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Farmer empowerment through the development of local agricultural commodities is an important strategy to increase farmers’ income and strengthen the independence of farmer groups. This community service activity aimed to enhance the capacity of the Padimas Farmer Group in Sana Tengah Village, Pasean Sub-district, Pamekasan Regency, through the development of local Madura melon as a regional flagship commodity. The program was implemented using a participatory–collaborative approach that actively involved farmers in all stages of the activity, including the identification of local needs and potentials, provision of demonstration plot land, cultivation assistance, and continuous monitoring and evaluation. The melon demonstration plot served as a practical learning medium for farmers in applying cultivation techniques adapted to local agroclimatic conditions. The results showed that the development of the local melon demonstration plot significantly improved farmers’ knowledge and skills in melon cultivation, with a plant survival rate reaching 99%. In addition, this activity supported the establishment of a group-based flagship commodity with promising economic value and market opportunities. Overall, the farmer empowerment program contributed positively to strengthening farmers’ economic independence and has the potential to serve as a model for sustainable horticultural agribusiness development in the Pamekasan region.

This study investigates the influence of financial literacy, entrepreneurship training, and financial inclusion on the performance of micro, small, and medium enterprises (MSMEs) through business sustainability. Using a systematic literature review (SLR) examines the impact of financial literacy, entrepreneurship training, and financial inclusion on MSME performance through business sustainability mediation by synthesizing empirical data from 12 research (2020–2025) across Scopus and Web of Science. Positive direct effects on sustainability (financial literacy via budgeting/risk management; training via adaptive resilience; inclusiveness via digital access) and performance metrics like profitability/growth are confirmed by results using the PRISMA 2020 flow.  Amid obstacles like financial access restrictions and COVID-19 disruptions, business sustainability appears as a crucial mediator, linking these factors to improved MSME results in developing contexts (Africa, Indonesia). Practical implications compel policymakers to give integrated literacy programs, contextual training, and inclusive finance top priority. Theoretical contributions combine financial literacy, entrepreneurial learning, and sustainability ideas into a holistic mediation model. The results highlight the importance of integrating financial education, entrepreneurial skill development, and inclusive financial systems to strengthen MSME resilience and competitiveness. This study provides practical implications for policymakers, financial institutions, and support organisations in designing effective interventions that foster sustainable business growth. The research also contributes theoretically by confirming the mediating role of business sustainability in the relationship between financial literacy, entrepreneurship training, financial inclusion, and MSME performance. Future studies may expand these insights by examining additional contextual factors such as digital technology adoption and business networking that further support sustainable MSME development.

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.

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.

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.

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 develops an interpretable machine learning model to predict the settlement status of Hajj fees in Jambi Province, Indonesia. Utilizing the XGBoost algorithm on a dataset of 4,332 prospective pilgrims from 2025, the research addresses the critical challenge of class imbalance where only 28.5% of samples are labeled "Unsettled". The baseline XGBoost model achieved a ROC-AUC of 0.7778, with a recall of 0.3482 for the minority class. SHAP (SHapley Additive exPlanations) analysis was employed to interpret model predictions, revealing that financial features specifically NILAI_VA (Virtual Account Value), JML_SETORAN (Deposit Amount), and JML_PELUNASAN (Settlement Amount) are the most significant factors influencing repayment risk, with negative SHAP values indicating increased default probability. The findings demonstrate that an interpretable XGBoost framework can provide both predictive accuracy and actionable insights for policymakers, enabling targeted interventions such as flexible payment schemes and enhanced financial monitoring for high-risk pilgrims..

Economic forecasting is becoming increasingly important year after year, especially during crises such as the pandemic of COVID-19 and the Russia-Ukraine war. Its development can be seen from the use of basic statistical models to the increasingly widespread use of machine learning technology. Economic forecasting plays an important role in helping to formulate policies and is also a reliable tool for researchers in dealing with uncertainty. Global crises, such as inflationary pressures due to the pandemic and supply chain disruptions from the Russia-Ukraine conflict, have prompted increased research in this field in an effort to anticipate economic shocks and emphasize the urgency of forecasting to prepare strategies for dealing with future uncertainty. This literature review uses the Scopus database with 2561 publications from 2020 to 2025, analyzed using R Studio with a bibliometrix approach (specifically biblioshiny) and VOSviewer to map relevant thematic connections. This analysis shows that economic forecasting is greatly influenced by market uncertainty and geopolitical factors, and at the same time influences public policy formulation and financial stability. Research contributions from Indonesia are still limited, with only 40 documents, thus emphasizing the need to strengthen economic forecasting studies in Indonesia to support monetary policy and national financial stability.

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

This 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.

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.

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.

The rapid development of digital technology and the increasing demand for entrepreneurial competence in higher education require students to possess adequate digital literacy and financial literacy, as well as obtain continuous institutional support to shape and strengthen their entrepreneurial interest. This study aims to empirically analyze the influence of digital literacy, financial literacy, and institutional support on the entrepreneurial interest of students in the Islamic Business Management Study Program at the Faculty of Economics and Islamic Business, UIN Raden Mas Said Surakarta. A quantitative approach was used, involving 75 respondents selected through accidental sampling, while the research data were analyzed using multiple linear regression. The results of the study indicate that all three independent variables have a positive and significant effect on students' entrepreneurial interest, with the model contributing 42.7%. These findings highlight the importance of integrating digital competencies, effective financial management, and a supportive campus ecosystem to foster students' entrepreneurial intentions. This study provides practical implications for higher education institutions to strengthen curricula, learning facilities, and mentoring programs aimed at developing young, digitally-driven, and sustainable entrepreneurs.

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.

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.

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.

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.

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.

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.

This study was motivated by the low level of internalisation of religious values in Islamic Religious Education, especially at the madrasah level, where the process of instilling valuesoften stops at the cognitive level. This study aims todescribe the process of internalising religious values through the film Laut Tengahand assess its effectiveness in improving students' understanding andappreciation of religious values at MTs Manbail Futuh 2 Bancar. The approach used was qualitative with a case study design, because the phenomenon under reviewwas contextual and required in-depth understanding. Datawere collected through direct observation during learning, interviews with teachers andstudents, as well as documentation of learning tools and film clips. Data analysiswas carried out through data condensation, data presentation, and drawingconclusions using the Miles, Huberman, and Saldaña model. The results showedthat the film Laut Tengahwas effective in facilitating the internalisation of religious values through the visual,emotional, and cognitive engagement of students. Students were able to identify values such ashonesty, patience, discipline, gratitude, and social awareness, anddemonstrated appreciation reflected in their self-reflections. With teacher guidance, films became a medium capable of connecting values with the real experiences ofstudents.