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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.

The development of information and communication technology has significantly transformed the field of education. Teachers no longer depend only on lectures to deliver learning materials but also use various learning media to improve the effectiveness of the teaching and learning process. One of the most widely used innovations is interactive learning media, which combines text, images, audio, video, and other interactive features to support student learning. Interactive learning media can create a more engaging and enjoyable learning environment. Through this media, students actively participate in learning activities rather than simply receiving information passively. As a result, their attention, motivation, and understanding of the learning materials can improve. However, low student learning outcomes remain a challenge in education. Limited student involvement in the learning process often affects their ability to understand lessons and achieve learning objectives. Previous studies have indicated that interactive learning media can enhance both the quality of learning and student achievement. Therefore, examining the influence of interactive learning media on student learning outcomes is important to support the development of more effective, innovative, and technology-based learning processes in the digital era.

Malware represents an evolving cybersecurity threat that demands more effective detection methods. Conventional signature-based detection systems have limitations in identifying new variants, driving the development of deep learning-based approaches. This research implements and evaluates four variants of the YOLOv11 algorithm (n, s, m, l) for malware classification based on visual image representation. The dataset consists of 22,056 malware and benign images, divided into 70% training, 15% validation, and 15% testing across 8 classes (adware, backdoor, benign, downloader, spyware, trojan, virus, worm). Each model was trained for 100 epochs with batch size 32 using Google Colab with GPU support. Results demonstrate that all variants achieve high accuracy (97.8%-98.1%) with YOLOv11m as the best performer (98.1%). YOLOv11n offers optimal balance between accuracy (97.9%) and efficiency (1.5M parameters, 0.3 ms/img inference) ideal for real-time applications. This research surpasses previous methods such as K-NN (97.18%) and hybrid CNN (96.55%) with superior inference speed (0.3-0.9 ms/img vs tens to hundreds of ms/img), proving the effectiveness of YOLOv11 for fast, accurate, and scalable malware detection.

Batik motif classification has attracted growing attention in visual computing due to its role in cultural heritage preservation, textile informatics, museum documentation, and automated cataloging. Although many studies report high classification accuracy, robustness under real-world acquisition conditions remains insufficiently understood. Batik images are frequently affected by illumination variation, blur, folds, watermark overlays, wearable deformation, scale inconsistency, and background clutter, creating challenges that extend beyond conventional image-noise assumptions. Existing studies largely focus on improving classification performance, while the interactions among acquisition variability, feature representation, evaluation practice, and deployment constraints remain fragmented. This systematic literature review addresses this gap by synthesizing batik classification research through a robustness-aware perspective. Using query expansion, backward and forward citation chaining, relevance screening, and thematic coding, 116 candidate records were identified, resulting in 50 highly relevant studies for detailed analysis. The review reveals that robustness is shaped less by denoising alone than by the combined effects of acquisition conditions, representation design, evaluation realism, and deployment context. Handcrafted descriptors remain competitive for small datasets and structured motifs due to their data efficiency and interpretability, whereas deep learning models achieve the highest reported accuracy when supported by sufficient data diversity and realistic augmentation. Hybrid representations emerge as the most consistently balanced approach, combining local texture stability with higher-level abstraction across heterogeneous acquisition settings. The review further identifies recurring robustness failure patterns, including background dependency, illumination instability, motif-scale inconsistency, wearable deformation, and source-shift vulnerability. Based on these findings, a robustness-oriented research agenda is proposed, emphasizing cross-acquisition evaluation, representation-stability analysis, batik-specific robustness benchmarks, acquisition-aware augmentation, and deployable lightweight or hybrid architectures. The study contributes a domain-specific synthesis that reframes batik motif classification from an accuracy-centric task toward a robustness-aware visual recognition problem.

