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Student dropout remains a significant challenge for higher education institutions as it impacts academic quality, educational management efficiency, and students' success in completing their studies. Therefore, an approach that can identify students at risk of dropping out is necessary so that timely academic interventions can be made. This study aims to develop a dropout detection model using an Artificial Neural Network (ANN). The data used come from a publicly available higher education dataset, ensuring research reproducibility. Data preprocessing steps were carried out to improve data quality before modeling, and the Synthetic Minority Over-Sampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) was applied to address class imbalance issues. The ANN model's performance was evaluated using accuracy, precision, recall, F1-score, and area under the ROC curve (ROC-AUC). The test results show that the ANN model can provide excellent predictive performance in detecting at-risk students. The application of SMOTE-ENN also proved to enhance the model’s sensitivity toward the minority class, as indicated by improvements in recall and F1-score. These findings indicate that the developed ANN model has the potential to be used as a student dropout detection system to support data-driven decision-making and strategy development within higher education institutions.

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

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 study aims to explain the application of various differentiated learning techniques based on Problem-Based Learning (PBL) to improve analytical and writing skills related to procedural texts among junior high school students. This research is based on students' lack of ability to understand and compose procedural texts methodically and in accordance with language conventions, which is caused by the prevalence of conventional teacher-centered learning. This study used a descriptive qualitative methodology involving seventh-grade students from a junior high school that has adopted the PBL model in Indonesian language subjects. Data were collected through observation, interviews, and documentation, then analyzed qualitatively. The results showed that the application of PBL along with differentiated learning and TPACK increased student engagement, accommodated diverse learning styles, and fostered critical thinking, analytical abilities, and collaborative skills. Learning became more meaningful and relevant, enabling students to compose procedural texts more effectively. This study recommends the application of the PBL model with differentiation as an innovative strategy to improve the quality of Indonesian language education in junior high schools.

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..

This study aims to explore the contribution of Christian Religious Education in developing the character of people with disabilities, grounded in Jean Vanier’s perspective on inclusive community and human dignity. People with disabilities are often marginalized due to persistent social stigma, which limits their access to education, meaningful participation, and employment opportunities, particularly within faith-based educational contexts. Employing a qualitative research approach through an in-depth literature review, this study examines key concepts in Christian Religious Education, the characteristics and lived experiences of individuals with disabilities, and the challenges and strategies associated with inclusive educational practices. The findings indicate that Christian Religious Education can function as an effective empowerment framework by integrating spiritual formation with the development of social skills, self-confidence, and communal belonging. Based on Jean Vanier’s inclusive vision, the study highlights practical implications for local churches, Christian schools, and faith-based communities, such as fostering inclusive learning environments, implementing participatory pedagogical models, and strengthening community-based support systems for people with disabilities.Furthermore, reducing social stigma through value-based education and community engagement emerges as a critical strategy to enhance educational participation and social integration. These findings contribute to the discourse on inclusive Christian education and offer contextual strategies applicable to local academic and ecclesial settings in promoting the dignity and empowerment of people with disabilities.

The Ministry of Social Affairs has made a new breakthrough in facilitating the public in checking social assistance recipients, namely the social assistance check application. User reviews can be used to find out whether the application provides benefits to the community or not. However, these reviews need to be processed using sentiment analysis. Then to do sentiment analysis requires machine learning. One method that includes machine learning is Naïve Bayes. The purpose of this research is to implement the Naïve Bayes method in conducting sentiment analysis and find out whether the social assistance check application is beneficial to society based on the results of sentiment analysis. In this study, two categories of sentiment are used, namely positive and negative. The author collects by crawling using the Google Play Scrapper library. The results of crawling data obtained as many as 4000 data. The results showed that the actual data that had been labeled using Textblob resulted in 987 negative label reviews and 628 positive label reviews. Meanwhile, the Naïve Bayes method is able to analyze the review sentiment of the social assistance check application with the results of 1181 negative sentiments and 434 positive sentiments. The Naïve Bayes model has a good accuracy rate of 0.77 or 77% in analyzing sentiment for social assistance check application reviews.

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.

Jambi Province is one of the largest natural rubber producing regions in Indonesia; however, rubber factories under GAPKINDO Jambi still face productivity issues, particularly the gap between production capacity and actual output, and productivity assessment that is still conducted manually by GAPKINDO Jambi. This study employs Decision Tree, Random Forest, KNN, and SVM algorithms within a structured pipeline involving preprocessing, feature selection, standardization, data balancing using SMOTE, and hyperparameter tuning. The proposed solution applies productivity level classification both individually and through paired combinations (ensemble voting). The results show that the Decision Tree + Random Forest model achieves the best performance with an accuracy of 0.84 and an F1-score of 0.83, confirming the effectiveness of ensemble methods in supporting productivity improvement decisions.

