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This research presents the development of the Rembulan E Learning platform using Moodle as the primary Learning Management System for SMK Negeri 1 Pandeglang.The main objective is to provide a structured, accessible, and interactive digital learning environment that effectively supports teaching and learning activities, enhances student engagement, and facilitates teacher management of course materials. The study applies the Waterfall development model, which includes five stages requirement analysis, system design, implementation, testing, and deployment. Data collection methods involve observation of classroom practices, in depth interviews with teachers to identify pedagogical and technological needs, and comprehensive documentation review to ensure alignment with curriculum standards and user expectations. The resulting system integrates features such as digital classrooms, learning modules, assignments, discussion forums, quizzes, and student performance monitoring, offering a comprehensive digital learning experience. System testing was conducted using Black‑box Testing, complemented by limited user trials with teachers and students, which confirmed that the platform is functional, user friendly, and capable of supporting a variety of learning activities. This research contributes to the implementation of Moodle based LMS development in vocational schools, providing practical guidance for improving digital learning quality, promoting blended learning approaches, and facilitating sustainable adoption of educational technology in secondary education.

This classroom action research investigates the effectiveness of the Round Robin cooperative learning model in improving the motivation and creativity of learning science among Grade IV students at UPT SDN Unjuruiya No. 45, Selayar Islands. The study was conducted to address students’ low engagement, limited participation, and insufficient creative expression during IPAS lessons. The research involved 12 students (6 male and 6 female) and was implemented in two cycles, each consisting of three meetings—two for instructional activities and one for assessing learning creativity. Data were collected through observation, learning motivation assessments, and creativity evaluations. Findings indicate a significant improvement in student learning outcomes following the implementation of the Round Robin model. In Cycle I, only 5 students (42%) achieved the creative category, while 7 students (58%) remained in the non-creative category. However, after refinement and continued application in Cycle II, the number of students achieving the creative category increased substantially to 11 students (92%), leaving only 1 student (8%) in the non-creative category. These results demonstrate that the Round Robin model effectively enhances student motivation, encourages active participation, and supports the development of creativity in science learning. Overall, the study concludes that cooperative learning through Round Robin provides a meaningful and engaging instructional alternative capable of improving both motivational and creative learning aspects in elementary science classrooms.

The application of Mastery Learning in Olympiad mentoring at Darussalam Islamic Junior High School was carried out using a classroom action research model involving 20 students from grades 8 and 9. The mentoring process followed a cyclical structure consisting of planning, action, observation, and reflection, supported by observation techniques, tests, and documentation. The implementation of the mastery learning model effectively improved students’ mathematics learning outcomes, as shown by a significant increase in mastery levels: 65% before the intervention, 75% after the first cycle, and 90% after the second cycle. In addition, the average learning score rose from 71 in the first cycle to 87 in the second cycle, reflecting substantial progress in understanding and problem-solving abilities. These results indicate that Mastery Learning not only enhances students’ mathematics performance but also strengthens their readiness and confidence to compete in the National OSN competition, demonstrating the model’s positive contribution to academic achievement and Olympiad preparation.

Digital transformation and advances in artificial intelligence (AI) have fundamentally changed the demands of the workplace, creating a gap between graduate competencies and industry needs. This study aims to evaluate the effectiveness of AI Agent-based career adaptability psychoeducation as a community empowerment strategy to improve the work readiness of high school/vocational school and university graduates. The study design used a descriptive-interventional approach with 27 participants who participated in a four-week online training. Data were collected through a pre-post survey using the Career Adapt-Abilities Scale (CAAS) and qualitative observations during the training. The results of the Wilcoxon Signed-Rank test showed a significant increase in career adaptability scores (Z = –4.543, p < .001), with all participants experiencing increased career adaptability. Observations showed that participants became more confident, reflective, and proactive in designing their career directions after interacting with the AI ​​Agent. These findings indicate that psychoeducational interventions integrated with intelligent technology can strengthen the adaptive capacity and work readiness of the younger generation. Theoretically, this study expands the application of the career adaptability concept in the context of AI-based learning; In practice, the results provide a relevant community empowerment model for educational and employment institutions in the era of digital disruption.

This study aims to analyze the role of UINSU’s Community Service Program (KKN) students in enhancing Islamic entrepreneurial awareness among the residents of Pahang Village, Talawi District, Batu Bara Regency. The KKN program serves as a platform for applying academic knowledge while empowering communities through educational, persuasive, and participatory approaches. The students introduced fundamental concepts of Islamic entrepreneurship, including principles of justice, honesty, sustainability, and the avoidance of riba, gharar, and maisir. Through a series of activities such as seminars, micro-business training sessions, financial management mentoring based on sharia principles, and simple business model simulations, the villagers began to show improved understanding of Islamic economic values. Moreover, the active involvement of students created an inclusive learning environment that was accessible to various community groups. The findings indicate that the presence of KKN students contributed positively to cultivating entrepreneurial motivation, particularly among youth and micro-business actors. This improvement was reflected in changes in mindset, interest in initiating halal businesses, and increased awareness of applying sharia principles in daily economic activities. Thus, the KKN program not only provides practical experience for students but also offers a tangible contribution to strengthening sharia-based economic empowerment at the village level.