Abstract: Detecting chili leaf diseases remains challenging due to the non-uniform manifestation of symptoms, local discoloration, small lesion regions, and visual similarity between disease patterns and natural leaf background variations. Although YOLO-based detectors provide favorable computational efficiency, lightweight variants often struggle to distinguish subtle lesion characteristics, while conventional attention mechanisms such as CBAM primarily rely on global feature aggregation and may overlook regional activation variability. To address these limitations, this study proposes a YOLOv9s-based detection framework integrated with a Region-Dispersion Channel Spatial Attention (RDCSA) module. The proposed module incorporates regional dispersion statistics, namely mean, standard deviation, and range, as channel descriptors to capture inter-region feature variability before applying spatial attention refinement. Experiments were conducted on the COLD dataset containing 532 original images from five chili leaf condition categories using a split-before-augmentation protocol to ensure objective evaluation. RDCSA was integrated at the P5 feature level and evaluated through attention placement analysis, component-wise ablation, sensitivity analysis, stability assessment, and comparison with modern attention mechanisms. The proposed YOLOv9s + RDCSA model achieved an mAP@50 of 0.894, mAP@50–95 of 0.773, precision of 0.858, recall of 0.861, and an F1-score of 0.859 with only a marginal increase in model parameters. The results suggest that regional dispersion-based attention improves feature discrimination while preserving computational efficiency, particularly for disease symptoms characterized by heterogeneous spatial patterns. Nevertheless, performance remains influenced by visually ambiguous symptom categories, indicating that further validation across multiple datasets and field conditions is required. Overall, the proposed RDCSA module enhances detection capability without substantially increasing computational overhead, making it a promising attention mechanism for lightweight plant disease detection systems.

The dissemination of personal data through digital media has increased significantly alongside the growing use of Quick Response (QR) Codes for various purposes, such as electronic tickets, certificates, and digital identities. Conventional QR Codes are open and can be easily scanned, copied, or manipulated by unauthorized parties. The personal data referred to in this study includes sensitive information such as full name, identity number (NIK/National ID), date of birth, address, phone number, and email address. This research proposes a layered security system that combines the Advanced Encryption Standard (AES) cryptographic algorithm with steganography using the Discrete Cosine Transform (DCT) method. The process begins with encrypting personal data using AES, converting the encrypted result into a QR Code, and embedding the QR Code into a digital image using DCT, hiding it in the image’s frequency domain. The digital images used are of fixed size and formats that preserve visual quality. System evaluation is carried out by testing the visual quality of the stego image, the success rate of QR Code extraction, and the integrity of the encrypted data. The results are expected to conceal sensitive information visually while maintaining its confidentiality, with potential applications in electronic ID cards, digital certificates, e-tickets, and other confidential documents.

The development of digital technology has brought about significant changes in various aspects of life, including early childhood faith education. Today’s children are growing up in an environment familiar with digital media, making it necessary to adopt a catechetical approach that keeps pace with the times. This article aims to examine the use of digital catechesis as a tool for early childhood faith education. This study employs a qualitative method with a literature review approach, gathering data from books, scientific journals, research articles, Church documents, and other relevant sources. The data is analyzed using descriptive-qualitative methods to understand the benefits, challenges, and role of digital catechesis in fostering children’s faith. Research findings indicate that digital catechesis can serve as an effective, engaging, and interactive medium for helping children learn about the teachings of the faith from an early age. The use of animated videos, religious songs, educational images, and learning apps can enhance children’s interest in learning, attention, and understanding of Catholic faith values. Furthermore, digital catechesis also assists the Church, families, and schools in providing faith education that is more contextual and aligned with the world of today’s children. However, the use of digital media still requires the guidance of parents, teachers, and faith mentors so that children receive proper direction and are protected from the negative impacts of technology. Thus, digital catechesis is a relevant tool in the faith education of young children when used wisely and purposefully. The presence of digital media does not replace the role of faith educators but serves as a tool that enriches the process of proclaiming the faith in the modern era.

Corona Virus Disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The first case was identified in Wuhan, China, in December 2019. The World Health Organization declared COVID-19 a Public Health Emergency of International Concern (PHEIC) on January 30, 2020, and a pandemic on March 11, 2020. This study aimed to determine the results of CT Value images on the first and second swab tests of children with COVID-19 in Indramayu. A descriptive design was used with total sampling, involving 54 respondents. Secondary data were analyzed using univariate analysis. On the first swab test, 27 children (50.0%) had a strong positive Ct Value, 26 children (48.1%) were positive, and 1 child (1.9%) had a weak positive Ct Value. On the second swab test, 23 children (71.9%) were positive, 6 children (18.8%) were strong positive, and 3 children (9.4%) were weak positive. All children tested positive on the first swab, while 32 (59.3%) remained positive and 22 (40.7%) became negative on the second swab. Most children showed decreased infection severity, although some remained positive.