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.

Gross motor development is an essential aspect of early childhood education because it contributes to body coordination, balance, muscle strength, and children’s readiness for physical activities. One of the activities that can be used to stimulate gross motor skills is dancing, particularly regional creative dance. This community service program aims to implement the Janger creative dance as a medium to develop gross motor skills in kindergarten group B children. The method used was evaluative with the CIPP model (Context, Input, Process, Product), involving observation, interviews, and documentation. The activity was carried out once a week through stages of rhythm introduction, basic movements, body coordination, and simple dance sequences. The results showed that more than 80% of the children experienced improvement in aspects of balance, movement coordination, agility, and large muscle control. In addition, the activity also enhanced children's self-confidence, courage, and social interaction. Therefore, the Janger dance is proven to be effective as a gross motor stimulation and is suitable to be used as a culturally based learning strategy in early childhood education.

This research examines the implementation of the Deep Learning approach in Islamic Religious Education (PAI) learning at SMAN 61 Jakarta and its contribution to the development of students' critical thinking skills. This study was conducted due to the need for a learning model that encourages students to understand religious concepts through active, reflective, and meaningful learning experiences. The purpose of this research is to describe the implementation of Deep Learning in PAI learning and its influence on the development of students' critical thinking skills. This study employs a qualitative method with a descriptive research design, using interviews, classroom observations, and documentation as data collection techniques. The findings indicate that the Deep Learning approach supports students in participating more actively in discussions, analyzing religious issues related to real-life situations, and constructing arguments based on their own understanding. Students also demonstrate improvement in evaluating information, considering diverse perspectives, and providing more reflective responses to classroom problems. The study further identifies several challenges, including limited digital facilities, varying levels of student participation, and teachers’ readiness in managing diverse learning strategies and media. These findings suggest that Deep Learning has strong potential to enhance students’ critical thinking skills when applied systematically and designed to foster active participation and meaningful exploration during the learning process.

Globalization and modernization amid the strong digital flow have become a huge challenges and obstacles for shaping moral and ethics of the students. There are many students who come to the class physically without having readiness and awareness in the study. Teacher as an educator needs to give an efforts in every process of learning with attractive concept and model to give attention and meaning in the study. Through the mindful learning, it is considered capable of giving full attention and self-awareness while studying which in the end not only gives good quality in learning but also shapes islamic ethics in the stundents. This research aims to know the concept, urgency, and the implementation of mindful learning which is capable of shaping islamic ethics. The method of the research is a qualitative approach with a literature study from various readings such as articles and journals that are relevant to the topic. The reasearch results show that mindful learning is a learning process that emphasizes self-reflection and self-awareness so students feel ready and motivated in learning. It shows that deep learning has a huge urgency in shaping student’s Islamic ethics. The implementation of mindful learning in Islamic education Studies is carried out through the pedagogic strategies that emphasize the conditioning of awareness, active students involvement, and deep reflection of Islamic values. Thus, mindful learning is capable of increasing internal awareness through muraqabah with the involving of students in every learning process.

: 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.

This qualitative study, using a phenomenological approach, aims to uncover, study, and deeply understand students' perspectives regarding the curriculum transformation for 21st-century learning they experience in elementary schools. The phenomenological approach was used to explore students' direct experiences as the primary subjects in the learning process, thus gaining a comprehensive understanding of the meaning of curriculum change from their perspectives. Data collection was conducted through in-depth interviews with upper-grade students in elementary schools in Jambi City. The results indicate that the curriculum transformation has a significant impact on students' learning styles, particularly through the implementation of project-based learning that integrates the use of digital technology. This learning model encourages students to be more active, creative, and collaborative in completing learning tasks. Furthermore, student-centered learning makes them the primary actors in the learning process, while teachers act as facilitators. This curriculum transformation also helps develop 21st-century skills, such as critical thinking, communication, collaboration, and digital literacy in elementary school students.