Bullying in Indonesian educational settings remains a serious problem that requires widespread attention. Poor legal understanding among children and adolescents, coupled with suboptimal child protection regulations, increases the risk of violence in schools. To address this issue, a "Fight Bullying" outreach program was conducted in Sumeith Pasinaro Village, aimed at improving students' understanding of the definition, forms, impacts, and legal consequences of bullying. This program utilized a qualitative descriptive approach and a participatory education model, involving 47 students from Sumeith Pasinaro Elementary School and Elpaputih 2 Junior High School. The methods employed included material delivery, interactive discussions, and assessments to provide students with an in-depth understanding of the legal education provided. The evaluation results showed a significant increase in students' understanding of bullying, with an average level of understanding reaching 80.83%. Students were able not only to identify the types of bullying but also to understand the long-term impacts on both victims and perpetrators. This outreach program also helped students understand the legal regulations governing bullying and encouraged them to report or stop such acts. This program emphasizes the importance of a planned and sustainable approach involving teachers and parents to prevent bullying, as well as creating a safe learning environment and supporting child protection.

Digital transformation and advances in artificial intelligence (AI) have fundamentally changed the demands of the workplace, creating a gap between graduate competencies and industry needs. This study aims to evaluate the effectiveness of AI Agent-based career adaptability psychoeducation as a community empowerment strategy to improve the work readiness of high school/vocational school and university graduates. The study design used a descriptive-interventional approach with 27 participants who participated in a four-week online training. Data were collected through a pre-post survey using the Career Adapt-Abilities Scale (CAAS) and qualitative observations during the training. The results of the Wilcoxon Signed-Rank test showed a significant increase in career adaptability scores (Z = –4.543, p < .001), with all participants experiencing increased career adaptability. Observations showed that participants became more confident, reflective, and proactive in designing their career directions after interacting with the AI ​​Agent. These findings indicate that psychoeducational interventions integrated with intelligent technology can strengthen the adaptive capacity and work readiness of the younger generation. Theoretically, this study expands the application of the career adaptability concept in the context of AI-based learning; In practice, the results provide a relevant community empowerment model for educational and employment institutions in the era of digital disruption.

The development of modern education demands that teachers play a broader role beyond merely delivering subject matter, including building professional reputation, credibility, and positive influence on students, colleagues, parents, and society. For Christian Religious Education teachers, personal branding has a deeper dimension because they also serve as role models and ambassadors of Christian values. This study aims to describe the formation of personal branding among Christian Religious Education teachers based on spiritual and theological values, as well as its impact on teaching and student character development. The method used is descriptive qualitative through a literature study of books, journals, notes, and relevant reports, with narrative analysis to illustrate the practices and values that shape teachers’ personal branding. The findings indicate that the personal branding of Christian Religious Education teachers is an integration of professional competence, personal integrity, and Christian values, encompassing authenticity, consistency, professionalism, spiritual and moral role modeling, inspirational and communicative abilities, adaptation to technology, and positive relationships with students and the community. Spiritual and theological values such as Christian love, integrity, humility, patience, justice, service, and commitment to God’s Word form the main foundation that strengthens teachers’ credibility, motivates students, and establishes them as living examples of Christian life. Personal branding for Christian Religious Education teachers is not merely an image or popularity, but a real reflection of faith, character, and daily behavior consistent with Christian teachings, thus supporting effective learning, student character formation, and the teacher’s reputation as an authentic, inspiring, and professional educator.  

This study investigates the implementation of the PAIKEM Gembrot model to enhance the literacy, numeracy, and creativity of fifth-grade students at UPT SDN Unjuruiya No. 45 in the Selayar Islands. Using a two-cycle classroom action research design—comprising planning, action, observation, and reflection—data were collected through tests, observations, and documentation to obtain comprehensive information on both learning processes and outcomes. In Cycle I, only 40% of students met the mastery criteria, largely due to their initial adjustment to the new learning approach and limited engagement in collaborative activities. After refining instructional strategies in Cycle II, including the use of more varied learning activities, concrete and contextual learning media, structured group discussions, and closer teacher guidance, student learning outcomes improved dramatically, with 100% of students achieving mastery. In addition, students showed increased participation, confidence, and motivation during classroom interactions. These results demonstrate that PAIKEM Gembrot effectively strengthens students’ abilities to comprehend information, apply numerical reasoning, and express creativity through active and meaningful learning experiences. Therefore, the PAIKEM Gembrot model serves as a promising and contextually relevant alternative for improving elementary science learning, particularly in island-based schools with limited educational resources.

