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This study explores the practice of participatory communication and creative production implemented by the Anggrek Waste Bank along the Ciliwung River as an educational strategy for flood disaster mitigation. The research aims to understand how community-based waste management initiatives can serve as platforms for environmental learning and social empowerment. Using a qualitative approach with a case study method, this study investigates social dynamics, interaction patterns, and meanings constructed by residents through collaborative waste management and creative production activities. Data were collected through observation, in-depth interviews, and documentation. The findings reveal that participatory communication is manifested not only in formal education, counseling, or campaigns, but also in everyday interactions, creative workshops, and collective community work. Creative products such as recycled crafts, soap made from used cooking oil, and eco enzymes function as symbolic communication media that persuasively convey ecological messages while providing tangible economic benefits. These practices foster a sense of ecological responsibility, strengthen social solidarity, and enhance community resilience to flooding. This study concludes that participatory communication integrated with creative production serves as an effective model for promoting sustainable behavior and community-based flood mitigation in urban environments.

Kreo Village, located in Larangan District, Tangerang Regency, still relies heavily on very traditional communication methods in daily life. While this approach reflects local wisdom and strong cultural values, the changing times demand a digital transformation, especially in the field of communication. With the rapid advancement of digital technology and the growing quality of human resources in the village, the need for a more modern communication system is becoming increasingly important. One potential solution is the use of artificial intelligence technology such as ChatGPT. ChatGPT (Generative Pre-trained Transformer) is a language model based on AI developed by OpenAI. This technology can understand and generate natural language interactively, similar to human conversation. By integrating ChatGPT into community communication activities, Kreo Village can speed up access to information, facilitate the exchange of ideas and opinions, and bridge the existing digital gap. In addition, ChatGPT can also be used as an educational tool to help residents understand digital technology, support learning activities, and strengthen community participation in village development. Through this initiative, Kreo Village can move toward becoming a more inclusive, adaptive, and competitive digital village in the modern era.

Millions of people suffer from malaria, one of the most serious parasitic diseases that threatens human life and causes high rates of morbidity and mortality, particularly in tropical and subtropical regions. Traditional diagnostic methods, such as blood smear examination, which can be performed using a microscope, face many challenges due to the inaccuracy of manual analysis and the reliance on individual skills. Therefore, the use of machine learning or deep learning algorithms to automate malaria detection offers promising solutions to improve accuracy, reduce diagnosis time, and enhance scalability. In this paper, a multi-class convolutional neural network (CNN)-based model is designed to classify cells infected with Plasmodium falciparum (P. falciparum) and Plasmodium vivax (P. vivax) and uninfected cells from blood smears, as most severe cases and deaths are caused by P. falciparum and P. vivax. This is achieved by building and training a CNN from scratch, rather than using transfer learning from pre-trained models. The proposed network was trained and tested on the Kaggle dataset, which consists of 27,558 images of infected and uninfected individuals. These images were divided into 13,779 images of uninfected individuals, 6,890 images of individuals with P. falciparum malaria, and 6,889 images of individuals with P. vivax malaria. The images were preprocessed using several operations, including blurring, denoising, and morphological processing. The proposed model achieved the best evaluation accuracy when compared with other deep learning algorithms, with an accuracy rate of 96.5%, a sensitivity rate of 95%, a specificity rate of 97.6%, and an F1-score rate of 96.5%. These results demonstrate the effectiveness of the proposed model as a tool to assist clinicians in malaria diagnosis, reducing reliance on manual analysis.

