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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.

The rapid development of information technology encourages the use of digital media as an educational tool in the health sector, particularly for pregnant women. One of the problems faced by Klinik Bidan Rima Pondok Meja is the limited use of conventional educational media, such as books and posters, which are considered less attractive and difficult to understand. This study aims to design and develop an Android-based Augmented Reality (AR) application as an educational medium for nutrition and fetal development for pregnant women. The application presents three-dimensional (3D) visualizations of fetal development from week to week, along with information on nutritional needs during pregnancy. The system development method used in this research is the Prototype model, while the Augmented Reality technology applies marker-based tracking. The development tools used include Unity, and Blender 3D. The result of this study is an Android-based AR application prototype that provides interactive and easily understandable information about fetal development and maternal nutrition. This application is expected to increase learning interest and understanding of pregnant women in maintaining a healthy pregnancy at Klinik Bidan Rima Pondok Meja.

The rapid development of artificial intelligence, particularly ChatGPT, has created new opportunities to support students’ academic activities in higher education. However, its utilization needs to be evaluated in terms of the alignment between academic task characteristics and technological capabilities to ensure optimal outcomes. This study aims to examine the feasibility of using ChatGPT in students’ academic activities by applying the Task–Technology Fit (TTF) model. This research employed a quantitative approach using Structural Equation Modeling based on Partial Least Squares (SEM-PLS). Data were collected through questionnaires distributed to university students and analyzed using SmartPLS 4 software. The variables examined included Task Characteristics, Technology Characteristics, Task–Technology Fit, Performance Impact, and Utilization. The results indicate that Task Characteristics and Technology Characteristics have a positive and significant effect on Task–Technology Fit. Furthermore, Task–Technology Fit significantly influences Performance Impact and Utilization. Performance Impact also shows a positive and significant effect on the utilization of ChatGPT by students. These findings suggest that the alignment between academic task requirements and the capabilities of ChatGPT plays a crucial role in improving students’ performance and encouraging sustained technology use. The implications of this study highlight the importance of selective and purposeful use of ChatGPT in higher education and provide a reference for higher education institutions in formulating policies related to the ethical and effective integration of artificial intelligence technologies as learning support tools.

This article develops a conceptual model that explains how social capital and digital literacy interact in shaping Islamic financial literacy in the digital era. Through a comprehensive literature review, this study synthesizes theories, empirical findings, and thematic patterns derived from reputable academic journals, scholarly books, and institutional publications. The analysis shows that social capital functions as a value foundation encompassing trust, collective norms, and behavioral orientations that influence individuals’ initial acceptance of sharia-based financial practices. Information obtained through family, religious communities, and social networks becomes a crucial entry point that shapes early perceptions and preferences toward Islamic financial products. Meanwhile, digital literacy strengthens individuals’ ability to access, evaluate, and verify Islamic financial information independently through various digital content such as online articles, infographics, educational videos, and Islamic fintech platforms. The interaction between these two dimensions creates a layered learning process in which social capital provides contextual value and trust, while digital literacy deepens technical understanding in a more objective manner. This article contributes theoretically by proposing the Social Capital–Digital Literacy Integrative Model and offers practical implications for Islamic financial institutions, regulators, and fintech providers in designing more effective strategies to enhance Islamic financial literacy in society.

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.

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 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.

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.

The rapid growth of e-commerce platforms in Indonesia, particularly Tokopedia, has resulted in a large volume of consumer reviews containing valuable information regarding customer perceptions and satisfaction. However, manual analysis of such reviews is inefficient and prone to subjectivity, necessitating an automated approach based on machine learning. This study aims to classify the sentiment of sports product reviews on Tokopedia into positive, negative, and neutral categories by applying Logistic Regression, Support Vector Machine (SVM), and Random Forest using the Term Frequency–Inverse Document Frequency (TF-IDF) approach. The data were collected through web scraping of Indonesian-language sports product reviews and processed through several preprocessing stages, including data cleaning, case folding, tokenization, stopword removal, and stemming. Feature representation was performed using TF-IDF to transform textual data into numerical vectors, after which the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the application of TF-IDF significantly improves the performance of all models, with SVM consistently achieving the most optimal performance compared to Logistic Regression and Random Forest. These findings demonstrate that classical machine learning algorithms combined with TF-IDF remain highly effective for sentiment analysis of Indonesian-language text. The implications of this study are expected to assist sellers in understanding customer opinions, support consumers in making informed purchasing decisions, and serve as a foundation for the development of sentiment analysis and recommendation systems on e-commerce platforms.