In the learning process, educators often face challenges in helping students achieve optimal learning outcomes. One of the contributing factors is the dominance of teacher-centered learning models, which frequently leads to low student engagement and reduced motivation. This condition affects students’ curiosity, particularly in Science learning, which requires active participation and exploration. Therefore, innovative teaching materials are needed to encourage student involvement while simultaneously stimulating their curiosity. One alternative that can be utilized is the Student Worksheet (Lembar Kerja Peserta Didik/LKPD), which can be designed systematically and contextually to support active learning. This study aims to determine the effectiveness of using LKPD in enhancing students’ curiosity in Grade VII Science learning at SMPN 1 Sanrobone. The research employed a Classroom Action Research (CAR) approach carried out in two cycles, each consisting of planning, implementation, observation, and reflection stages. The findings indicate a significant increase in students’ curiosity after the implementation of LKPD, as shown by improvements in questioning behavior, interest in the material, and the ability to observe and explore scientific phenomena. Based on these results, it can be concluded that the use of LKPD is effective in fostering students’ curiosity and contributes to a more interactive and meaningful Science learning experience.

Demam berdarah dengue merupakan tantangan kesehatan masyarakat yang terus berulang di wilayah tropis, termasuk Indonesia. Penelitian ini bertujuan untuk memprediksi jumlah kasus tahunan dengan memanfaatkan lima algoritma pembelajaran mesin, yaitu Regresi Linier, Decision Tree, Random Forest, Support Vector Machine (SVM), dan Neural Network. Data historis tahun 2017–2024 diolah menggunakan teknik windowing deret waktu untuk menghasilkan fitur lag yang sesuai bagi pembelajaran terawasi. Evaluasi kinerja dilakukan melalui metrik Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), serta koefisien determinasi (R²). Model Decision Tree menunjukkan performa paling unggul pada sebagian besar indikator. Prediksi untuk tahun 2025 mengindikasikan adanya peningkatan moderat jumlah kasus. Namun, rendahnya nilai R² pada seluruh model mengisyaratkan perlunya pendekatan multivariat yang lebih kompleks dengan mempertimbangkan faktor iklim, lingkungan, dan demografi. Hasil penelitian ini menegaskan pentingnya kualitas data dan pemilihan fitur yang tepat dalam peramalan epidemiologis guna mendukung perencanaan kesehatan yang lebih efektif.

This study aims to evaluate the effectiveness of the Artificial Intelligence-Based Learning Media Development Program (P3MP-AI) in enhancing teachers’ technological and pedagogical competencies at SMK Muhammadiyah 2 Malang. The program employed a descriptive approach using both quantitative and qualitative methods, including pre-test and post-test assessments, as well as direct observation of the training process. A total of 30 teachers from various disciplines actively participated in the program conducted on August 12, 2025. The evaluation results revealed an increase in the participants’ average scores from 100 to 130 out of a maximum of 150, indicating a significant improvement in their understanding of AI concepts and applications in education. Beyond competency enhancement, the training also fostered teachers’ confidence, creativity, and ability to integrate AI-based tools into interactive learning media. However, several challenges were identified, such as limited technological resources and time constraints in classroom implementation. Overall, this program has made a tangible contribution to strengthening teachers’ digital literacy and can serve as a replicable professional development model for other vocational schools seeking to advance AI-based educational transformation.

Credit card fraud is a critical issue in digital financial transactions. This study aims to develop and evaluate fraud detection models using Logistic Regression and Gradient Boosting on an imbalanced dataset, where fraudulent transactions constitute only a small portion of the data. To address this imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. Logistic Regression, used as a baseline model, achieved 95% accuracy, 78.6% precision, 55.9% recall, and a 65.3% F1-score. After applying class weighting and SMOTE, recall improved to 88.7%, but precision dropped to 52%, indicating that the model became overly sensitive and prone to false positives. Gradient Boosting initially produced better results, with 98% accuracy, 95.5% precision, 84.3% recall, and an 89.5% F1-score. After hyperparameter tuning and resampling, its performance improved further to 96.7% precision, 86.1% recall, and a 91.1% F1-score. These results indicate that Gradient Boosting is more effective in handling imbalanced data and offers greater reliability in detecting fraudulent transactions. The findings support the growing evidence in favor of ensemble learning techniques in fraud detection applications. This research contributes practical insights into improving the accuracy and security of machine learning-based fraud detection systems in financial services.

