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Measurement learning is a fundamental and applicable mathematical topic in everyday life, but it often causes learning difficulties for students, especially in understanding the meaning of units, relationships between quantities, and the conceptual measurement process. These difficulties are not only caused by students' limited cognitive abilities, but also by learning designs that do not fully facilitate social interaction and student learning development. This study aims to reconstruct the contextual didactic design in measurement learning by reviewing the role of social interaction and the Zone of Proximal Development (ZPD) in the student learning process. This study uses a qualitative approach with the type of Didactic Design Research (DDR). The research stages include analysis of the initial didactic situation to identify student learning barriers, implementation of contextual didactic design in measurement learning, and retrospective analysis of student responses as a basis for reconstructing the didactic design. Data were collected through learning observations, analysis of student work results, interviews, and learning documentation. Data analysis was carried out qualitatively by examining social interaction patterns, forms of scaffolding, and student movements in the Zone of Proximal Development. The results of this study indicate that understanding of measurement concepts develops through social interactions between students and between students and teachers within a meaningful learning context. Social interactions and scaffolding provided gradually can encourage students to move from actual abilities to potential abilities within the Zone of Proximal Development. Retrospective analysis indicates that reconstruction of the didactic design is necessary to refine the learning context, activity sequence, and scaffolding strategies to better align with students' learning characteristics. The reconstruction of the didactic design can reduce learning barriers and improve the quality of students' conceptual understanding in measurement learning. This research provides theoretical contributions to the study of social constructivism-based mathematics education and provides practical implications for teachers in designing measurement learning that is more responsive to social interactions and student learning development.

Reading literacy in early childhood is an important foundation for children's cognitive, language, and social development in subsequent educational stages. Literacy in early childhood can begin through letter recognition, fostering interest in printed materials, enriching vocabulary, and developing oral communication skills. At this stage, the initial skill that needs to be developed is the introduction of written language through reading activities tailored to the child's developmental characteristics. However, early childhood's ability to understand reading literacy shows significant differences, both in terms of interest, ability, and environmental support. This condition indicates that reading literacy understanding in early childhood is not evenly distributed. Therefore, this community service activity aims to provide reading literacy education to early childhood through a fun, interactive, and contextual approach. The activity methods include reading assistance, the use of visual media, and the active involvement of educators and parents. The results of the activity show an increase in reading interest, letter recognition skills, and children's positive responses to literacy activities. This activity is expected to be a sustainable effort to support the strengthening of reading literacy in early childhood education.

Problem solving in permutation and combination requires the ability to understand context, choose strategies, and perform calculation procedures accurately. Based on the analysis of students' answers, it was found that difficulties arose consistently at each stage of problem solving according to Polya, namely the problem understanding stage, the planning stage, the plan implementation stage, and the rechecking stage. In general, students' weaknesses are not only related to their understanding of permutation and combination concepts, but also to their inability to apply problem-solving steps systematically. The results of the study indicate the need for a learning approach that not only focuses on mastering formulas, but also strengthens problem literacy, the ability to identify relevant information, and the selection of solution strategies appropriate to the characteristics of the problem. In addition, the habit of reflection through reviewing the process and results of the solution needs to be developed consistently so that students are able to recognize mistakes and improve their accuracy in solving permutation and combination word problems in a more accurate, logical, and structured manner.

This article examines various guidance and counseling (BK) management models relevant to the context of modern education in Indonesia. The models discussed include POAC (Planning, Organizing, Actuating, Controlling) management, comprehensive BK services, school-based BK management, specialized service management, and the use of digitalization in BK services. This review was compiled using a literature review method, drawing on various credible national sources, such as scientific journals, reference books, and education policy documents. The purpose of this research is to provide an in-depth understanding of the characteristics, principles, and implementation of each BK management model in educational practice. The results indicate that each model has its own advantages and limitations, both in terms of service planning, resource management, and the effectiveness of service delivery to students. Therefore, the selection and implementation of a BK management model must be tailored to the needs of the school, the availability of professional staff, infrastructure, and student characteristics. The integration of a systematic, data-driven, and adaptive management approach to developments in information technology is a key factor in improving the quality and effectiveness of BK services. This article is expected to provide theoretical and practical contributions to school counselors, educators, and education policymakers.  