This study examines how integrating local ecological wisdom and eco-literacy education fosters environmental awareness, behavioral transformation, and health outcomes among Indonesian primary school students. The research responds to the ecological paradox of rapid technological growth amid worsening environmental degradation, where youth eco-literacy remains below 45%, indicating a gap between environmental knowledge and sustainable action. The study aims to develop a culturally responsive model of sustainability education that connects environmental ethics, cultural identity, and public health. Using a qualitative case study design, the research was conducted at SD Islam Kreatif Mutiara Anak Sholeh, Sidoarjo, East Java, from July to August 2025, involving 60 students and six teachers. Data were collected through semi-structured interviews, observations, and document analysis. Four major themes emerged: (1) cultural narratives as catalysts for environmental awareness, (2) eco-literacy as experiential and behavioral transformation, (3) collaborative learning as collective environmental agency, and (4) eco-health as psychosocial and physical well-being. Findings show that integrating Majapahit-era ecological values and local storytelling into eco-brick and composting projects enhanced students’ responsibility, cooperation, and emotional balance. The study synthesizes Eco-pedagogy, Constructivism, and Eco-health frameworks into a Culturally Responsive Eco-Health Pedagogy, demonstrating that sustainability learning rooted in culture and participation promotes both environmental and health outcomes. This model contributes to the global Education for Sustainable Development (ESD) 2030 agenda by linking culture, ecology, and well-being in primary education.

The dynamics of student organization structure have significant implications for talent retention and student career development. This study aims to comparatively analyze the influence of centralization and decentralization structures in the Information Technology Study Program Student Association on the effectiveness of talent retention strategies and career development capacity. The research method uses a quantitative approach with a comparative design involving 120 respondents selected through purposive sampling technique with criteria of at least one period of organizational experience. Data collection instruments use structured Likert scale questionnaires that have been tested for validity and reliability with Cronbach's Alpha values above 0.80. Data analysis uses Structural Equation Modeling and independent sample t-test to compare both structural models. The results show that the decentralization structure has a strong significant effect on talent retention with a path coefficient of 0.628 compared to centralization of 0.312. Comparative analysis identifies substantial differentiation in all dimensions of career development with the decentralization structure consistently outperforming centralization, especially in the aspect of decision-making autonomy. The findings confirm that the distribution of authority in decentralization creates a learning ecosystem that facilitates diversification of leadership experiences and strengthens students' organizational commitment through participatory empowerment mechanisms.

Cross-Cultural Experience: Introducing Balinese Traditional Clothes to Walailak University Students in Thailand is part of an international community service program (KKN) that students from the Institute of Technology and Education Markandeya Bali took part in. The main goal of this activity was to introduce Balinese cultural values by teaching students about traditional Balinese clothes as a way to learn about different cultures. This study uses a qualitative descriptive approach with the CIPP (Context, Input, Process, Product) evaluation model to evaluate the success of the activity. The results of this activity show that there is an increase in understanding, fostering an attitude of appreciation, and strengthening cross-cultural communication among students. This activity is effective as a medium for cultural diplomacy and experiential learning.

Cross-Cultural Experience: Introducing Balinese Traditional Clothes to Walailak University Students in Thailand is part of an international community service program (KKN) that students from the Institute of Technology and Education Markandeya Bali took part in. The main goal of this activity was to introduce Balinese cultural values by teaching students about traditional Balinese clothes as a way to learn about different cultures. This study uses a qualitative descriptive approach with the CIPP (Context, Input, Process, Product) evaluation model to evaluate the success of the activity. The results of this activity show that there is an increase in understanding, fostering an attitude of appreciation, and strengthening cross-cultural communication among students. This activity is effective as a medium for cultural diplomacy and experiential learning.

The mathematics learning outcomes of students at SMP Negeri 3 Bontang on Rational Numbers are still relatively low. Data from the 2019 National Examination (UN) shows an average math score of 46.43, which is in the "poor" category. Summative assessment results indicate that most students have not yet achieved the Learning Objective Achievement Criteria (KKTP). This situation indicates that the learning process tends to be conventional and lacks active student engagement. Therefore, a more innovative learning model is needed, one of which is the Team Games Tournament (TGT), which combines group work, competition, and educational games. This study aims to determine the effect of the TGT learning model on the mathematics learning outcomes of seventh-grade students at SMP Negeri 3 Bontang in the topic of Rational Numbers. This study used a quantitative approach with a quasi-experimental type and a Posttest-Only Control Group Design. The study population was 203 seventh-grade students in the 2024/2025 academic year, with a sample consisting of class VII A as the experimental group (33 students) and class VII F as the control group (34 students), selected through a purposive sampling technique. The research instrument was a five-item essay test. The analysis results showed that the average posttest of the experimental group was 67.848, higher than the control group at 61.794. The Independent Sample t-Test produced a significance value of 0.031 <0.05, so H₀ was rejected. This indicates that the Team Games Tournament (TGT) learning model has a significant effect on improving students' mathematics learning outcomes in the Rational Numbers material.