Digital transformation in higher education demands the sustainable use of Learning Management Systems (LMS). However, most previous studies have predominantly focused on cognitive aspects such as perceived usefulness and user satisfaction, overlooking the motivational dimensions that drive long-term engagement. This study aims to explore how System-Provided Autonomy Support (S-PAS)—the degree of autonomy support embedded within the system—shapes users’ continuance intention to use LMS through psychological need satisfaction and autonomous motivation. Employing an exploratory qualitative approach, data were collected through in-depth interviews, focus group discussions (FGDs), task-based observations, and critical discourse analysis of institutional policies and system micro-copies across several universities under the coordination of LLDIKTI Region IX Sultanbatara. Thematic analysis revealed that users’ experiences with LMS are influenced by five key dimensions of S-PAS: meaningful choice, rationale provision, non-controlling language, emotional validation, and informational feedback. When the system and policies offer flexibility, empathetic communication, and clear rationales behind instructions, users exhibit higher levels of autonomous motivation and stronger commitment to LMS continuance. Conversely, administrative rigidity, technostress, and opaque surveillance practices undermine psychological need satisfaction and weaken users’ continuance intention. The study extends the application of Self-Determination Theory (SDT) to educational technology by introducing S-PAS as a conceptual framework that bridges system design and user motivation. Practically, the findings offer strategic guidance for policymakers and developers to design LMS environments that are more autonomy-supportive, human-centered, and sustainable. Future research is encouraged to quantitatively validate the S-PAS model across diverse institutional and cultural contexts to enhance the generalizability and theoretical robustness of these findings.

This research aims to enhance students' English technical competence by implementing Problem-Based Learning (PBL) for the English for Mechanical Engineering course at Politeknik Negeri Fakfak. However, the setting of this research is rooted in students' difficulties in understanding technical terms and applying English in engineering work environments. Implementing a participatory action research model with descriptive qualitative approaches, the research was conducted among 50 students of two classes. Data were collected through questionnaires, observations, and interviews. The findings showed that 88% of students perceived the learning material as highly relevant, 84% reported improved mastery over technical vocabulary, and 90% reported greater collaboration and problem-solving skills. Furthermore, the intervention fostered active learning behaviors, peer leadership, and a positive shift towards more favorable attitudes among students towards English as a professional tool. These findings confirm that PBL is a valuable approach to English language learning contextualization in the vocational education context, enhancing the development of both language and soft skills. The research proposes wider implementation of PBL across technical curricula to improve educational outcomes in correspondence with industry requirements.

This study aims to examine the process and improvement of reading comprehension skills among slow learner students in Grade VII at SMP Negeri 2 Karangan through the use of interactive animation video media based on the Project Based Learning (PjBL) model. Employing the Classroom Action Research (CAR) method following the Kemmis and McTaggart model, the study was conducted in two cycles with six slow learner students as research subjects. The observation results indicated an improvement from 89.58% in Cycle I to 100% in Cycle II. The average reading comprehension score increased from 65 to 83, with the percentage of students achieving the minimum mastery criteria rising from 67.85% to 89.28% with a difference of 18 points increase. These findings demonstrate that interactive animation video media based on PjBL effectively enhances both student engagement and learning outcomes, particularly in understanding English texts. This success was supported by the active role of the teacher in adapting the PjBL syntax to meet the specific needs of the slow learner students.

Digital transformation has opened vast opportunities for the younger generation to engage in technology-based entrepreneurship. However, the low level of literacy regarding Sharia-based business among university students remains a major challenge in building a competitive halal startup ecosystem. This community engagement program aims to equip students with both conceptual understanding and practical skills in developing digital businesses aligned with Sharia principles. The program was implemented on May 10, 2025, involving 60 students from UNISKA, UMB, and Poliban. The method employed an educational-dialogical approach based on experiential learning, encompassing interactive seminars, technical workshops, and reflective evaluations. The training materials included the Sharia Business Model Canvas, halal design thinking, Islamic ethical digital marketing, as well as halal legality and certification. The program’s effectiveness was measured through pretest and posttest instruments, which showed a significant improvement in the average score from 58.3 to 82.7. These findings indicate that the program was effective in enhancing students’ understanding and readiness to become halalpreneurs. Furthermore, the experience fostered a transformation in students’ mindsets toward perceiving business as a means of worship and social contribution. The program is recommended to be replicated on a larger scale with cross-sectoral collaboration and support.