Generation Z, growing up in the digital era, faces various challenges in social, emotional, and academic adjustment in the school environment. This study aims to determine the effectiveness of individual planning services in improving the adjustment abilities of Generation Z students. A qualitative approach was used with a literature review from the past five years. The results of the study indicate that the use of digital technology to support students' personal development can help them better understand themselves, set goals, and plan adaptive steps. This service also plays a role in providing support and early intervention, which helps counselors shift from a reactive approach to a more proactive one. The findings of this study underscore the importance of changing the role of counselors, from being responsive to being more proactive and preventative, by integrating personal and technological approaches in the mentoring process. Therefore, guidance and counseling teachers are advised to design services that are more structured, participatory, and relevant to the unique characteristics of Generation Z. This will ensure that the services provided are more effective in helping students adjust to the school environment.

Dengue Hemorrhagic Fever (DHF) is a life-threatening disease caused by the dengue virus, which consists of four serotypes: DEN-1, DEN-2, DEN-3, and DEN-4. Laboratory examinations supporting DHF diagnosis include complete blood count, urine analysis, serological tests, and viral identification using Reverse Transcriptase Polymerase Chain Reaction (RT-PCR). Prevention efforts focus on eliminating Aedes aegypti mosquito larvae through the implementation of 3M Plus, as well as using mosquito bite prevention products such as aromatherapy candles that are simple and practical for community use. Mancasan Baki Village, Sukoharjo, has experienced a high incidence of dengue cases, including fatalities, highlighting the need for community education on the genetic characteristics and molecular diagnostics of the dengue virus and preventive measures using innovative products. Educational activities were conducted through community presentations and demonstrations on making aromatherapy candles. The effectiveness of the program was measured by comparing participants’ knowledge before and after the intervention. The average pretest score was 6.63, which increased to 8.89 in the posttest. Statistical analysis using a t-test showed a significant difference (p < 0.05). Participant satisfaction results indicated that 64.5% rated the program as good, 32.5% as very good, and 3% as fairly good.

Micro, Small, and Medium Enterprises (MSMEs) are the backbone of regional economies, including in the city of Banjarmasin, as they play a crucial role in job creation and income generation for local communities. However, many MSMEs continue to face challenges in the marketing aspect, particularly due to their reliance on conventional marketing methods that have limited reach and require relatively high costs. In response to these issues, this community service program aims to promote the digitalization of MSME marketing methods in Banjarmasin through intensive training and mentoring in the utilization of social media marketing. The implementation methods include an initial needs assessment survey to identify partners’ levels of understanding, workshops on digital marketing strategies, and hands-on mentoring for direct implementation on social media platforms such as Instagram and Facebook. The results of the program indicate a significant improvement in MSME actors’ knowledge and skills, particularly in managing business accounts, developing content strategies, and creating visually appealing content that aligns with target market characteristics. Furthermore, MSME partners were able to expand their market reach more effectively and efficiently, which is expected to enhance business competitiveness and sustainability in the digital era.

This study aims to test the effectiveness of the implementation of a simplified Mini-QHSE (Quality, Health, Safety, Environment) system in improving working conditions at the Syams Handicraft woven craft MSME in Pati Regency, Central Java. The study used a quantitative method with a one-group pretest–posttest design involving 47 workers as respondents. Data collection was conducted through questionnaires and observations before and after the twelve-week intervention. The results showed that the implementation of the Mini-QHSE system was able to significantly reduce the level of occupational safety and health risks and significantly improve worker welfare. Further analysis revealed that the Mini-QHSE system made a significant contribution to variations in OHS risks and worker welfare. The dimension of safe work procedures proved to be the most influential factor in reducing work risks, while the training aspect was the main factor in improving worker welfare. Based on these findings, it can be concluded that the Mini-QHSE system designed according to the capacity and characteristics of MSMEs is effective in creating a safer and more prosperous work environment. This model has the potential to be applied to similar MSMEs with contextual adjustments as a practical solution in managing quality, safety, and occupational health in the micro-business sector.

Early Childhood Education (PAUD) emphasizes the principle of child-centered learning through meaningful and contextual play activities. Environment-based learning is one of the approaches that is relevant to the characteristics of early childhood because it provides a real learning experience and is close to the child's life. This study aims to describe in depth the implementation of environment-based play learning and its impact on cognitive, language, social-emotional, and motor development in early childhood. The research method used was qualitative descriptive with group B child subjects in PAUD Mawar. Data collection techniques are carried out through observation, interviews, and documentation. The results of the study show that environment-based play learning is able to increase children's active involvement, curiosity, communication skills, cooperation, and motor skills. The conclusion of this study shows that environment-based play learning is effectively applied in PAUD Mawar and can be used as an alternative to contextual and holistic learning strategies.

Stunting and wasting are nutritional problems in toddlers that remain a double burden of malnutrition in Indonesia and have an impact on the quality of health and future human resource development. Monitoring the nutritional status of toddlers is generally carried out using anthropometric indicators, but the use of this data is still limited to descriptive analysis. This study aims to apply the K-Means algorithm in clustering infants vulnerable to stunting and wasting based on anthropometric indicators, so that groups of infants with different levels of nutritional vulnerability can be identified. The dataset used consists of infant data with variables of gender, age (months), height (cm), and weight (kg). The research stages included data preprocessing, encoding categorical variables, data normalization, determining the optimal number of clusters using the Elbow and Silhouette Score methods, and analyzing the characteristics of each cluster. The evaluation results showed that the optimal number of clusters was four. Each cluster has different anthropometric characteristics and distributions of stunting and wasting status, ranging from groups with relatively normal nutritional conditions, groups with a tendency toward overnutrition, to groups that are vulnerable to acute and chronic malnutrition. These clustering results provide a more comprehensive and segmented mapping of toddlers, which can be used as a basis for formulating more targeted and data-driven nutrition policies and interventions.

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.

Diabetes Mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action or both. Hyperglycemia is a medical condition in the form of an increase in glucose levels beyond normal limits which is a characteristic of several diseases, especially Diabetes Mellitus, in addition to various other conditions. Diabetes Mellitus is currently a global health threat. Classification is one of the techniques of data mining that can be used to help predict the results of the classification of types of diabetes using the naïve Bayes algorithm. Testing was carried out using 5 evaluation models including rapid miner with 3 options, namely use training set, 5 Fold Cross-Validation, 10 Fold Cross-Validation, and 2 other evaluation models, namely Microsoft Excel and Python. Testing data regarding Diabetes Mellitus has high accuracy in the excel evaluation model, which is 89.00% compared to other evaluation models. Meanwhile, the lowest accuracy is the Python evaluation model which obtains an accuracy of 86.36%. The Naïve Bayes algorithm can be said to be one of the most effective algorithms, both in terms of calculations and the final results, where the test can be used as a basis for diabetes mellitus considering the accuracy results are above 85%.

The massive adoption of Internet of Things (IoT) devices is expanding the cyberattacks surface, particularly by the Mirai botnet, which exploits the dynamic characteristics of data traffic. This research proposes a Mirai detection approach based on a Recurrent Neural Network (RNN) optimized using Bayesian Optimization to improve prediction accuracy on sequential data. Unlike previous studies, this research utilizes the latest CIC IoT-DIAD 2024 dataset and applies probabilistic optimization to the hyperparameter space, including RNN units, dropout, and learning rate. The experiment was conducted on 201,021 valid data points, with dimensionality reduction using PCA as the optimal point to represent essential features without redundancy. The results show a significant increase in accuracy from 97.95% to 99.69%, accompanied by an 84% decrease in False Negatives, an 86% decrease in False Positives, and an AUC value of 0.9999. These findings confirm that integrating RNN and Bayesian Optimization not only improves numerical performance but also strengthens the reliability of the intrusion detection system for modern IoT ecosystems with controlled computational loads.

Sentiment analysis is a technique in Natural Language Processing (NLP) used to identify user opinion tendencies based on textual reviews. This study analyzer user reviews of the Maxim application on the Google Play Store and compares three Machine Learning algoritmhs-Naïve Bayes, Support Vector Machine (SVM), and CatBoost-in classifying sentiment. The research stages include data collection, text preprocessing, feature extraction using TF-IDF and Chi-Square, class balancing using SMOTE, and performance evaluation through Accuracy, Precision, Recall, and F1-Score. ANOVA is used to examine the influence of feature selection on model performance. The results show that each model exhibits different performance level across the tested feature combinations. The CatBoost achieved the highest accuracy of 99,26% and demonstrating the most stable performance. Meanwhile, the Naïve Bayes and SVM models experienced performance decreases experiments, especially after applying SMOTE. These findings indicate that the choise of algorithm, feature extraction method, and class balancing technique significantly affects classification outcomes. Overall, CatBoost is identified as the best-performing model, providing more consistenst classification result in accordance with the characteristics of the user reviews.

AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.

This study 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.

This study aims to analyze the effect of the use of kahoot game media on the cognitive learning outcomes of moral beliefs in grade VII C MTS negeri 1 Tuban students. The background of this research is the low enthusiasm and learning outcomes of students in learning moral beliefs which are still dominated by conventional methods while the characteristics of agency students demand a more interactive and adaptive learning approach to technology. This study uses a quantitative approach with a quasi-experimental design of one group pretest design involving 34 students as a sample. This research instrument is in the form of a multiple-choice cognitive learning outcome test of 20 questions covering 4 levels of bloom taxonomy, namely remembering, understanding, applying, and analyzing. The data was analyzed using descriptive statistics and normalistic calculations, the results of the study showed a significant increase in students' cognitive learning outcomes where the average class score increased from 66.03 in the pre-test to 80.29 in the post-test with an engine value of 0.42 which was in the medium category. These findings identify that kahood media has a positive impact on improving students' cognitive learning outcomes.

The discourse on the attributes of God has developed to include the issue of His physical attributes, as described in the texts, which state that God has hands, a face, a chair, a throne, and so on. This article employs a literature study as its method. Literature data are secondary sources, meaning the researcher obtains material indirectly and not from original, first-hand sources. Such sources may contain the biases or perspectives of their authors, and the researcher does not always have full control over how the data were collected or organized according to their original purpose. The results of this study indicate that the existence of God’s attributes is clearly explained by Abduh: the attributes that must be believed by the faithful are derived from the guidance of reason and the information provided by Islamic law. Regarding the classification of God’s attributes, there are 20 attributes that are obligatory for God, 20 that are impossible for God, and attributes that are jaiz (possible) for God. Summarizing the attributes of God mentioned in Surah Al-Qashash verses 68–70: God is the Creator, God is free to choose, God is Most Holy, God is All-Knowing, God is One, God is worthy of praise, God is Most Wise, and to God all things will return. One of the characteristics of a believer is to affirm and have certainty in the existence of God while distancing themselves from ideologies that negate or oppose God.

The development of small and medium enterprises (SMEs) and household industries is often regarded as the economic foundation of a region. However, much of the existing research in Indonesia remains focused on quantitative descriptive analysis, while providing limited attention to spatial dynamics and interregional disparities. This study aims to critically evaluate the spatial distribution patterns of SMEs and household industries at the regency and city levels, with particular emphasis on clustering tendencies, unequal distribution, and their relationships with regional characteristics. A spatial analysis approach is employed to identify spatial autocorrelation and industrial clustering patterns, which is complemented by a structural analysis of infrastructure availability, market accessibility, and regional institutional capacity. The findings indicate that the distribution of SMEs and household industries is not geographically random, but rather forms clusters that are predominantly concentrated in areas with higher levels of accessibility and economic activity. This condition reflects spatial inequality that may exacerbate development disparities between regencies and cities. Furthermore, the results reveal that uniform industrial development policies are insufficient to accommodate the diverse spatial characteristics across regions. Therefore, this study underscores the importance of formulating spatially informed and context-sensitive policies for the development of SMEs and household industries in order to promote more balanced and sustainable regional industrial development.

This study aims to analyze the comparison of small business development strategies in improving the income of traders in traditional markets around Medan City. The research employed a qualitative descriptive approach using interviews, observations, and documentation involving three traders of fruit, vegetables, and tomatoes. The results show that each trader applies different strategies depending on the characteristics of their commodities and business capacity. Fruit and tomato traders tend to implement quality sorting, price adjustments, and trust-building through honest customer service. Meanwhile, the vegetable trader relies more on traditional approaches such as giving bonuses to customers. Market facilities significantly influence the effectiveness of business strategies, where traders with proper stalls are better able to maintain income stability compared to those using temporary tents in muddy and uncomfortable areas. Seasonal factors also strongly affect income fluctuation as they determine the quality and supply of commodities. This study concludes that small business development strategies in traditional markets are shaped not only by traders’ managerial abilities but also by market infrastructure conditions and external environmental factors. It is recommended that market managers improve market facilities to support the sustainability of small traders’ businesses.