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High-dimensional clinical data exhibit complex and non-linear relationships among patient attributes, where outcomes are often influenced by feature interactions rather than isolated variables. However, many existing machine learning models prioritize predictive performance while providing limited interpretability and insufficient insight into interaction structures. This study aims to address this limitation by developing an interpretable and robust framework for feature interaction mining in clinical data. We propose a hybrid tree–neural modeling framework that explicitly captures and ranks feature interactions while maintaining stable predictive performance. Tree-based ensemble models are employed to identify non-linear interaction patterns, while neural representations enhance learning flexibility and generalization. The framework integrates interaction importance analysis, cross-validation–based stability assessment, and evaluation across multiple data splits to ensure robustness and interpretability. Experiments conducted on a real-world high-dimensional clinical dataset demonstrate that the proposed approach achieves consistent predictive performance, with AUC values ranging from 0.628 to 0.641 across five cross-validation folds (mean AUC ≈ 0.633). Performance remains stable under varying train–test splits, indicating strong generalizability. Interaction analysis reveals that a small number of dominant feature interactions—such as age combined with length of hospital stay and medication count combined with diagnostic information—consistently contribute to model predictions, appearing in over 80% of validation folds. Ablation studies further confirm that removing interaction-aware components leads to noticeable performance degradation, highlighting their importance.  In conclusion, this study demonstrates that explicit feature interaction modeling enhances interpretability, stability, and generalization in clinical prediction tasks. The proposed hybrid framework provides a reliable foundation for developing trustworthy and transparent clinical decision-support systems

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.

Learning media is a tool that can be used to help students learn better and more efficiently. The media used in SMP Negeri 23 Pekanbaru is a teacher-centered picture/poster media so that students quickly get bored, and because of the limited media from the school. Most students prefer learning that involves them directly such as the element of games. The learning models used are discovery, PBL, and lecture models, and student learning activities are still lacking or less active in science learning. The purpose of this study is to develop learning media in the form of a cooperative-based snakes and ladders game with the Teams Games Tournament (TGT) type which aims to train student learning activities. The research method used is Research and Development (R&D) by implementing the ADDIE model which consists of five stages, namely Analyze, Design, Development, Implement, and Evaluation. Based on the validation results obtained, media experts were 94% with very feasible criteria, material experts were 92% with very feasible criteria, teachers gave a practicality assessment of 95% with very practical criteria, and students' responses were 85% with very interested criteria. This shows that the snakes and ladders media based on Teams Games Tournament (TGT) is very reasonable, very practical to use, and very interesting for students during the learning process. The results of observations on all student learning activities showed that 79% got a good interpretation.

This study aims to analyze the application of reality therapy counseling in improving students’ learning motivation influenced by social and family environmental factors. This research uses a qualitative approach with a literature study method supported by interview data as complementary information. Data were collected through the analysis of relevant scientific literature and supported by interviews with parents to describe students’ daily behavior. The results show that low learning motivation is influenced by an unfavorable social environment and lack of family supervision, leading to behaviors that do not support learning activities, such as poor time management and low engagement in academic tasks. The application of reality therapy using the WDEP technique (wants, doing, evaluation, planning) helps students develop self-awareness, evaluate their behavior, and plan more adaptive actions. The findings indicate that reality therapy contributes positively to improving students’ learning motivation through behavioral change and increased personal responsibility. This study concludes that reality therapy can be used as an alternative approach in helping students overcome learning motivation problems.

This study aims to evaluate the Quran Tahfidz program at Bahrul Hikmah Islamic Junior High School using the Context, Input, Process, and Product (CIPP) evaluation model introduced by Stufflebeam. The findings of the research are as follows: First, the evaluation of the context aspect indicates that the program objectives have been comprehensively formulated. Data analysis confirms that the program implementation is well-aligned with the established goals, placing it in the 'good' category. Second, the evaluation of the input component reveals that the managerial competence of the administrators and the pedagogical capabilities of the educators have met the expected standards. Nevertheless, the infrastructure aspects require further optimization to foster a more conducive learning environment for students. Third, regarding the process aspect, the program implementation has generally achieved the targeted outcomes. However, observations identified obstacles predominantly stemming from students' internal factors; this implies the crucial role of teachers in integrating motivational stimuli as a preventive measure against declining learning enthusiasm. Fourth, the product evaluation shows that the daily memorization targets are generally achieved, although some students require remedial sessions. Ultimately, students are capable of completing the memorization of three juz within the three-year period.

This study aims to explore the effectiveness of group counseling services integrated with self-regulated learning (SRL) techniques in enhancing students’ learning motivation. Learning motivation is a crucial factor influencing academic achievement, yet many students still experience low motivation due to various internal and external challenges. The integration of SRL is considered beneficial as it encourages students to actively manage their learning processes through goal setting, self-monitoring, and reflective evaluation. This research employs a literature review approach by examining recent and relevant academic sources. The findings indicate that the application of SRL within group counseling contributes positively to students’ motivation, as reflected in increased participation, improved learning discipline, and stronger responsibility toward academic tasks. Furthermore, students demonstrate better ability in organizing their learning activities and maintaining consistency in achieving learning objectives. These results suggest that SRL-based group counseling can serve as an effective strategy for school counselors in supporting students’ academic motivation and engagement.

This study evaluates the effectiveness of group counseling integrated with self-management techniques in minimizing students' academic procrastination. Procrastination is a prevalent academic challenge driven by deficiencies in self-regulation, poor time management, and a lack of learning motivation. A quasi-experimental approach was employed, utilizing a pretest-posttest design. The participants consisted of students identified with high levels of task avoidance. The intervention was delivered through structured group counseling sessions focusing on self-monitoring, self-evaluation, and self-reinforcement strategies. The findings reveal a significant reduction in procrastination levels following the treatment. These results reinforce existing theories regarding the critical role of self-regulation in academic behavior. This study implies that organized self-management frameworks within group counseling can effectively foster student discipline and accountability toward their academic obligations.

Instilling saving habits from an early age is an essential component of character education, particularly in fostering discipline, responsibility, and independence. Initial observations at PAUD Nurul Islam, Jelutih Village indicate that early childhood saving interest remains relatively low due to limited engaging learning media and the absence of structured habituation. This Community Service Program (KKN) aims to foster early childhood saving interest through creative piggy bank media aligned with children’s developmental characteristics. The implementation method comprised planning, execution, and evaluation stages. During execution, children were introduced to saving concepts through simple storytelling and interactive dialogue, followed by hands-on creation of creative piggy banks using simple, child-friendly materials. These piggy banks were then used to habituate saving behavior. The results demonstrate increased enthusiasm and saving interest among children, reflected in their willingness to set aside money and heightened responsibility toward their piggy banks. Therefore, creative piggy bank media serve as an effective alternative learning tool for fostering saving interest and positive character development among early childhood learners at PAUD Nurul Islam, Jelutih Village.

Drug abuse (NAPZA) has become a serious threat among students and poses significant risks to their physical health, psychological well-being, and future development. Therefore, preventive efforts through structured education and mentoring programs are essential. This community service activity aims to describe the process and outcomes of a drug abuse prevention mentoring program conducted at SMP Negeri 2 Bulu, Temanggung. The program applied a participatory approach consisting of four stages: coordination with the school, educational socialization on the dangers of drugs, follow-up activities through discussions and student reflections, and evaluation to measure students’ understanding. The educational sessions were delivered using interactive lectures, question-and-answer methods, and visual learning media to enhance students’ engagement and comprehension. The results indicate an increase in students’ knowledge and awareness regarding the types, impacts, and prevention strategies of drug abuse. Moreover, students demonstrated more positive attitudes and a stronger commitment to avoiding drugs as a form of self-protection for their health and future. This program is expected to serve as a school-based preventive education model to reduce drug abuse among students through collaboration between teachers, schools, and the wider community.

This community service program aims to develop a local wisdom-based literacy model as an effort to improve students’ reading interest at SD Negeri 025 Samarinda Utara. The low level of reading interest among elementary school students remains a challenge in the implementation of the School Literacy Movement initiated by the Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi. One of the contributing factors is the lack of contextual reading materials that are relevant to students’ socio-cultural environment. Therefore, integrating local wisdom into literacy activities is considered an innovative strategy to enhance students’ engagement in reading activities.The implementation method included needs analysis, the design of a literacy model, program implementation through storytelling of East Kalimantan folktales, the development of a culturally themed reading corner, and teacher mentoring in integrating literacy into classroom learning. Evaluation was conducted through observation of student participation, reading interest questionnaires, and reflective discussions with teachers.The results indicate an increase in students’ enthusiasm for reading activities, a higher frequency of classroom reading corner utilization, and improved understanding of local cultural values. The local wisdom-based literacy model proved effective in creating a contextual, engaging, and meaningful learning atmosphere. This program is expected to serve as a sustainable model for strengthening literacy culture in elementary schools.

Hypertension is a non-communicable disease with a high prevalence and is a major risk factor for serious complications such as stroke and heart disease. Low public awareness and limited capacity of health workers to provide education and early detection pose challenges in hypertension prevention efforts at the community level. The program “Capacity Building for Health Workers as an Effort to Prevent and Control Hypertension in Mandiangin Timur Village” was implemented by Field Learning Experience (PBL) Group 20 students in August 2025 in Mandiangin Timur Village, Karang Intan District, Banjar Regency. The intervention was carried out through interactive lectures, discussions, and the use of educational media in the form of posters, leaflets, and videos. The targets of the activity consisted of 17 cadres and 30 villagers. Knowledge evaluation using pre-tests and post-tests showed an increase in the average score of cadres from 95.28 to 96.46, while the community increased from 76 to 92.33. Blood pressure checks also showed that most participants were in stage 1–2 hypertension, emphasizing the need for ongoing education. This program proved effective in increasing the capacity of cadres and community knowledge about risk factors, prevention, and management of hypertension. In addition, this activity strengthened the role of cadres as agents of change in ongoing efforts to control hypertension at the village level.