Behind the success of the Free Nutritious Meal Program (MBG), there are several problems related to the health factors of the program targets, namely, there are several cases of allergies that occur in schools, inadequate understanding of allergen management owned by food processing vendors, and the high cost of laboratory tests and the process that takes a long time. So, to overcome these problems, an application is proposed that can help detect allergens in food products using data mining and machine learning approaches. SVM and AdaBoost algorithms each have advantages that can be used to help build an optimal allergen detection model. This research uses a cross-validation model validation method with a value of K = 10 to help improve the performance of the model built. In this study, from the entire fold, an average accuracy value of 98.74% was obtained. To evaluate the model built, this research has also conducted several new data inputs, and in each new data input, the accuracy value is obtained above 99%. This indicates that the model built, namely the combination of SVM and AdaBoost algorithms with the cross-validation model validation method, produces high accuracy, so this model can greatly assist the allergen detection process in food products.

Infectious diseases remain a dynamic and evolving public health threat, requiring data-driven approaches for early detection and targeted policy planning. This study aims to model spatio-temporal trends and clustering patterns of HIV transmission in Bogor Regency during the period 2020–2023 by utilizing a combination of unsupervised and supervised machine learning techniques. The dataset was obtained from the Bogor Regency Health Office and includes annual data on the number of HIV cases across 40 sub-districts. The research methodology consists of data preprocessing stages, clustering using the K-Means algorithm, and classification using a Decision Tree model. The preprocessing steps include data integration, attribute selection, temporal aggregation, handling of missing data, and normalization using Z-score. K-Means clustering is applied to identify hidden patterns in the development of HIV cases, resulting in three distinct clusters based on multi-year trends. The resulting cluster labels are then used as target classes in the supervised classification process. The Decision Tree classification model demonstrates high accuracy in predicting cluster membership, indicating a strong relationship between the temporal patterns of HIV cases and cluster identity. The integration of clustering and classification techniques provides a robust analytical framework for understanding the dynamics of HIV transmission, while also supporting the formulation of more precise, evidence-based, and region-specific public health interventions.

This study aims to analyze the factors that influence students' interest in continuing their education to college using a machine learning approach. Data was collected through an online questionnaire completed by 727 students between July 27 and August 22, 2025, covering 23 variables consisting of respondent identity (gender, grade level, major) as well as internal and external factors such as parental support, learning motivation, and preferred type of college. The data preparation stage was carried out through column cleaning, deletion of empty data, encoding of categorical variables, and division of the dataset into 80% training data and 20% test data. The Naive Bayes algorithm of the CategoricalNB type was used because it was suitable for the categorical nature of the data. The evaluation results showed that the model was able to predict student interest with 96% accuracy. For the class of students interested in continuing their studies, the precision, recall, and F1-score values were above 0.95, while the performance in the class of students who were not interested was slightly lower due to the smaller amount of data. These findings show that Naive Bayes is proven to be effective and reliable in classifying students' interest in continuing their studies and can be the basis for decision-making in designing more targeted educational strategies.

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

Communication is a fundamental aspect of human life. However, individuals with hearing and speech impairments often face barriers in communicating with the general public. The Indonesian Sign System (SIBI) serves as a communication solution for the deaf and speech-impaired community in Indonesia, yet public understanding of SIBI remains limited. To address this issue, this study aims to develop an automatic translation model from SIBI sign language into Indonesian text by utilizing Deep Learning technology, specifically the Convolutional Neural Network (CNN) algorithm. CNN was chosen for its ability to effectively recognize visual patterns, making it suitable for processing hand gesture images in sign language. This research involved collecting and classifying a dataset of hand images based on the alphabet or words in SIBI, which were then used to train the CNN model. The designed CNN model was built to accurately classify hand signs and translate them into Indonesian text. The results of this study have the potential to serve as a supportive solution for inclusive communication between the deaf community and the wider public, and can be further developed for contextual sentence translation. Keywords: Indonesian Sign System (SIBI), CNN, Deep Learning, Automatic Translation, Inclusive Communication

This study aims to investigate the factors influencing the continuance intention of Al-Kautsar Senior High School students in using metaverse-based learning media. The background of this research lies in the rapid adoption of immersive technologies in education, while students’ levels of acceptance have not yet been fully understood. The objective is to identify the antecedents of satisfaction, which subsequently influence continuous intention. The research model examines the effects of perceived interactivity, perceived sociability, perceived enjoyment, perceived ease of use, perceived security, and social influence on satisfaction. A quantitative approach was employed by distributing questionnaires to students, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that satisfaction is a very strong and statistically significant predictor of continuous intention to use metaverse applications (β = 0.716, p < 0.001). The six hypothesized antecedent variables were not found to have a significant individual effect on satisfaction. In conclusion, for digital native students at Al-Kautsar Senior High School, factors such as ease of use, interactivity, and enjoyment have shifted from being drivers of satisfaction to becoming basic expectations (hygiene factors). Satisfaction itself emerges as the primary determinant, likely influenced by more substantive elements such as content quality or pedagogical design rather than merely the technical features of the platform.

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms