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This study aims to describe the implementation of the Project-Based Learning (PjBL) model in creative dance learning. It also analyzes the results of creative dance projects produced by grade XI students of SMA Negeri 14 Semarang. This study uses a descriptive qualitative approach with data collection techniques including observation, interviews, and documentation. The research subjects consist of one Cultural Arts teacher and students of grade XI. Data analysis employs the interactive model of Miles and Huberman, including data reduction, data display, and conclusion drawing. The results show that the implementation of PjBL is conducted through seven systematic stages: project determination, planning, scheduling, exploration and movement creation, monitoring, project presentation, and reflection. Students demonstrate active involvement in group discussions, movement exploration, and collaboration. Each group successfully produces creative dance work with diverse themes, reflecting the development of creativity and critical thinking skills. This study highlights students creative processes at each stage of the project, which has not been widely explored in previous studies.

This study aims to analyze the implementation of the Reconnecting active learning method in Islamic Religious Education (PAI) for ninth-grade students at SMP Negeri 2 Sentani and to examine its role in enhancing students’ learning interest and engagement. This research employs a descriptive qualitative approach with a case study design to gain an in-depth understanding of the learning process. Data were collected through in-depth interviews with three PAI teachers, the school principal, and three students, and were further supported by classroom observations and documentation. The data analysis technique used the Miles and Huberman interactive model, which includes data reduction, data display, and conclusion drawing. The findings indicate that the implementation of the Reconnecting method has a positive impact on students’ learning experiences, particularly in increasing their interest, active participation, and ability to relate learning materials to real-life contexts. Students become more responsive and motivated during the learning process. However, several challenges were identified, including limited instructional time and varying levels of teacher readiness in applying the method effectively. Therefore, continuous teacher training and better time management are recommended to optimize the use of this method in PAI learning

This literature study examines the process of self-regulation in transforming compliance with school rules originating from external pressure into behavioral regularity emerging from personal awareness, and its impact on the effectiveness of student learning outcomes. Using a qualitative approach with content analysis method, this study synthesizes relevant literature to build a theoretical framework on how self-regulation facilitates the internalization of disciplinary values. The findings reveal that self-regulation occurs through a series of interconnected stages including goal setting, strategy planning, self-monitoring, evaluation, and adjustment. The success of this process is determined by internal motivation, appropriate environmental support, positive direct experiences, and healthy emotional management. Strong self-regulation directly impacts learning outcome effectiveness through improved cognitive strategies, strengthened intrinsic motivation, enhanced time and environment management, and developed capacity to constructively cope with failure. Learning outcomes achieved through self-regulation processes are characterized by lasting understanding, knowledge transfer ability, and the formation of lifelong learning dispositions. Schools and teachers play strategic roles in strengthening self-regulation through curriculum design that supports autonomy, formative feedback, role modeling, and collaboration with parents. This study contributes theoretically by positioning self-regulation as the central mechanism bridging external influences and internal disposition formation.

Cloud job-length prediction remains challenging when the target distribution is highly skewed and contains rare extreme values. This study proposes a log-transformed, regime-based machine learning framework for robust prediction of cloud job length, represented in million instructions (MI). The approach integrates sequential feature engineering, logarithmic target transformation, weighted learning, and regime-aware modeling to distinguish between normal and extreme job-length behavior. Using an ordered GoCJ-derived cloud job-length sequence of 1000 jobs, the dataset exhibits a heavy-tailed distribution, with a mean of 129,662 MI, a median of 93,000 MI, a 95th percentile of 525,000 MI, a 99th percentile of 900,000 MI, and a skewness of 3.695. The proposed model is evaluated against sequential baselines and stronger machine learning baselines, including Naive_Last, RollingMean_5, Global_Log_ExtraTrees, RandomForest, GradientBoosting, and MLP_Log. On the main test split, the proposed Regime_Log_ExtraTrees achieved the best RMSE of 206,255.66 and the least negative R² of −0.01062, while Global_Log_ExtraTrees remained competitive in terms of MAE, MedAE, and RMSLE. Additional walk-forward validation confirms that the regime-aware model consistently achieves the best mean RMSE and mean R² across temporal folds. Ablation results further show that regime-aware learning is the primary contributor to robustness, although accurate prediction of extreme jobs remains challenging. These findings indicate that log-transformed, regime-based learning provides a practical and more robust strategy for cloud job-length prediction under heavy-tailed workload conditions.