English speaking skill is a crucial competence for university students, particularly those involved in student organizations focusing on language development. Members of English clubs are expected to actively use English in academic and organizational contexts; however, many still face challenges related to pronunciation accuracy, fluency, limited active vocabulary, and low confidence. These challenges are often caused by limited structured speaking practice and insufficient integration between digital learning tools and communicative pedagogy.This community service program aimed to enhance the English-speaking skills of members of the Bhamada English Club at Universitas Bhamada Slawi through the utilization of the Duolingo application integrated with a Task-Based Learning (TBL) approach. The program was implemented through three stages: preparation, implementation, and evaluation. Activities included needs analysis, speaking pre-test, guided Duolingo practice focusing on pronunciation and speaking features, task-based speaking activities, and post-test evaluation.The results demonstrated improvements in pronunciation accuracy, speaking fluency, and participants’ confidence in using English orally. The integration of Duolingo as a source of comprehensible input and Task-Based Learning as a communicative output strategy proved effective in enhancing speaking skills. This program indicates that technology-supported task-based instruction can serve as an effective and sustainable model for improving English speaking skills among university students.

Effective communication through the SBAR (Situation, Background, Assessment, Recommendation) method is a crucial component in improving patient safety and the quality of nursing care. However, nursing students often fail to systematically implement SBAR communication during clinical practice. This community service project aims to improve students' SBAR communication competency through mentoring-based training with an experiential learning approach at RSU Haji Medan. The method used was a pretest–posttest design without a control group involving 15 nursing students. The intervention was carried out through the stages of reinforcing the SBAR concept, direct mentoring in the treatment room, and observation- and reflection-based evaluation. Competency was measured using an observation sheet covering the aspects of Situation, Background, Assessment, Recommendation, communication clarity, and self-confidence. The results showed an increase in the average score from 65.6 in the pretest to 83.8 in the posttest, with an average increase of 18.2 points and all participants experienced an increase (100%). These findings indicate that experiential learning-based mentoring is effective in improving students' SBAR communication competency. In addition to improving individual skills, this program also contributes to building a culture of professional communication and supporting patient safety in the hospital environment

This community service activity was conducted to optimize English language learning through the implementation of the Flipped Classroom model for students of SMP IT Imam Syafi’i 2 Pekanbaru, Riau, Indonesia. The activity aimed to enhance students’ learning engagement, autonomy, and comprehension in English through a student-centered instructional approach. The program was implemented in three stages: preparation, implementation, and evaluation. Learning materials in the form of instructional videos and digital resources were provided prior to classroom meetings to enable students to study independently. Face-to-face sessions were then devoted to interactive discussions, collaborative tasks, and communicative practice to reinforce students’ understanding and language skills. The outcomes of the program indicated increased student participation, improved motivation, and better mastery of English materials. The Flipped Classroom model contributed to creating a more active and meaningful learning environment. Therefore, this community service initiative demonstrates that the Flipped Classroom approach can serve as an effective instructional strategy to support English language learning at the junior high school level

This research aims to develop and assess the feasibility of Microsoft Access-based learning media as a digital archive tool. Using a Research and Development (R&D) approach, the study follows the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The participants included one media expert, one material expert, one language expert, and 33 class X MPLB students at SMK Negeri 49 Jakarta. Data collection was conducted through observations, interviews, and questionnaires rated on a Likert scale, evaluated by experts and students. The findings show that: 1) The Microsoft Access-based media, developed for the MPLB basics subject, can be run on laptops or computers; 2) The media suitability received an 81.3% rating ("Very Appropriate"), material suitability 89.3% ("Very Appropriate"), and language suitability 100% ("Very Appropriate"). In the trials, individual testing with three students yielded 89.6% ("Very Eligible"), small group testing with 10 students received 75.7% ("Decent"), and field testing with 20 students reached 86.8% ("Very Eligible"). 3) The product is deemed highly suitable by experts, with strong support from small group and field trials, confirming its effectiveness as a learning tool.

