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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.

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.

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%.

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.

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.

This paper explores the role of Mary as a model of salvation in Catholic faith, focusing on the understanding of Mariology within the cultural context of the Ende-Lio people in Flores, East Nusa Tenggara. Mary is regarded not only as the Mother of Jesus Christ but also as an example of faith marked by humility, obedience, and hope. Her humility and willingness to accept God’s will, especially in the Annunciation, serve as a concrete inspiration for Catholics to respond faithfully to God’s call. Within the Ende-Lio culture, which values loyalty, solidarity, respect for ancestors, and harmony with nature, Mary becomes a figure closely connected to daily life. Devotions such as the rosary, pilgrimages to Marian grottoes, and the celebration of the Marian months highlight Mary’s living presence in both the faith and cultural traditions of the community. Thus, Mary serves as a bridge between the Gospel and local culture, revealing a Church that is rooted in its own soil yet remains faithful to Christ. This reflection affirms that contextual faith is a living faith deeply rooted in local culture while open to the saving work of God.

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.

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 explore students’ perceptions of using Canva as a digital storytelling platform to enhance their visual and written narrative skills in English writing. The research employed a descriptive qualitative method involving 34 students who participated in a questionnaire consisting of eight closed-ended statements and two open-ended questions. The data were analyzed descriptively, using percentages to support qualitative interpretation. The findings revealed that most students perceived Canva as an accessible, enjoyable, and effective tool for developing writing skills. Students agreed that Canva helps them visualize ideas, organize stories logically, and combine words with images creatively. Moreover, Canva was found to improve students’ motivation, confidence, and emotional expression in English writing. These results are consistent with previous studies by Utami and Suriyani (2022), Febriansyah et al. (2023), Siregar et al. (2024), Herwani (2024), Eragamreddy and Joseph (2025), Hafidzin et al. (2025), and Jayanti et al. (2025), which collectively highlight Canva’s role in supporting writing engagement, creativity, and organization. The study concludes that Canva transforms traditional writing into a multimodal and meaningful process, combining linguistic and visual literacy. Therefore, Canva can be considered an innovative pedagogical tool that promotes creative thinking, digital competence, and expressive communication in the English writing classroom.

The development of technology in the era of globalization has brought significant changes in society, particularly through the emergence of the internet and social media such as WhatsApp, X (Twitter), Facebook, Instagram, Telegram, and TikTok, which facilitate rapid information dissemination. This development has also given rise to a new profession, namely content creators, who produce and share content in the form of images, videos, or text for branding, professional purposes, or self-expression, often resorting to sensationalism to attract audience attention. On the other hand, the ease of access to social media has also triggered the spread of negative content, including pornography, as evidenced by Komdigi/Kominfo data showing millions of blocked negative content, with X being one of the dominant platforms. In Islamic perspective, anything that leads to adultery is prohibited as stated in QS. Al-Isra verse 32. A prominent case is Dea OnlyFans (Gusti Ayu Dewanti) who was arrested for distributing pornographic content through OnlyFans and Google Drive, charged under the Pornography Law and ITE Law, and found guilty in the Supreme Court Decision Number 2086 K/Pid.Sus/2023. This study discusses 1) How are the differences in judges' considerations at the District Court, High Court, and Cassation? 2) Can the Supreme Court judges' considerations provide a deterrent effect? This research uses a descriptive method with normative legal research based on literature study, using primary, secondary, and tertiary legal materials.

This study examines Marian devotion in the Catholic Church as a form of veneration (dulia) rather than worship (latria), addressing persistent misconceptions that equate Catholic devotional practices with idolatry. The research aims to identify the forms of Marian devotion practiced by Catholics in Indonesia and to analyze the challenges encountered in living out these practices. A descriptive qualitative approach was employed using a Systematic Literature Review (SLR) guided by the PRISMA framework. Two hundred articles were initially identified through Google Scholar and screened according to relevance, recency, geographical context, and methodological criteria, resulting in four eligible studies for analysis. The findings reveal four principal forms of devotion: the use of statues as aids to prayer, pilgrimages to Marian sites, Montfort-style Marian consecration, and the rosary emphasizing praise, imitation, and prayer with Mary. These practices deepen faith and lead the faithful closer to Christ without replacing divine worship. Challenges include external religious intolerance toward devotional symbols and limited theological understanding among believers. Therefore, sound catechesis grounded in Scripture and Church Tradition is essential to ensure authentic and balanced Marian devotion.  

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.

Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.

Background: Accurate detection of renal cysts on CT urography requires high diagnostic precision, while manual interpretation by radiologists is susceptible to inter-observer variability and potential delays in clinical decision-making. These challenges underscore the need for a reliable automated detection system to support radiological assessment. Objective: This study aims to develop and evaluate the performance of the Neo-ZasAI application based on the YOLOv8 algorithm for the automatic identification of renal cysts. Methods: Employing a Research and Development design using the ADDIE model, the study encompassed needs analysis, model design, software development, system implementation using 200 CT urography images, and diagnostic performance evaluation. Classification results generated by Neo-ZasAI were compared with radiologist readings through confusion matrix analysis and ROC–AUC assessment. Results: The findings indicate that Neo-ZasAI achieved an accuracy of 97,5%, sensitivity of 96%, specificity of 99%, positive predictive value of 98,9%, and negative predictive value of 96,1%. The ROC analysis yielded an AUC of 0.988 (p < 0.001), demonstrating excellent discriminative capability and high concordance with radiologist interpretations as the diagnostic gold standard. Conclusion: These results suggest that Neo-ZasAI is capable of performing rapid, consistent, and accurate renal cyst detection and is thus feasible for implementation as a clinical decision support system in radiology, with potential integration into PACS workflows and further development to enhance model generalizability.

Digital images often experience noise disturbances that can reduce visual quality and interfere with the image analysis process. One common type of noise is salt and pepper noise, especially in grayscale images, which is characterized by the random appearance of black and white dots. This study applied the Deep Convolutional Autoencoder (DCAE) method with a skip connection mechanism to eliminate salt and pepper noise in grayscale images measuring 256×256 pixels. The dataset used consists of 300 pairs of clean images and noisy images that have gone through the preprocessing stage, including normalization and data augmentation. The model was trained using an Adam optimizer with a Mean Squared Error (MSE) loss function and validated through a train-test split scheme to avoid overfitting. Model performance was evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. The test results showed that the DCAE model with skip connections was able to effectively reduce noise while maintaining the main structure of the image based on the PSNR and SSIM values obtained, and showed better performance than conventional median filters. In addition, the model was successfully implemented into a Streamlit-based application to perform the image denoising process interactively, making it easier for users to experiment and visualize results in real-time.

This study highlights the role of social media, particularly Instagram, as a digital communication tool to promote and preserve the local culture of Paubokol Village. This village has a unique cultural heritage, but its recognition and appreciation are still limited among people outside the area. By utilizing Instagram as the primary medium, this study focuses on effective digital communication strategies in presenting visual content and cultural narratives. The communication techniques applied include selecting representative images, creating engaging stories, and delivering information interactively to attract a wider audience. The results of this activity indicate that the use of Instagram not only increases public awareness of the cultural values ​​of Paubokol Village but also builds more constructive relationships between local residents and viewers from outside the area. Engaging visual content and informative narratives play a significant role in fostering a higher appreciation of cultural heritage and encouraging support for the preservation and development of the village's potential. This study illustrates how social media can function as a strategic communication tool in promoting local culture, expanding the reach of information, and creating a dynamic space for cultural interaction between the village community and the wider public. These findings open up opportunities for the development of more innovative digital strategies for local cultural preservation.  

Film is a communication medium capable of conveying moral messages to the public through a series of moving images, usually accompanied by sound. One such medium is the short film. "The waiting room" by Galih Firdaus, a production of PT Kereta Api Indonesia (KAI) released in April 2024 and received positive acclaim for its engaging plot and relevance to the audience's reality. This study aims to analyze the moral message in the film using Charles Sanders Pierce's semiotic theory, which emphasizes three main elements: Representation, Object, and Interpretant. The study used a descriptive qualitative approach with a content analysis method to uncover the moral meanings behind the film's scenes. The results found 15 scenes that explicitly convey moral values, including the importance of open communication within the family, the meaning of simple kindness, valuing togetherness, the commitment of true love, a mother's fortitude, loyalty in waiting, and sincere care. The moral message is constructed through Representation (physical signs such as a bowl of soto), Object (the reality conveyed, such as an apology), and Interpretant (the audience's understanding that the soto symbolizes improving relationships). In conclusion, the film "The waiting room" successfully conveys a strong moral message through a neatly structured narrative.

In the increasingly tight competition of the soft drink industry, companies need to understand how the advertisements broadcast are able to attract attention, arouse interest, and encourage consumers to make purchases. The purpose of this study is to measure 1) The television advertisement of Nutrisari version of the champion nyegerin on residents of the Graha Indah 2 Pamulang complex. 2) Purchasing decisions on residents of the Graha Indah 2 Pamulang complex. 3) The influence of the television advertisement of Nutrisari version of the champion nyegerin on product purchasing decisions on residents of the Graha Indah 2 Pamulang complex. The theory used in this study is the Marketing Communication Theory. Television Advertisements with dimensions: Head words & sound effect, Music, Seen word, Picture, Color, and Movement, as well as the Purchasing Decision theory with dimensions: Problem Recognition, Information Search, Alternative Evaluation, Purchase Decision, and Post-Purchase Behavior. This study uses a quantitative approach with a survey method with a questionnaire as the data collection instrument. The population in this study were residents of the Graha Indah 2 Pamulang RW 25 complex with characteristics that had been found as many as 123 and the number of samples as many as 94. The sampling technique used probability sampling technique. The results of the study showed that 1) The images presented in Nutrisari advertisements made the audience's attention interesting and got the highest average value of 3.28 from the Picture dimension. 2) In the Purchase Decision, there was the highest value of 3.21 from the Purchase Decision dimension. 3) There was an Influence of Nutrisari Television Advertisement Version of Si Juara Nyegerin on Product Purchase Decisions. t-count was (9.992) > t-table (1.661). The magnitude of the influence was R 0.719 or 71.9% and R Square was 0.517 or 51.7%