transactions. These findings empirically contribute to the literature on digital consumer behavior and e-commerce strategies, and suggest that businesses adopt immersive technologies to increase consumer trust and engagement in the online commerce ecosystem.

This study explores the intellectual structure and research trends surrounding ethnocentrism in multinational corporations (MNCs) through a bibliometric mapping approach, with particular attention to cross-cultural management and workforce diversity studies. An initial dataset of 200 records was retrieved using Publish or Perish from Google Scholar and Crossref, of which 96 peer-reviewed journal articles published between 2020 and 2025 were retained following a PRISMA guided screening process. Bibliometric analysis was conducted using VOSviewer to examine keyword co occurrence, network relationships, and thematic density. The results indicate that ethnocentrism functions as a central organizing concept within the literature, closely associated with cultural differences, expatriation, workforce diversity, and organizational practices in multinational contexts. Temporal patterns observed in overlay and density visualizations suggest a growing scholarly emphasis on practice-oriented and context-specific perspectives, particularly in relation to managerial and organizational implications in global operations. Overall, this study offers a structured synthesis of existing research, highlights emerging thematic directions, and provides a foundation for future empirical and comparative inquiries into cultural diversity management in multinational corporations.

This study presents a Graph-Augmented Isolation Forest (GAIF), an unsupervised anomaly-detection framework for analyzing mobile user behavior. The proposed framework represents users and behavioral attributes as a user–feature bipartite graph, enabling the capture of relational dependencies that are not explicitly modeled in conventional vector-based approaches. Low-dimensional user representations are learned through Node2Vec and Graph Sample and Aggregate (GraphSAGE), and the resulting embeddings are subsequently processed by an Isolation Forest to produce anomaly scores. Experiments are conducted on a Mobile Device Usage and User Behavior dataset comprising 700 user profiles derived from application-level behavioral indicators. The dataset is treated as a behavioral abstraction rather than as a malware classification benchmark. A consistent 80:20 stratified train–test split is employed, with all learning-capable operations restricted to the training data to mitigate information leakage. Detection performance is evaluated post hoc using precision, recall, F1-score, and area under the curve (AUC) metrics. Under the evaluated setting, GAIF achieves an F1-score of 0.94 and an AUC of 0.97, demonstrating improved anomaly detection effectiveness relative to representative unsupervised baseline methods. These results are obtained on a static, proxy dataset and should not be interpreted as evidence of real-time deployment capability. Model interpretability is supported through post-hoc Uniform Manifold Approximation and Projection (UMAP) visualizations of the learned embeddings, providing structural insights into anomalous user behavior. Overall, the findings indicate that integrating graph-based representation learning with isolation-based anomaly scoring constitutes a computationally efficient approach for unsupervised mobile user behavior anomaly detection within the scope of this study.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.

Heart disease is the leading cause of death in Indonesia and globally, necessitating an early screening system that is both accurate and clinically trustworthy. Although XGBoost demonstrates high predictive performance, its black-box nature undermines clinical trust, while low recall risks missed diagnosis an unacceptable consequence in population screening, especially in middle-income countries with limited healthcare resources. This study aims to develop a sensitive, transparent, and implementation-ready heart disease screening framework through the integration of SHAP-based Explainable AI. The CDC's Indicators of Heart Disease dataset (319,795 samples) was processed according to WHO/CDC standards, followed by class imbalance handling, hyperparameter optimization using RandomizedSearchCV, evaluation based on metrics sensitive to minority classes (AUC, recall, F1-score, AUC-PR), and threshold tuning to maximize recall. The baseline model showed a very low recall of 12.18%. After optimization and threshold tuning at 0.10, the model achieved recall >96% (96.79%) with a G-mean of 0.7477, supported by SHAP interpretation stability and the ability to capture non-linear interactions between advanced age (AgeCategory_WHO) and poor general health (GenHealth). SHAP analysis confirmed the alignment of dominant features with medical evidence, and its visualizations provide transparent explanations for healthcare professionals indicating its potential implementation as an interpretable clinical decision support system.

This study evaluates public sentiment toward Constitutional Court Decision No. 90/PUU-XXI/2023 regarding the age limit for presidential and vice-presidential candidates, a controversial issue closely related to Indonesia’s democratic dynamics. Understanding public opinion on Twitter, as a major platform for political expression, is essential for informing electoral policy formulation. Data were collected using Tweet Harvest through Google Colab and analyzed using the Naïve Bayes algorithm as the primary sentiment classification method, with RapidMiner employed to support and streamline the analytical process. The analysis process included data cleaning, text normalization, stopword removal, manual labeling of 80 tweets as training data, and automatic sentiment classification to identify positive and negative sentiments. From a total of 151 analyzed tweets, 84 (55.63%) were classified as negative and 67 (44.37%) as positive, with the model achieving an accuracy of 66.67%. These findings suggest a tendency toward public opposition to the decision, reflecting dissatisfaction among Twitter users. The study demonstrates that Naïve Bayes is reasonably effective for sentiment classification with limited datasets and provides insights for policymakers in understanding public responses to election-related regulations.