The importance of education as a foundation for developing an intelligent and qualified generation, with an emphasis on Islamic religious education as a tool for shaping students' character and morals. Islamic religious education, particularly in the study of faith and morals, emphasizes ethical values ​​such as caring and honesty. With the challenges in education becoming increasingly complex, one example is that many educators are unable to utilize learning media in today's advanced technology. One proposed solution is the use of the digital platform Kahoot, which has been proven to attract student interest in learning faith and morals, overcoming the boredom often encountered with traditional methods, such as lectures. It is emphasized that schools need adequate facilities and strategies for using gadgets to enhance students' learning experiences.The quantitative method used in this study is a pre-experimental study. In this study, the researcher used the one-group pretest and posttest design model. Based on the research results, there was a pretest score with a mean of 93.46, a median of 95, and a mode of 95. The posttest score had a mean of 84.62, a median of 90, and a mode of 100. The sig. (2-tailed) table showed 0.002 with a research alpha of 5% or 0.005. It can be concluded that there is no effect because 0.002 < 0.005. Therefore, H1 is rejected. Therefore, there is no effect of using the Kahoot application on the subject of faith and morals on improving student learning outcomes at MAS Tarbiyah Islamiyah.

This study explores the dynamics of children’s character formation within Christian families in the digital era from the perspective of church parents. Employing a descriptive light mixed-methods design, the study integrates survey data and semi-structured interviews to obtain both quantitative and qualitative insights. The findings indicate that digital media has become an integral and unavoidable part of family life, making children’s character formation highly dependent on parental guidance, reflective communication, and consistent faith-based education at home. Parents face various challenges related to time constraints, limited digital literacy, and the increasing exposure of children to external values that may not align with Christian teachings. At the same time, many parents actively utilize technology as a supportive and accessible tool for spiritual learning and moral development. The study concludes that character formation is shaped not merely by technology itself but more significantly by the quality of family relationships and parental role modeling grounded in Christian values. Therefore, the findings recommend strengthening faith-based digital literacy among parents and enhancing church–family collaboration to foster more holistic, balanced, and sustainable character development in children.

This study aims to describe the role of parents in increasing the motivation to learn to read and write the Qur'an (BTA) at TPQ Babul Khair Abepura and analyze its impact on the children's abilities. The research uses a qualitative approach with a case study design, involving teachers/ustadz, parents, and students as data sources. Data was collected through observations, interviews, and documentation, and analyzed using the Miles and Huberman model, with its validity tested through source triangulation. The results show that the role of parents is crucial in enhancing children's learning motivation, both as educators, motivators, facilitators, and role models. Active parental involvement has proven to improve discipline, enthusiasm for learning, and children's achievements in reading and writing the Qur'an. On the other hand, a lack of parental involvement negatively affects children's motivation and abilities. The motivation provided by parents helps in the smooth reading of the Qur'an, understanding tajwid, and writing hijaiyah letters. In conclusion, the success of BTA learning is not only dependent on the process at TPQ but is also influenced by parental involvement at home. The synergy between the family and educational institutions is crucial in improving the quality of Qur'an learning and shaping a morally upright generation.

This study aims to improve student engagement and learning outcomes in the subject of Natural and Social Sciences (Ilmu Pengetahuan Alam dan Sosial / IPAS) through the implementation of the Auditory, Intellectually, Repetition (AIR) learning model in a fourth-grade elementary school class. The initial problem indicated that student engagement in learning was still low, at 37.5%, with learning mastery reaching only 33.3% and an average class score of 68.0, which did not meet the Minimum Mastery Criteria (KKM) of 75. Therefore, improvement efforts were needed through the implementation of a more active and student-centered learning model. This study employed a Classroom Action Research (CAR) approach conducted in two cycles, where each cycle consisted of planning, action, observation, and reflection stages. The research subjects were 24 fourth-grade students. Data collection techniques included observation of student engagement, learning outcome evaluation tests, field notes, and documentation. Student engagement data were analyzed using percentages, while learning outcomes were analyzed through mean scores and the percentage of classical learning mastery. The results showed a significant improvement in each cycle. In Cycle I, student engagement increased to 62.5%, with learning mastery reaching 54.17% and an average score of 74.29, although it had not yet achieved classical completeness. In Cycle II, student engagement increased to 87.5%, with learning mastery reaching 100% and an average score of 85.42. These improvements indicate that the implementation of the AIR model was able to gradually and sustainably enhance both the learning process and outcomes. Based on these findings, it can be concluded that the Auditory, Intellectually, Repetition (AIR) learning model is effective in improving student engagement and learning outcomes in IPAS. This model can serve as an alternative learning strategy to create a more active, systematic, and student-centered learning environment.

This research is motivated by the urgency of effective Islamic Education Management (MPI) as a prerequisite for enhancing the role of Islamic Education (PAI) teachers in shaping students’ religious character amid the challenges of digitalization and institutional coordination constraints. The purpose of this study is to analyze the implementation of MPI experienced by PAI teachers, the interaction among stakeholders, and the teachers’ perceptions of its effectiveness at PKPPS Minhajurrosyidin, East Jakarta. Using a descriptive qualitative method with a case study and phenomenological approach, data were collected through triangulation (interviews, observations, and document studies) and analyzed using the Miles and Huberman interactive model. The findings indicate that MPI is an essential structural mediating variable. The optimization of MPI—particularly in terms of digital facility support and professional training—significantly enhances the effectiveness of PAI teachers as Uswah Hasanah and as facilitators of adaptive and scientific learning. However, the main obstacle lies in the lack of horizontal coordination among teachers in holistically integrating Values-Based Education. The implications of this study emphasize the need for formal managerial policies to ensure collective responsibility in character formation.

This study aims to develop a storytelling-based learning media in the form of a YouTube video to support students’ understanding of narrative text based on their learning needs. This study employed a Research and Development (R&D) design using the ADDIE model, focusing on the stages of analysis and development. The data were collected through a questionnaire as part of the needs analysis to identify students’ responses toward listening and repetition activities, as well as the difficulties they face in understanding narrative texts. The results show that students generally respond positively to listening and repetition. However, they still experience difficulties in understanding the storyline and vocabulary. The findings also indicate that students need learning support in the form of audio, visual elements, and vocabulary assistance. Based on these findings, a learning product was developed in the form of a YouTube-based digital storytelling video entitled “Learn Narrative Text Through Fun Storytelling (Listen, Repeat & Retell)”. The video integrates storytelling, repetition, vocabulary explanation, and speaking practice to support students’ comprehension. Due to time limitations, this study was limited to the development of a prototype and has not yet included expert validation. Therefore, future research is recommended to conduct validation to evaluate the feasibility and effectiveness of the developed product.

The retention of customers in the retail banking sector is a critical economic imperative; however, predictive modeling is frequently hindered by severe class imbalance and the “Black Box” nature of complex algorithms. This study proposes a Heterogeneous Stacking Ensemble framework integrating XGBoost, CatBoost, and Random Forest base learners with a Logistic Regression meta-learner to forecast customer attrition. To overcome the pervasive “Majority Class Bias,” we introduce a “Dual-Imbalance Defense” that synergizes the Synthetic Minority Over-sampling Technique (SMOTE) with algorithmic cost-sensitive penalization. Furthermore, moving beyond standard accuracy metrics, the framework mathematically derives a dynamic classification threshold to guarantee a strict 0.90 recall rate, actively optimizing the capture of at-risk capital. Model opacity is addressed through the integration of a SHapley Additive exPlanations (SHAP) TreeExplainer. This cooperative game theory approach provides localized, patient-level “Reason Codes” for regulatory compliance and reveals global systemic vulnerabilities, including non-linear drivers such as the “Product Paradox.” Achieving a 0.90 recall rate and an AUC of 0.8654, this framework provides a statistically robust and operationally transparent tool for targeted customer retention.