This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets and detecting emerging malware variants, the study employed SMOTE to ensure balanced training data and fair model evaluation. The dataset was divided into 80% training and 20% testing subsets, and models were assessed using key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. The findings revealed that the proposed Random Forest model outperformed the other classifiers, achieving an accuracy of 0.993, precision of 0.992, recall of 1.000, F1-score of 0.996, and a near-perfect ROC AUC of 0.9998 surpassing state-of-the-art approaches. These results affirm the superior predictive capability, consistency, and robustness of the Random Forest algorithm in Android malware detection. The study concludes that base models, when integrated with class-balancing techniques, provide reliable and efficient malware detection across imbalanced datasets. For future research, the study recommends exploring advanced hybrid or ensemble frameworks that integrate Random Forest with deep learning architectures or other meta-heuristic optimization techniques to further enhance detection accuracy, adaptability, and resilience against rapidly evolving Android malware threats.

The issue of plastic waste is increasingly becoming an environmental concern, including in elementary schools, due to its non-biodegradable nature and its potential to cause environmental pollution if not properly managed. This activity aims to implement the Ecobrick method as an innovative solution for managing and utilizing plastic waste at SDN 054945 Desa Dogang, Gebang District, Langkat Regency. The method used is a descriptive qualitative approach involving 65 students from grades IV and V through a community service activity. The activity was carried out on July 17, 2024, through preparation, implementation, and evaluation stages, with data collection techniques including observation, documentation, and reflective discussion. The results show that the application of the Ecobrick method successfully increased students' understanding and awareness of the dangers of plastic waste and fostered an early environmental concern. Moreover, previously worthless plastic waste was processed into functional Ecobrick benches that can be utilized within the school environment. This activity also encouraged a sense of mutual cooperation, responsibility, and creativity among the students. Thus, the Ecobrick method has proven to be an effective alternative for plastic waste management and a contextual learning medium in creating an environmentally friendly school culture.

Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly in resource-limited settings and among elderly populations, where timely diagnosis and continuous monitoring are often constrained by limited clinical infrastructure. This study presents an edge–cloud–integrated framework for early pneumonia risk monitoring, leveraging multimodal wearable sensors and deep learning to support continuous short-duration monitoring. The proposed system is designed to operate in near real time under simulated deployment conditions, continuously acquiring and analyzing physiological signals (respiratory rate, heart rate, SpO₂, and body temperature) alongside event-driven acoustic biomarkers (cough sounds) within a distributed architecture. A lightweight edge module performs local signal preprocessing and anomaly triage, selectively transmitting salient information to a cloud-based multimodal deep learning model for refined risk estimation and interpretability analysis. The framework was evaluated using a multi-source dataset comprising public repositories (MIMIC-III and Coswara) and a clinically supervised wearable study conducted in two Nigerian hospitals, resulting in 718  hours of quality-controlled multimodal monitoring data. In a pooled multi-source evaluation, the system achieved an AUC of 0.95, while in a clinically realistic local-only evaluation, the AUC was 0.86, reflecting a consistent but preliminary diagnostic signal. These results highlight the importance of local data adaptation for real-world applicability and suggest that multimodal AI can provide meaningful early risk indicators under resource constraints. Beyond predictive performance, this work demonstrates the feasibility of integrating multimodal learning, edge–cloud computation, and explainable analytics into a deployment-aware, privacy-preserving monitoring framework for low-resource healthcare environments.

This article aims to explore the evaluation of the context, inputs, processes, and products of the implementation of Islamic Religious Education and Character Education at SMA Negeri 1 Tikep. Until now, there are still many obstacles faced by a number of educational institutions, especially in the aspects of implementation and evaluation. This study uses a qualitative approach and a type of evaluation research with the CIPP (Context, Input, Process, and Product) evaluation model, which was developed by Stufflebeam. This research was conducted by analyzing the data to answer the problem formulation without testing the hypothesis. The main data from this study were obtained through descriptive analysis, with the data collection process through interviews, documentation, and Post-Test as additional data. The results of this study indicate that from the perspective of the CIPP evaluation model developed by Stufflebeam, the context, input, process, and product aspects of PAI and Budi Pekerti learning based on the 2013 curriculum at SMA Negeri 1 Tikep  are included in the good category.

This study was motivated by the importance of the availability and quality of infrastructure as a major supporting factor for the comfort and smooth running of the lecture process at the postgraduate level. The purpose of this study was to evaluate educational facilities and infrastructure based on the perceptions of postgraduate students in the Islamic Education Study Program (PAI) using the Context, Input, Process, and Product (CIPP) evaluation model. This research is an evaluative study with a qualitative approach, involving PAI graduate students as research subjects. Data collection was conducted through questionnaires, in-depth interviews, and field observations, then analyzed using the Question Discourse technique to obtain a comprehensive understanding of the students' experiences and assessments. The results show that students view facilities and infrastructure as important to very important in supporting the lecture process. In general, the facilities are considered adequate, but there are still limitations in the air conditioning system and internet network stability, which affect the comfort and effectiveness of learning. The implications of this study emphasize the need for continuous improvement in the quality, maintenance, and management of facilities and infrastructure to support the quality of PAI postgraduate education.