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Literacy skills in elementary schools play a crucial role in supporting students’ academic development and overall learning success. However, observations conducted at SDN 2 Demulih revealed that many students still experience difficulties in comprehending reading materials and retelling stories coherently and systematically. This study aims to enhance students’ literacy and language skills through the implementation of the retelling technique as an instructional strategy. A descriptive research approach employing the ADDIE development model was applied, consisting of five stages: analysis, design, development, implementation, and evaluation. The program involved 21 students from grades IV–VI and utilized two audiovisual story media, namely “Independence” and “Bawang dan Kesuna.” The results indicate that the application of the retelling technique based on audiovisual media effectively improved students’ reading comprehension, learning activeness, and self-confidence in speaking activities. Students were able to reconstruct storylines accurately and express moral messages using their own words. Therefore, the retelling technique is proven to be an engaging, practical, and effective literacy learning strategy for elementary school students.

Islamic character education faces dual challenges: declining moral values among students and conventional teaching methods inadequate for developing higher-order thinking skills. This study examines the implementation of a smart learning environment (SLE) model to enhance metacognitive understanding in Aqidah Akhlak (Islamic Creed and Ethics) instruction among eighth-grade students at MTs Salafiyah Merakurak. Employing a mixed-methods action research design, involved 16 eighth-grade students divided into two groups. Data collection utilized classroom observations, semi-structured interviews, and learning documentation. The SLE model integrated Google Classroom, interactive video content, WhatsApp Group discussions, and Google Forms assessments to create a technology-enhanced learning ecosystem. Findings revealed substantial improvements in students' metacognitive capacities: planning skills increased from 25% to 75%, monitoring abilities rose from 31% to 81%, and evaluation competencies grew from 19% to 69%. Students demonstrated enhanced learning autonomy, active participation in collaborative discussions, and improved self-reflection on content comprehension. The SLE approach successfully fostered engaging learning experiences while facilitating deeper internalization of Islamic ethical values. However, implementation encountered constraints including limited technological infrastructure and varied digital literacy levels among students. This research underscores the critical need for developing teachers' digital competencies and strengthening madrasah technological infrastructure to optimize technology-integrated Islamic education.

This study evaluates the AIESEC Incoming Global Volunteer (IGV) Program at the Veteran National Development University in Yogyakarta using the CIPP (Context, Input, Process, Product) evaluation model. Employing a descriptive qualitative approach, data were collected through interviews, non-participatory observation, and documentation studies, with validity ensured through triangulation. The findings reveal that the IGV Program is highly relevant to the university’s internationalization agenda and contributes significantly to strengthening cross-cultural competencies among students. The availability of resources and the overall implementation of the program have been effective, though improvements are needed in ensuring consistent mentoring for international participants. The evaluation highlights that the program has generated positive outcomes, particularly in enhancing intercultural competencies and fostering collaboration with local partners. These results underscore the importance of sustaining and refining the IGV Program as a strategic initiative to support global engagement and student development.

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.

This psychoeducation is motivated by the need for service support for students with special needs at SLB Negeri Pembina Yogyakarta. Some students with intellectual disabilities require assistance in recognizing and expressing their emotions. The purpose of this counseling activity is to provide appropriate media needed to support the development of students’ emotional expression in everyday social interactions. The counseling method employed was a play-based learning approach. The evaluation of the effectiveness of this method involved 10 students with intellectual disabilities, consisting of 7 male students and 3 female students in Grade VII of junior high school. Based on the results of the counseling activities, which were conducted in three stages—namely the opening stage, the emotion recognition stage, and the application of the emotion wheel—it was found that the use of the emotion wheel media strengthened the emotional expression abilities of students with intellectual disabilities. This improvement was evident from observable changes in the emotional behavior of some students.

Digital-based learning evaluation is an important effort to improve the effectiveness and efficiency of the assessment process. However, learning evaluation practices at MTsN 2 Kota Tangerang are still dominated by conventional methods and the limited use of simple digital applications. This community service activity aims to strengthen digital-based learning evaluation practices through training on the use of the Zep Quiz application for teachers. The activity employed a participatory and applicative approach, including observation, focus group discussions, training and mentoring, and program evaluation. The training was conducted in a hybrid format and focused on introducing, developing, and analyzing learning evaluations using Zep Quiz. The results indicate that teachers improved their understanding and skills in designing and operating digital-based learning evaluations independently.Teachers also demonstrated high enthusiasm and active participation throughout the activity. Although technical challenges such as varying levels of digital literacy and limited internet access were encountered, these issues were addressed through direct mentoring. Therefore, the Zep Quiz training was effective in strengthening digital-based learning evaluation practices at MTsN 2 Kota Tangerang.