This study aims to develop and implement an artificial intelligence-based reading learning application using Deep Learning technology to enhance the literacy skills of eighth-grade junior high school students. The research employed the Kemmis & McTaggart Classroom Action Research model combined with a mixed-methods approach. Data collection involved pretests and posttests, complemented by observations, interviews, and questionnaires. The findings revealed that the use of this application significantly improved students' reading comprehension, question-answering skills, and overall engagement in the learning process. Key features of the application, such as adaptive learning technology, allowed for real-time adjustments to the difficulty level of the material, which catered to each student’s individual learning pace. Additionally, the provision of instant feedback enhanced the learning experience by helping students understand their progress and areas for improvement. These results suggest that the application is an effective tool in fostering literacy development and aligns with the goals of the Independent Curriculum. Consequently, this Deep Learning-based application offers a promising innovation for improving student literacy skills in the digital age. 

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

 Low student thesis completion productivity is a challenge in higher education, particularly at UIN Palangka Raya. Thesis writing requires self-regulation skills and time discipline to enable students to complete their final assignments effectively and on time. This study aims to analyze the role of self-regulated learning and time discipline in improving the thesis completion productivity of final-year students. The study used a qualitative approach with a case study design of five final-year students who were in the process of completing or had completed their theses. Data collection techniques included in-depth interviews, limited observation, and documentation. The data were then analyzed using the Miles and Huberman interactive model through the stages of data reduction, data presentation, and conclusion drawing. The results indicate that self-regulated learning plays a role in helping students plan goals, control motivation and emotions, and conduct consistent self-evaluation. Time discipline has been proven effective in reducing procrastination through the implementation of daily schedules, prioritization, and distraction management. Therefore, the integration of self-regulated learning and time discipline is an important strategy in increasing the thesis completion productivity of students and supporting sustainable academic success.

The digital era demands a fundamental transformation in the role of early childhood educators, shifting from passive technology consumers to active architects of digital literacy. However, the dominance of technocentric views often acts as a substantial psychological and pedagogical barrier for teachers in regional areas. This collaborative community service project aims to reconstruct the paradigm of 50 kindergarten teachers in Bulukumba Regency by integrating "unplugged coding" logic and deep learning into play-based learning. Utilizing a Product-Based Intensive Training method with a "Logic over Laptop" strategy, the program focused on deconstructing technology-related stigmas and reconstructing teachers' ability to transform abstract concepts into safe, concrete media for children. Data analysis revealed a significant shift in teacher paradigms; while the majority were initially in the "less successful" category, 100% of participants reached positive categories (successful and very successful) post-intervention. Statistically, the program's effectiveness was evidenced by a dramatic increase in mean scores from 18.04 to 31.24 (p < 0.05) and an N-Gain score of 0.778, classified as highly effective. Furthermore, the partner satisfaction index reached 4.82 (very satisfied), confirming that the tri-campus collaboration model (STAI Al-Gazali, UNM, and Unismuh) is highly relevant to the implementation of the Merdeka Belajar curriculum. This project concludes that strengthening digital literacy through non-digital algorithmic reasoning effectively dismantles technical barriers for teachers while ensuring the safety of child development in the digital age.

Attention mechanisms have been widely incorporated into recurrent neural network architectures for financial time series forecasting, with most prior work reporting improvements in price-level error metrics. This study revisits that claim through a controlled empirical comparison of four deep learning architectures on nearly two decades of Telkom Indonesia (TLKM) closing price data from the Indonesia Stock Exchange (IDX). The models evaluated are a three-layer Gated Recurrent Unit (GRU) baseline, a comparable Long Short-Term Memory (LSTM) network, a Bahdanau end-attention GRU (Attn-GRU-V2), and a multi-head self-attention GRU hybrid (Attn-GRU-V3). Each architecture is trained over 30 independent runs with distinct random seeds, and performance is reported as 95% confidence intervals derived from the t-distribution. Statistical comparisons employ the Wilcoxon signed-rank test, a nonparametric paired test appropriate given the confirmed non-normality of residuals. The main finding is a consistent trade-off: the plain GRU achieves the lowest RMSE (94.02 ± 1.22 IDR) across all 30 runs, while Attn-GRU-V2 achieves the highest directional accuracy (45.91 ± 0.09%), surpassing GRU in every independent run. Bahdanau attention weights are nearly uniform across the 30-day lookback window (coefficient of variation: 3.21%), indicating that the mechanism cannot identify selectively informative timesteps in this univariate price series. This finding is consistent with the weak-form Efficient Market Hypothesis for the Indonesian market. An ablation study reveals that a 20-day lookback window maximizes directional accuracy (47.72 ± 0.21%) for the Attn-GRU-V2 model. These results suggest that Bahdanau end-attention consistently and significantly improves directional accuracy relative to a plain GRU baseline, providing an architecturally attributable advantage for direction-based applications, even when absolute price-level error is not reduced. The directional accuracy values remaining below 50% across all models are consistent with a weak-form efficiency characterization of the Indonesian market.