This study aims to describe students’ mathematical critical thinking skills in terms of their learning styles based on the VARK model (Visual, Auditory, Reading/Writing, and Kinesthetic). The research employed a descriptive qualitative approach involving four seventh-grade students of SMP Negeri 9 Gresik, each representing one type of learning style. Data were collected through a learning style questionnaire, a mathematical critical thinking test based on Facione’s indicators, and in-depth interviews to strengthen the analysis. The results revealed that each learning style demonstrates distinct characteristics of critical thinking. Visual learners excelled in interpretation and self-regulation, auditory learners performed well in evaluation and verbal explanation, reading/writing learners showed strength in analysis and written reasoning, while kinesthetic learners were better in concrete activities but weaker in evaluation and inference. Overall, interpretation and analysis indicators were more dominant than evaluation and inference. These findings highlight the importance of implementing differentiated instruction aligned with students’ learning styles to foster optimal and comprehensive development of mathematical critical thinking skills.

This study aims to determine the contribution of the variables of Self-Motivation, Learning Discipline, and Parental Support both partially and simultaneously to student learning achievement in the Automotive Engineering expertise program at SMK Negeri 2 Pengasih. This type of research is included in quantitative correlational research with multiple regression analysis techniques. The entire population was sampled with a total of 104 grade XII students. Data collection used a closed questionnaire and student final exam results documents. The research instrument that has passed the validity and reliability test, then continued with the classical assumption test to ensure that the regression model meets the eligibility requirements. The results showed that the three independent variables contributed significantly to student automotive learning achievement with a value of F = 26.718. Simultaneously with a significance value of 0.000 (p <0.001) and an Adjusted R Square value of 0.428 which means the model is able to explain 42.8% of the variation in learning achievement. Partially, the Self-Motivation variable significantly (p<0.05) provides the largest contribution to student learning achievement with a t-value of 2.120 followed by Learning Discipline with a t-value of 1.992 and Parental Support with a t-value of 2.021. The magnitude of the real contribution of each variable, namely: Self-Motivation of 12.15%, Learning Discipline of 10.49%, and Parental Support of 10.24%. These results emphasize the importance of strengthening students' intrinsic motivation, habituating learning discipline, and active parental involvement to improve learning outcomes in vocational education.

Graphic design training is an important strategy to enhance student learning competencies, particularly for students participating in the Internship Program (PKL) at Rideatama. This study aims to provide foundational understanding and advanced skills in using the Canva application as an effective learning medium. The training was attended by PKL students from SMKN 1 Kopang with an interactive learning method and hands-on practice. The evaluation results showed significant improvement in creativity, the ability to utilize design features, and students' confidence in developing independent works. The majority of participants stated that the training material was relevant to their needs, and the instructor's delivery was easy to understand, as reflected in the average evaluation score of 86.67%. Although there were some suggestions regarding the training duration being too short, overall, the activity successfully enhanced student learning competencies. This training is expected to serve as a competency development model that is beneficial for both the education and professional sectors, providing skills that can be directly applied in the field of graphic design.

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder that presents significant diagnostic challenges due to its heterogeneous clinical manifestations and symptom overlap with other neurological conditions. Early and accurate diagnosis is critical for initiating timely interventions and improving patient outcomes. Traditional diagnostic approaches rely heavily on clinical expertise and manual interpretation of neuroimaging data, such as structural MRI, Diffusion Tensor Imaging (DTI), and functional MRI (fMRI), which are inherently time-consuming and prone to interobserver variability. Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) have demonstrated potential for automating neuroimaging analysis, yet existing models often suffer from limited generalizability across modalities and datasets. To address these limitations, we propose a Transformer-augmented deep learning ensemble framework for automated ALS diagnosis using multi-modal neuroimaging data. The proposed architecture integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Vision Transformers (ViTs) to leverage the complementary strengths of spatial, temporal, and global contextual feature representations. An adaptive weighting-based fusion mechanism dynamically integrates modality-specific outputs, enhancing the robustness and reliability of the final diagnosis. Comprehensive preprocessing steps, including intensity normalization, motion correction, and modality-specific data augmentation, are employed to ensure cross-modality consistency. Evaluation using 5-fold cross-validation on a curated multi-modal ALS neuroimaging dataset demon-strates the superior performance of the proposed model, achieving a mean classification accuracy of 94.5% ± 0.7%, precision of 93.9% ± 0.8%, recall of 92.9% ± 0.9%, F1-score of 93.4% ± 0.7%, spec-ificity of 97.4% ± 0.6%, and AUC-ROC of 0.968 ± 0.004. These results significantly outperform baseline CNN models and highlight the potential of transformer-augmented ensembles in complex neurodiagnostic applications. This framework offers a promising tool for clinicians, supporting early and precise ALS detection and enabling more personalized and effective patient management strategies.