Public speaking is an essential communication skill that plays a crucial role in enhancing self-confidence, delivering ideas effectively, and fostering social participation. However, many women in rural areas still lack opportunities to develop this skill due to limited access to training, gender stereotypes, and minimal experience in public forums. These challenges lead to low levels of women’s involvement, particularly housewives who are members of PKK, in community activities and decision-making processes at the local level. This study aims to examine the impact of public speaking training on improving the knowledge, self-confidence, and social participation of PKK mothers in Nagari Sumpur Kudus. The research employed a descriptive qualitative method with a participatory approach through interactive lectures, speaking practice simulations, reflective discussions, participatory observations, and qualitative evaluations. Data were analyzed using data reduction, data display, and conclusion drawing based on field notes, observations, and participants’ feedback. The findings indicate that the training successfully improved participants’ understanding of basic public speaking techniques, including intonation, eye contact, body language, and structured idea delivery. Furthermore, the participants experienced significant growth in self-confidence, courage to speak in front of groups, and motivation to continuously practice their communication skills. The interactive and supportive learning atmosphere also strengthened collaboration and solidarity among PKK members. Thus, public speaking training not only enhances individual competencies but also contributes to women’s empowerment and their active participation in community development at the village level.  

Chronic Kidney Disease (CKD) is a heterogeneous disorder that gradually affects the structure and function of the kidneys, is difficult to recover, and causes the body to be unable to maintain metabolism and fail to maintain fluid and electrolyte balance, leading to increased urea levels. Chronic kidney disease data was obtained from Kaggle, in this study a comparison was made between two classification algorithms, namely Naïve Bayes Classifier (NBC) and Random Forest because it is not yet known what algorithm is best in classifying chronic kidney disease (CKD). Both algorithms are evaluated based on performance metrics such as accuracy, precision, recall, and confusion matrix. The results of the evaluation showed that in a dataset of 400 samples, the performance  of the Naïve Bayes Classifier (NBC) algorithm obtained an accuracy of 94%, while Random Forest had an accuracy of 93%. Then in the small dataset (158 data), Random Forest got a better accuracy score with 87% compared to the Naïve Bayes Classifier (NBC) of 78%. Based on the results of the evaluation, Random Forest has a more stable performance on small datasets, while Naïve Bayes Classifier (NBC) provides higher performance on larger datasets in the context of chronic kidney disease classification.

The Mastery Learning Strategy is a learning approach oriented towards achieving comprehensive student competencies before they move on to the next material or learning stage. This approach is based on the assumption that every student has the potential to succeed, provided they are given the appropriate time, methods, and guidance. In its application, Mastery Learning emphasizes systematic learning planning, the establishment of clear learning objectives, and ongoing evaluation to measure the level of student mastery. Students who have not yet achieved competency standards will receive corrective feedback and remedial activities, while students who have completed them will be provided with enrichment programs to deepen their understanding. With this mechanism, learning gaps can be minimized so as not to hinder the learning process in the next stage. Furthermore, this strategy encourages individualized, structured, and measurable learning according to student needs. Therefore, the implementation of Mastery Learning is considered effective in improving the quality of the learning process, strengthening conceptual understanding, and contributing to optimal and sustainable learning outcomes.  

This study aims to describe the integration of environmental education into early childhood learning activities at TK Prawidya Dharma Demulih through the use of recycled waste as a creative and educational learning medium. The study was motivated by the low environmental awareness among children and the limited use of environmentally themed learning media in the institution. A qualitative descriptive approach was applied using the ADDIE development model, consisting of the stages of analysis, design, development, implementation, and evaluation. The findings reveal that employing a recycled-material spinner game enhanced children’s understanding of environmental cleanliness and encouraged environmentally responsible behavior through playful learning activities. The children showed strong enthusiasm, participated actively, and began to develop habits related to cleanliness after the learning sessions. Moreover, teachers gained new insights into designing innovative and functional learning media using discarded materials. Overall, the use of recycled waste as an educational tool proved effective in fostering environmental awareness while supporting creativity and meaningful learning experiences for early childhood learners..

Digital portfolios are a crucial tool for professionally showcasing abilities and learning objectives. Nevertheless, the projects and certificates of participants in the Independent Study program at VINIX7 are still kept apart and are not controlled by an integrated system. This requirement is the basis for the research's design and development of a website-based digital portfolio system that will serve as participants' key platform. Requirements analysis, system design, implementation, and testing are all steps in the Waterfall approach of system development. The Laravel framework was used to create the VinixPort website, which is backed by a MySQL database. The system has tools for managing portfolio material, talent evaluation, user registration and login, and data presentation via analytics. The study's findings show that the VinixPort website was created successfully and that all of the system's primary features work as intended. This service helps users create organized digital portfolios that are readily available and prepared for both professional and academic use.

AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.

This study conducts sentiment analysis of Indonesian user reviews of the CapCut application using IndoBERT and compares two evaluation schemes: a single 80/20 train–test split and stratified 5-fold cross-validation (k=5). A total of 1,048,575 reviews were collected from the Google Play Store through web scraping and labeled into three sentiment classes based on rating: negative (1–2), neutral (3), and positive (4–5). After preprocessing—cleaning, case folding, banned-word removal, normalization—and duplicate removal, 517,962 reviews were retained. IndoBERT Base P1 was fine-tuned using fixed hyperparameters (batch size 32, learning rate 2e-5, up to 4 epochs, early stopping patience 2), while undersampling was applied to the training set to address class imbalance. Performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC, supported by confusion matrix and ROC-curve visualizations. The single split achieved an accuracy of 0.756, whereas cross-validation produced a mean accuracy of 0.740. Across both schemes, the positive class achieved the best performance (F1-score 0.850; ROC-AUC 0.918–0.919), while the neutral class remained the most challenging (precision 0.198–0.206; F1-score 0.280–0.283). Overall, cross-validation is recommended for reporting because it reduces dependence on a single partition and provides a more representative estimate across multiple splits.

Sudden Infant Death Syndrome (SIDS) is a sudden and unexpected death in infants that is often associated with the prone sleeping position. This study aims to develop an automated monitoring system capable of detecting SIDS risk factors using the YOLOv8 algorithm and to analyze the effect of data augmentation on model performance. The dataset consists of two classes, baby-lying-on-back (supine) and baby-lying-on-stomach (prone), which were processed through model training and evaluation using precision, recall, F1-score, and mAP metrics. The model was trained under two scenarios, without data augmentation and with data augmentation. The results show that the model without augmentation achieved a precision of 90%, recall of 85%, F1-score of 86%, and mAP50 of 93.7%. After applying augmentation, performance improved to a precision of 90%, recall of 87%, F1-score of 88%, and mAP50 of 95.1%. These findings indicate that augmentation increases detection accuracy and enhances model generalization, including robustness against variations in lighting and camera angles. Furthermore, testing with image and video inputs revealed that the non-augmented model exhibited a tendency toward overfitting, particularly in favor of the baby-lying-on-stomach, whereas the augmented model successfully classified both classes accurately. The developed system is also equipped with an alarm feature and early-warning notifications via Telegram to smartphone when a prone position is detected for a certain duration. Overall, the results demonstrate that YOLOv8 with data augmentation is effective for an automated, non-invasive monitoring system for infants, making it suitable for detecting and preventing potential SIDS risk factors.

This community service activity aims to strengthen the role of higher education institutions in preserving local culture through the revitalization of cultural arts learning based on local wisdom, particularly traditional Nias carving art. The main problems faced by the partners include the limited availability of contextual cultural arts learning, minimal integration of traditional art practices into university courses, and students’ low understanding of the philosophical values embedded in Nias carving motifs. The implementation method employs a participatory–educational approach consisting of preparation, socialization, theoretical and practical training, intensive mentoring, and evaluation stages. This activity involves students and lecturers as participants as well as agents of cultural preservation. The results indicate a significant improvement in participants’ knowledge of the symbolic meanings of Nias carving motifs, their basic skills in designing and drawing carving motifs, and their appreciative attitudes toward the preservation of local cultural arts. This activity contributes to the strengthening of cultural arts learning in higher education and has the potential to serve as a sustainable model of community service based on local culture.