This study aims to identify the learning needs of deaf students in internship programs, examine the challenges they face, develop appropriate solutions, and design as well as evaluate a visual module-based learning model to improve their work skills. The study used a Research and Development (R&D) approach with a 4D model: Define, Design, Develop, and Disseminate. The participants included deaf students from special needs high schools (SMALB) involved in vocational internships, mentor teachers, and industry supervisors. Data were collected through observation, interviews, questionnaires, documentation, and focus group discussions, and analyzed using qualitative techniques supported by descriptive analysis. The findings indicate that deaf students require visual, structured, and easily understandable work instructions supported by symbols, color codes, and guidance materials. Major challenges include limited verbal communication, difficulty understanding instructions, and risks of procedural errors. To address these issues, a systematic and communicative visual module-based learning model was developed, incorporating collaborative support from schools and industry. The resulting model integrates planning, implementation, mentoring, and evaluation stages, and has proven feasible and effective in enhancing students’ independence, technical competence, and overall work readiness.

The increasing sophistication of malware has rendered traditional signature-based detection methods insufficient, necessitating behavior-driven and adaptive analytical frameworks. This study presents a sequential deep learning framework that models system-level API call sequences as structured linguistic representations for behavioral malware detection. Unlike conventional comparative studies, this work systematically evaluates recurrent and attention-based architectures under controlled experimental conditions, with a particular focus on generalization performance and overfitting mitigation. Two neural architectures, a Long Short-Term Memory (LSTM) network and a Transformer-based attention model, are trained on publicly available API call sequence data for binary classification of malicious and benign executables. Beyond standard accuracy metrics, the study further examines model stability, convergence behavior, and the impact of long-range dependency modeling on detection robustness. Experimental results demonstrate that the Transformer architecture achieves superior performance, attaining 95.54% classification accuracy and consistent improvements in precision, recall, and F1-score, indicating a stronger ability to capture complex behavioral dependencies. These findings highlight the effectiveness of attention mechanisms in behavioral malware modeling and provide empirical evidence that NLP-inspired architectures offer a robust and scalable approach for real-world cybersecurity applications.

This study aims to descriptively analyze the ability of elementary students to imitate rhythmic gymnastics movements through the Just Dance Now application in Physical Education learning at SDN Bulay 1 Pamekasan. The background of this research stems from the low motivation and movement accuracy of students in conventional rhythmic gymnastics learning. Using a qualitative descriptive approach, this study involved 24 fifth-grade students as subjects. Data collection techniques included participatory observation, in-depth interviews, and documentation. Data were analyzed using the Miles and Huberman interactive model consisting of data reduction, data presentation, and conclusion drawing. The findings indicate that students' ability to imitate rhythmic gymnastics movements through Just Dance Now falls into the sufficient category, with students demonstrating good proficiency in basic movement patterns but experiencing challenges in complex body coordination and musical rhythm synchronization. The application successfully enhanced student enthusiasm and engagement compared to traditional methods. However, technical constraints such as internet connectivity and limited space were identified as implementation barriers. This study implies that digital game-based media like Just Dance Now can serve as an effective alternative visual aid in elementary physical education, particularly for rhythmic gymnastics instruction. Educators are encouraged to integrate technology creatively while addressing infrastructural limitations to optimize learning outcomes.