Sign language recognition is one of the areas of image recognition and image processing technology that is developing rapidly in human-computer interaction. This technology really helps the deaf and speech impaired in communicating with non-disabled people. This research aims to examine the optimization of an object tracking system in sign language using the Gaussian Mixture Model (GMM) and Kalman Filter by including the Region of Interest (ROI). The proposed system consists of three main components, namely hand detection, object extraction, and classification. Hand detection is done using the Kalman Filter to track hand movements accurately. Next, Region of Interest (ROI) features, such as shape, direction and movement features, are extracted from the detected part of the hand. These features are fed into a Gaussian Mixture Model (GMM) classifier, which can recognize sign language based on the extracted features. With the combination of GMM and Kalman Filter in this research, it can increase accuracy in object tracking, reduce interference from the background, and ensure the tracking focus remains on important objects. The dataset used is in the form os SIBI alphabet symbols, namely A-Z with the amount of data for each class, namely 620 images. Based on the research result, model testing using GMM, Kalman Filter and ROI produces higher accuracy of 99%, while model testing using GMM and ROI produces accuracy of 90%.

Rebonding is a chemical hair straightening procedure widely popular in Indonesia, involving two-stage redox reactions on keratin disulfide bonds using ammonium thioglycolate and hydrogen peroxide. Although it provides effective hair-straightening results, rebonding potentially causes significant damage to the hair cuticle structure. This study aimed to analyze the morphological changes of hair surface before and after rebonding treatment through a descriptive comparative case study approach using Scanning Electron Microscopy (SEM). The samples consisted of one strand of healthy, chemically untreated hair (control) and one strand of post-rebonding hair that both obtained from a female. Samples were prepared using gold-sputter coating prior to SEM observation. Representative SEM images from each sample were then qualitatively compared based on cuticle morphology parameters. The results showed clear morphological differences between the two samples: the control hair displayed tightly arranged and orderly cuticle scales with a smooth surface, while the post-rebonding hair showed surface erosion and the presence of cracks in multiple areas of the cuticle. These findings confirm that rebonding procedures cause visible and characterizable morphological cuticle damage as identified through SEM, providing a scientific basis for developing more protective post-rebonding hair care products.

Artificial intelligence has the potential to support radiology workflows by assisting in the identification of cases that may require additional clinical attention. However, alert-oriented medical AI systems should provide not only classification outputs but also interpretable evidence that can be reviewed and audited by clinicians. This study develops and evaluates an explainable multimodal framework for binary chest X-ray alert classification using paired radiology reports and chest X-ray images. The text branch employs TF-IDF n-gram features with a class-balanced Logistic Regression classifier, while the image branch fine-tunes a pretrained ResNet18 model. The two branches are integrated through probability-level late fusion using a validation-selected fusion weight. Explainability is implemented in a modality-specific manner: global coefficient analysis is used to identify influential textual cues, while Grad-CAM heatmaps are used to visualize salient image regions. Experiments were conducted on paired samples from the Open-i/IU X-Ray dataset using text-only, image-only, and fusion-based evaluation settings. Additional analyses include case-level complementarity analysis, bootstrap confidence intervals for ROC-AUC, shortcut-feature inspection, and qualitative Grad-CAM auditing. The results indicate that the text modality provides the dominant predictive signal under the current proxy-label setting. Late fusion produced a small descriptive improvement on the test set, increasing accuracy from 0.8533 to 0.8667, F1-score from 0.8817 to 0.8936, and ROC-AUC from 0.8936 to 0.9025 compared with the text-only baseline. However, the observed ROC-AUC improvement was not statistically conclusive based on bootstrap analysis. These findings suggest that the proposed framework is useful as a reproducible and auditable multimodal prototype, while also highlighting important limitations, including proxy-label ambiguity, potential label leakage from radiology reports, limited image-branch contribution, lack of external validation, and the need for stronger explanation and calibration assessment.