English literacy at early childhood is a crucial foundation for language development, cognitive growth, and academic readiness. TK Desa Bagi 01, despite its high academic achievements, faces several challenges in teaching English, including a busy daily schedule, limited learning time, and restricted access to interactive learning media. This community service program was designed to address these challenges by introducing English literacy to young learners in a creative and engaging manner while simultaneously enhancing teachers’ skills in employing interactive and contextually relevant teaching media. The program employed several methods: observation of current teaching practices, planning and development of creative learning media such as flashcards, posters, storybooks, and puppets, implementation of learning activities through play-based approaches, and evaluation of student and teacher outcomes. The results indicate a significant increase in student enthusiasm, active participation, and comprehension of basic English vocabulary. Teachers also demonstrated improved creativity and confidence in using various teaching media to support literacy learning. This program shows that English literacy can be effectively fostered in early childhood using simple, locally relevant, and sustainable media. Furthermore, it highlights the importance of teacher development in enhancing the quality of English learning in rural school contexts. Overall, the initiative provides a practical model for integrating interactive English literacy education in early childhood settings with long-term potential for rural education improvement.

This community service activity aims to increase educational technology literacy among Vocational High School (SMK) teachers by introducing the basic concept of Deep Learning and its application in the world of education. This training was motivated by the urgent need to equip educators with basic understanding and skills regarding artificial intelligence (AI), especially Deep Learning, to be able to keep up with rapid technological developments and relevant to vocational education. Through practice-based training using simple tools such as Teachable Machine, teachers are given hands-on experience in creating simple machine learning models that are applicable in the school environment. The results of the pre-test and post-test showed a significant increase in understanding in the participants. The group discussions also produced various ideas for the application of AI such as a face-based attendance system, student voice recognition, and recommendations for learning materials according to students' interests. Despite challenges such as limited infrastructure and teacher learning time, this activity proves that a practical and contextual training approach can increase teachers' motivation to integrate technology into learning. Similar training is highly recommended to be replicated in other schools to support equitable access to modern educational technology.

This study examines the application of the Project-Based Learning (PjBL) model in Natural and Social Sciences education to enhance environmental awareness among fifth-grade students at SDIT Wihdatul Ummah in Makassar City. The background of this research stems from the low environmental awareness observed among students, evidenced by behaviors such as littering, lack of concern for energy conservation, and minimal participation in school cleanliness activities. The study aimed to improve students' environmental consciousness by implementing the PjBL model, where students were tasked with creating environmental awareness posters. The research used a Classroom Action Research (CAR) method, which was carried out in two cycles, each consisting of three meetings. The stages of PjBL involved determining essential questions, project planning, scheduling, project implementation, result testing, and evaluation of experiences. Data collection was done through observations, attitude questionnaires, and documentation of student activities. The findings showed that the PjBL model successfully increased students' active participation, creativity in expressing environmental awareness through posters, and their practical actions toward environmental protection. The environmental awareness indicators, such as maintaining cleanliness, managing waste, conserving energy, understanding waste disposal systems, and using cleaning tools, significantly improved from Cycle I to Cycle II. The results indicate that the PjBL model is effective in enhancing environmental awareness while making the learning process more engaging and contextual for elementary school students.

