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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 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.

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.

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.

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.

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.

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..

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 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.

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.

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 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.

The development of technology in the digital era has brought significant changes to global education systems, including in Indonesia. Digital transformation encourages learning models that are more flexible, interactive, and focused on 21st-century competencies. However, this progress also presents various challenges, particularly related to moral degradation among youth, unequal access to technology, and low digital literacy among teachers and students. This study aims to analyze character education models from a global perspective and evaluate the challenges and strategies for their implementation in Indonesia in the digital era. Using a qualitative research method with a library research approach, data were obtained from credible sources such as scientific journals, books, official reports, and national and international educational documents. The findings indicate that developed countries have successfully integrated technology into their curricula, teacher training, and educational policies, resulting in well- established digital learning ecosystems. In Indonesia, digitalization in education has grown rapidly, especially since the pandemic, but still faces obstacles such as limited infrastructure, varied levels of digital competence among teachers, and a lack of integration of digital pedagogy. In addition, student readiness and access inequality in frontier, outermost, and disadvantaged regions (3T) present further challenges in applying global education models

This study examines the role of teachers as Islamic counselors in addressing various psychological problems experienced by students, such as learning anxiety, academic stress, low self-confidence, difficulty controlling emotions, and social problems such as bullying. Teachers serve not only as instructors, but also as murabbi, mursyid, and moral role models in guiding students' spiritual and emotional development. The study used a qualitative method with a library study approach, where data were obtained through relevant literature related to Islamic counseling, Islamic education, and student psychology. The results of the study indicate that the implementation of Islamic counseling through strategies such as mau'izhah hasanah, tazkiyah an-nafs, prayer and dhikr, empathy development, and collaboration with guidance and counseling teachers and parents has been proven to reduce students' anxiety and stress levels, increase learning motivation, strengthen morals and character, and provide spiritual peace. Thus, teachers play a strategic role in creating a holistic educational environment and supporting students' psychological well-being. In addition, the implementation of Islamic counseling also plays a role in building students' mental resilience in facing academic and social pressures. In the long term, this will strengthen the Islamic values ​​applied in students, while creating a more resilient and noble generation.

General education at Islamic Religious Higher Education Institutions (PTKI) plays an important role in developing students’ critical thinking skills so they can analyze religious and social issues reflectively and contextually. However, learning practices in PTKI are still dominated by teacher-centered approaches and low cognitive achievement, which limits the development of analytical, evaluative, and problem-solving abilities. Meanwhile, studies show that integrating digital media, particularly video, can improve conceptual understanding, motivation, and cognitive engagement, yet a digital video-based meaningful learning model specifically designed for the PTKI context remains limited. Therefore, this study aims to formulate a Digital Video-Based Meaningful Learning Model (MPBBVD) to enhance students’ critical thinking skills. This research employed a literature review method by analyzing books, scientific articles, research reports, and digital sources related to meaningful learning, constructivism, multimedia learning, digital video use, and critical thinking development. Data were analyzed descriptively and qualitatively to identify the theoretical foundations, learning syntax, and design principles of the model. The findings indicate that MPBBVD integrates Ausubel’s Meaningful Learning theory, constructivist perspectives from Piaget, Vygotsky, and Bruner, Mayer’s Multimedia Learning theory, and the critical thinking framework of Facione and Ennis. The model uses digital video as the main learning medium and applies the ACTIVE stages (Attend, Connect, Think, Interpret, Verify, Evaluate) to promote contextual and interactive learning. Integrating pre-, during-, and post-video activities supports knowledge construction, reduces cognitive load, and increases student engagement, contributing theoretically and practically to improving critical thinking in PTKI in the Industrial Revolution 4.0 era.

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.

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.

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.

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

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.