Fiqh learning in Madrasah Ibtidaiyah is often considered difficult and boring by students because the material is abstract and requires practical understanding. Therefore, a fun and easy-to-understand learning method is needed. This study aims to describe the application of joyful learning using pop-up book media in improving students' understanding of fiqh on ablution material in class II of Madrasah Ibtidaiyah Raudatul Jannah Tiris Probolinggo. This study is a descriptive qualitative study conducted with data collection techniques through observation, interviews, and documentation. The results showed that the use of pop-up book media was able to create a more lively and enjoyable learning atmosphere. Students looked more enthusiastic, focused, and active during the learning process. The display of three-dimensional images in the pop-up book helped students understand the sequence and procedures of ablution more clearly and made it easier for them to practice. Therefore, the use of a joyful learning approach combined with pop-up book media was able to create a fun learning atmosphere and improve students' understanding of the material.

This study aims to analyze fifth-grade students’ understanding of the water cycle concept at SD Muhammadiyah Sidayu using animated video assistance, as well as to describe supporting and inhibiting factors, identify obstacles faced by teachers and students, explain teachers’ efforts, and examine students’ responses in science learning. The research used a descriptive qualitative method with 23 fifth-grade students as participants. Data were collected through tests, questionnaires, interviews, and observations, and analyzed using data reduction, data presentation, and conclusion drawing. Data validity was ensured through triangulation of technique, source, and time. The results indicate that students’ understanding of the water cycle concept is categorized as moderate, with an average score of 69.43. Students are able to explain the definition and stages of the water cycle through images, classify events based on similarities in processes, and distinguish between evaporation and condensation. However, they still face difficulties in explaining the relationships between processes and in providing real-life examples related to the water cycle. Supporting factors include students’ interest and learning motivation, while inhibiting factors involve differences in comprehension abilities and students’ health conditions. Teachers face obstacles such as limited audio-visual facilities, shared LCD usage, and challenges in selecting appropriate animated videos. To overcome these issues, teachers use simple explanations, emphasize key points, replay videos, provide individual guidance, and assign diagram-based projects. Students’ responses are very positive, as animated videos increase their interest, attention, motivation, and conceptual understanding.

Understanding mathematical concepts is a fundamental ability that elementary school students must possess, particularly in multiplication, which serves as a foundation for learning more advanced mathematical topics. Based on the conditions observed in the fourth-grade class at UPT SDN 185 Gresik, there are still students who do not fully understand the concept of multiplication, as the learning process tends to emphasize memorization rather than conceptual understanding. In addition, differences in learning styles and gender are suspected to influence students’ conceptual understanding abilities. Therefore, this study aims to describe the conceptual understanding of fourth-grade students in multiplication, viewed from their learning styles and gender.This study employs a descriptive qualitative research method, with six fourth-grade students of UPT SDN 185 Gresik as the research subjects. Data were collected through learning style questionnaires, concept understanding tests and questionnaires, and interviews. Data analysis was conducted through the stages of data reduction, data presentation, and conclusion drawing, based on indicators of conceptual understanding. Data validity was ensured using triangulation of techniques, sources, and time. The results of the study indicate that auditory learners tend to have better conceptual understanding compared to visual and kinesthetic learners, and female students outperform male students. Based on these findings, teachers need to implement differentiated instruction tailored to students’ learning styles: using diagrams, images, and colored multiplication tables for visual learners; applying discussions, question-and-answer sessions, and repeated verbal explanations for auditory learners; and utilizing concrete teaching aids along with hands-on activities for kinesthetic learners to optimize conceptual understanding.

The coal mining industry requires accurate stockpile volume measurements for inventory and production reporting. Conventional methods have limitations in accuracy, efficiency, and safety. This study compares the accuracy and efficiency of coal stockpile volume measurements using a Terrestrial Laser Scanner (TLS) Leica MS60 and an Unmanned Aerial Vehicle (UAV) DJI Matrice 4E, validated by the ASTM D6172-98 standard. Conducted on five Run of Mine (ROM) coal stockpiles covering 13,500 m² at PT XYZ, Lahat, South Sumatra, the TLS method used 43 scan positions, while the UAV employed 430 aerial images with specific flight parameters. Data were processed using Leica Infinity, Surpac, and Agisoft Metashape. The results showed volumes of 94,076 m³ (TLS) and 94,965 m³ (UAV), with a difference of 889 m³ (0.95%). Volume deviations ranged from 0.48% to 1.89%, with an average of 1.42%, all within the ASTM tolerance (<2%). Time efficiency analysis revealed that the UAV method required 200 minutes (3.33 hours), saving 63.3% (approximately 6.17 hours) compared to the TLS method (570 minutes). The largest efficiency gain occurred during field data acquisition, with an 85% reduction in time. This study confirms UAV photogrammetry as a valid, accurate, and efficient alternative for coal stockpile volume measurement in mining.

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 explores how young legislative candidates at the local level utilize Instagram as a campaign medium in the era of political digitalism. The focus is on two DPRD candidates in Tangerang City during the 2024 election in Electoral District 3 (Cipondoh–Pinang): Muhamad Azka Nur Fauzi from the National Mandate Party and Ashma Nafilah Maulida from the Prosperous Justice Party. Employing a qualitative descriptive approach and in-depth interviews with key informants, this research analyzes their personal branding strategies using Peter Montoya’s eight laws of personal branding.  The findings reveal that despite their relatively small number of followers, both candidates successfully built authentic and community-relevant political images. MANF emphasized UMKM development and religiosity, aligning with his personal background, while ANM highlighted humanistic social programs and her unique writing hobby. Nevertheless, both still showed weaknesses in several of Montoya’s principles, particularly distinctiveness and visibility consistency. The study concludes that Instagram is not merely a low-cost promotional tool but a strategic platform for local candidates to foster public trust, provided their personal branding remains authentic, consistent, and responsive to local needs.

Background: Trigeminal neuralgia (TN) is often associated with neurovascular compression in the trigeminal nerve root entry zone, necessitating the simultaneous visualisation of nerves and blood vessels. Fusion of 3D SPACE and 3D TOF MRA images provides an integrated neurovascular view; however, not all hospitals have fusion software. This study developed a MATLAB-based image fusion method as an alternative and evaluated its equivalence to hospital-based fusion software.Methods: This study employed a descriptive quantitative research design, conducted in November 2025 at Diponegoro National Hospital and Dr Kariadi General Hospital in Semarang. A total of 16 brain MRI datasets (3D SPACE and 3D TOF MRA) were fused using hospital software and the MATLAB fusion application (MATLAB R2025b GUI). The fusion results were assessed by specialist radiologists. Diagnostic performance metrics (sensitivity, specificity, NDP, NDN, accuracy) were calculated, and paired differences were tested using the McNemar test. Intra-observer reliability was assessed using percentage agreement and Cohen’s Kappa.Results: MATLAB fusion yielded a sensitivity of 90.91%, specificity of 80.00%, NDP of 90.91%, NDN of 80.00%, and accuracy of 87.50%; the McNemar test (p=1.000) indicated no significant difference. Intra-observer reliability was very good (percent agreement 94%; Kappa 0.875). These findings indicate that MATLAB-based fusion is equivalent to hospital software fusion on the study data and has the potential to serve as an alternative in facilities without fusion software, provided that registration standardisation and user training are in place.

This study aims to design and develop a functionally efficient and operationally effective Internet of Things (IoT)-based air quality monitoring system for radiology departments. The system utilises a DHT22 sensor integrated with an ESP32 microcontroller to monitor the temperature and humidity of diagnostic rooms in real time, and to display the data via the UdaraKu mobile application. The research method employed a quantitative experimental approach focused on measuring system performance, specifically the accuracy of the temperature and humidity sensors. The research model used was the Research and Development (R&D) method, aimed at transforming conventional air quality monitoring in radiology into a real-time digital system based on IoT. The research results indicate that the IoT-based monitoring system is capable of maintaining room temperature and humidity stability within the ideal range, namely 22–24°C and 50–60% RH, in accordance with international standards. This improvement in environmental stability has a direct impact on reducing noise in digital radiography images, as evidenced by an increase in the Signal-to-Noise Ratio (SNR). Instrument validation demonstrated a high level of reliability with a Cronbach’s Alpha value of 0.848, reinforcing the reliability of the data and the system. Overall, the IoT-based air quality monitoring system has proven effective in controlling noise in digital radiography images, improving the quality of diagnostic services, and supporting patient safety principles and operational efficiency within radiology departments.