Digital transformation has significantly reshaped human resource management (HRM) through the adoption of Human Resource Information Systems (HRIS), artificial intelligence (AI), big data analytics, e-learning platforms, and remote work technologies. Although these innovations improve efficiency and decision-making, they also generate ethical challenges related to data privacy, algorithmic bias, transparency, and employee monitoring. This article examines the role of professional ethics in HRM within the context of digital transformation, highlighting both emerging challenges and potential opportunities. This study employs a conceptual research approach supported by a comprehensive literature review of scholarly works on HRM, professional ethics, and digitalization. The analysis focuses on core ethical principles such as integrity, fairness, responsibility, professionalism, and confidentiality, and evaluates their implementation in digital HR practices. The findings indicate that unethical use of digital technologies may lead to discrimination, reduced employee trust, and violations of individual rights, particularly through biased AI-based recruitment systems and opaque performance evaluation mechanisms. However, digital transformation also offers opportunities to strengthen ethical HR governance. The use of ethical data management, algorithmic audits, digital transparency, and e-learning-based ethics training can enhance accountability and fairness in HR processes. The study concludes that integrating professional ethics with digital HRM is essential for developing human-centered, sustainable, and trustworthy organizations in the digital era.

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

Sentiment analysis is a technique in Natural Language Processing (NLP) used to identify user opinion tendencies based on textual reviews. This study analyzer user reviews of the Maxim application on the Google Play Store and compares three Machine Learning algoritmhs-Naïve Bayes, Support Vector Machine (SVM), and CatBoost-in classifying sentiment. The research stages include data collection, text preprocessing, feature extraction using TF-IDF and Chi-Square, class balancing using SMOTE, and performance evaluation through Accuracy, Precision, Recall, and F1-Score. ANOVA is used to examine the influence of feature selection on model performance. The results show that each model exhibits different performance level across the tested feature combinations. The CatBoost achieved the highest accuracy of 99,26% and demonstrating the most stable performance. Meanwhile, the Naïve Bayes and SVM models experienced performance decreases experiments, especially after applying SMOTE. These findings indicate that the choise of algorithm, feature extraction method, and class balancing technique significantly affects classification outcomes. Overall, CatBoost is identified as the best-performing model, providing more consistenst classification result in accordance with the characteristics of the user reviews.

Raining activities on making ketupat weaving for adolescents are important to implement as an effort to preserve Indonesia’s local cultural heritage. Ketupat weaving is a traditional skill that contains cultural, social, and philosophical values, which are at risk of fading among younger generations. This community service program was specifically designed for adolescents with the aim of providing hands-on experience and practical skills in traditional ketupat weaving. The training was conducted through several stages, including preparation, direct practice, guidance, and evaluation. Participants were actively involved in the entire process, starting from selecting materials, learning basic weaving techniques, to completing the final woven ketupat forms. The results of the activity indicate positive outcomes, including the improvement of participants’ traditional crafting skills, increased awareness of cultural values embedded in ketupat weaving, and strengthened social interaction among adolescents. In addition, the training contributed to fostering a sense of cultural pride and responsibility in preserving local traditions. Overall, this community service activity demonstrates that practical and participatory cultural training can serve as an effective medium for cultural transmission, character development, and social engagement among adolescents, while supporting the sustainability of local cultural heritage in the modern era.

: The rapid expansion of cybersecurity standards and threat intelligence frameworks has led to significant semantic fragmentation among security terminologies, hindering effective information retrieval and interoperability across systems. Traditional keyword-based search approaches are inadequate for capturing the contextual meaning of security terms, particularly within formal frameworks such as NIST, MITRE ATT&CK, and CWE. This study addresses this challenge by proposing CyberBERT, a transformer-based semantic search framework designed to align cybersecurity terminologies through deep contextual representation and ontology-driven reasoning. Research Objectives: The primary objective of this research is to develop a semantic retrieval model capable of understanding conceptual relationships between security terms beyond lexical similarity. Methodology: The proposed methodology fine-tunes a BERT-based model on the NIST Glossary corpus using a combination of masked language modeling and triplet loss objectives to generate discriminative semantic embeddings. These embeddings are further aligned with cybersecurity ontologies, including MITRE ATT&CK and CWE, to enhance semantic consistency and explainability. Semantic retrieval is performed using cosine similarity within a 768-dimensional embedding space and evaluated using Mean Reciprocal Rank (MRR) and Precision@K metrics. Results: Experimental results demonstrate that CyberBERT achieves an MRR of 0.832, outperforming domain-adapted baselines such as SecureBERT and CyBERT. The integration of ontology alignment improves semantic accuracy by over 6%, while robustness evaluations confirm resilience against adversarial linguistic perturbations. Visualization using t-SNE reveals coherent semantic clustering aligned with the five core NIST Cybersecurity Framework functions. Conclusions: In conclusion, CyberBERT effectively bridges semantic gaps across cybersecurity terminologies by combining transformer-based contextual learning with ontological reasoning. The framework offers a robust, interpretable, and scalable solution for semantic search, supporting improved interoperability and knowledge discovery in cybersecurity operations and standards harmonization.