This study presents a high-accuracy real-time soft failure detection framework for large-scale fiber-to-the-home(FTTH) optical access network using a hybrid ensemble of Isolation Forest and One-Class Support Vector Machine (OCVSM). The proposed model was trainde and validated on a real-word multivariate performance dataset comprising more than 1.8 million samples collected at 5-minute intervals from 50 Optical Line Terminal (OLTs) and over 3,000 Optical Network Terminals (ONTs) across a five-month periode(June-October 2025). Ground-truth validation was performed using 111 confirmed network incidents in October 2025 affecting 12,990 customer. The hybrid ensemble achieved Precision 0.940, Recall 0.982, with an average detection delay of only 7.8 minutes-representing an 87.7% reduction compared to conventional manual response (63.5 minutes). The framework significantly outperforms traditional threesholding and recent ML-based methods while demonstrating practical deployability in live operational enviroments.

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 examines the role of mathematics learning in improving logical thinking skills in early childhood. The background of this study is based on the importance of logical thinking skills as a foundation for children's cognitive development, which can begin at an early age through appropriate mathematics learning. The purpose of this study is to analyze how mathematics learning can stimulate the development of logical thinking in early childhood and explore effective learning strategies. The methods used are library research and observation of several models of mathematics learning for early childhood in early childhood education institutions. The findings indicate that fun and concrete activity-based mathematics learning can improve children's abilities in critical thinking, constructing patterns, drawing conclusions, and solving simple problems. The implications of this study emphasize the need for the application of creative and interactive mathematics learning methods to support the development of logical thinking from an early age, while also encouraging educators to integrate mathematics into children's daily activities. This study also recommends the development of learning media appropriate to children's developmental stages for optimal results.

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.

The increasing social and cultural diversity of contemporary societies has positioned schools as strategic spaces for nurturing inclusive attitudes and managing the challenges of pluralism. Many schools still face obstacles such as limited teacher readiness, inadequate curriculum representation of diversity, and school cultures that are not fully supportive of inclusive practices. This study aims to analyze school strategies in fostering students’ inclusive dispositions in response to pluralistic challenges. The research employed a descriptive qualitative approach through library research, using purposive sampling to select relevant scientific literature. Data were analyzed using an interactive model that involved data reduction, data display, and conclusion drawing. The findings indicate that participatory learning strategies, the application of Universal Design for Learning, and project-based learning are effective in strengthening students’ inclusive attitudes, empathy, and tolerance. The role of teachers as role models, the development of inclusive school culture, and active community involvement were identified as key supporting factors for successful inclusive education. The implications of this study highlight the importance of synergy between teacher professional development, curriculum adaptation, and school policy reinforcement to establish equitable, inclusive, and sustainable educational practices in pluralistic contexts.

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.

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.

This research aims to develop GeoQuest, an interactive learning media integrated with Artificial Intelligence (AI), to enhance the understanding of trigonometry concepts among Grade XI students at SMAN 1 Percut Sei Tuan. The development follows the Research and Development (R&D) approach using the ADDIE model, which consists of Analysis, Design, Development, Implementation, and Evaluation. The product integrates AI-based features such as adaptive practice questions, automated feedback, and dynamic visualizations of trigonometric graphs. Data were collected through expert validation, student response questionnaires, and learning outcome tests. The results show that the media meets the criteria of validity, practicality, and effectiveness. Material experts and media experts rated GeoQuest as "very valid," while students responded positively to its ease of use and engaging interface. Learning outcome tests indicate a significant improvement in students’ understanding of trigonometry after using the AI-based GeoQuest media. Thus, GeoQuest is proven to be a reliable and effective digital learning tool to support trigonometry learning.

End-to-end autonomous driving has emerged as a promising paradigm in which deep neural networks directly map raw visual inputs to continuous control actions. Despite its effectiveness, this approach suffers from limited transparency, posing significant challenges for deployment in safety-critical driving scenarios. This study addresses the lack of interpretability in vision-based end-to-end autonomous driving systems and aims to analyze model decision-making behavior under critical conditions such as sharp steering maneuvers and abrupt control transitions. To this end, an explainable end-to-end autonomous driving framework is proposed, combining a convolutional neural network trained via imitation learning with gradient-based visual attribution techniques, including Grad-CAM. The model predicts continuous steering, throttle, and braking commands directly from front-facing camera images, while explainability mechanisms are applied to reveal input regions influencing each control decision. Model performance is evaluated using both prediction accuracy and safety-oriented behavioral metrics. Experimental results show that the proposed explainable model achieves lower control prediction errors compared to a baseline end-to-end CNN, reducing steering mean squared error from 0.034 to 0.031, throttle error from 0.021 to 0.019, and brake error from 0.018 to 0.016. Moreover, safety-oriented analysis indicates improved driving stability, with steering variance reduced from 0.087 to 0.072 and abrupt control changes decreased from 14.6 to 10.3 events. Visual explanations consistently highlight road surfaces and lane-related structures during complex maneuvers, indicating reliance on semantically meaningful cues. In conclusion, the results demonstrate that integrating explainability into end-to-end autonomous driving not only preserves predictive performance but also correlates with smoother and more stable driving behavior. This framework contributes to the development of transparent and trustworthy autonomous driving systems suitable for safety-critical applications

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.

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.