This study examines the revitalization of the Qur’anic Education Center (TPA) in Mekar Jaya Village, which had ceased operating due to the loss of its main teacher and limited volunteer support. Using a Participatory Action Research (PAR) approach, the program engaged community members, volunteers, religious leaders, and parents in collaborative efforts to restore regular Qur’an learning activities and strengthen the TPA’s function as a vital center of Islamic education. The intervention led to increased student participation, including the return of former learners and the enrollment of new participants, as well as improved learning motivation through active engagement and parental support. This collaborative revitalization not only revived essential Islamic learning for children but also fostered sustained community involvement, highlighting the critical role of volunteers and local leadership in sustaining nonformal religious education. The model developed through this initiative provides practical implications for other rural communities seeking to maintain and strengthen Islamic educational institutions.

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.

Conflict is an inevitable phenomenon in organizational dynamics, whether in the education sector, social sector, or wider society. Conflicts that are not managed properly can lead to disintegration, but if handled appropriately, they can become a means of learning and innovation. This literature review aims to identify sources of conflict, resolution strategies used, and their impact on organizational performance. The research questions asked are: (1) What are the main factors causing conflict in educational and social organizations? (2) What are the most effective conflict resolution strategies according to the context? This research method uses a literature review approach by searching articles through Google Scholar and Publish or Perish (PoP) using the keywords “sources AND levels of conflict.” A total of 20 articles were selected from the period 2021–2025, with a final result of five valid articles that were analyzed thematically. The review results show three main themes: (1) sources of conflict include differences in vision, interests, and resource distribution; (2) conflict resolution strategies vary, ranging from mediation, negotiation, to religious and cultural-based approaches; (3) the impact of conflict is ambivalent, it can decrease performance but also encourage positive change.The conclusion of this study emphasizes the importance of contextual conflict management and opens up opportunities for further research to develop more adaptive resolution models based on local values.

JakLingko is a digital-based public transportation integration system developed to facilitate access to various transportation modes in Jakarta. Along with the increasing number of users, reviews on the JakLingko application reflect user experiences and perceptions. This study aims to analyze the sentiment of user reviews on the Google Play Store using the Naïve Bayes method. Data collection was conducted through web scraping, resulting in 3,260 reviews. The data were preprocessed, sentiment-labeled, and classified using Orange Data Mining. The research applied a quantitative experimental approach with a machine learning framework. The classification results showed that neutral sentiment dominated user reviews, followed by negative and positive sentiments. The Naïve Bayes model achieved 100% accuracy based on the confusion matrix and other evaluation metrics such as precision, recall, and F1-score. The findings highlight that Naïve Bayes can be a reliable approach for analyzing public opinion and serve as a reference for evaluating and improving digital service applications.

This study aims to develop an interactive learning medium on the Gimkit platform with the topic of modal verbs at the A1 level. The research method applies a qualitative approach using the model of Richey & Klein (2017), which consists of three stages: (1) planning, (2) development, and (3) evaluation. The data source of this study is the book Grammatik aktiv A1–B1 by Friederike Jin and Ute Voß (2014), Chapter 5, pages 16–18, focusing on the topics: (1) conjugation of modal verbs in the present tense, (2) conjugation of modal verbs in the past tense, and (3) comparison of modal verbs in the present and past tenses. The learning medium was validated by a material expert with a score of 95 and by a media expert with a score of 90; both results fall into the category “very good.” Based on these results, the Gimkit learning medium on the topic of modal verbs is considered suitable and effective for teaching German at the A1 level in senior high schools, both for independent learning and for classroom instruction.

This research is motivated by the low cognitive abilities of students in science learning in elementary schools, which is largely caused by conventional teaching methods that do not actively involve students. To overcome this problem, the Team Assisted Individually (TAI) learning model is used, which combines a cooperative approach and individual learning to improve the understanding of science concepts more effectively. The purpose of this study is to determine the effect of the TAI model on the cognitive abilities of fifth-grade students of SD Islam Baitul Salam Ciledug on natural phenomena material. This study uses a quantitative approach with a quasi-experimental design and a pretest-posttest control group design technique. The sample consists of two classes, namely the experimental class that uses the TAI model and the control class that uses conventional learning methods. The results of the study show that there is a significant increase in the cognitive learning outcomes of students who participate in learning with the TAI model compared to the conventional method. Therefore, this study is expected to provide an alternative effective learning model for teachers, as well as improve the quality of the process and student learning outcomes, especially in science learning.