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This study investigates the development of Indonesian language literacy among third-grade elementary school students. The primary problems identified were acute classroom passivity and low reading comprehension, evidenced by an initial learning mastery of only 45.16%, which were largely driven by conventional teacher-centered pedagogy. The objective of this research is to enhance early-grade reading literacy and active participation through an innovative instructional intervention. The proposed method employed a two-cycle Classroom Action Research (CAR) design at SDN Jrebeng Kulon 1, integrating the Problem-Based Learning (PBL) model assisted by serial picture media. This approach utilizes chronological visual scaffolding to facilitate narrative comprehension for students in the concrete operational stage. The results demonstrated a highly significant academic progression: classical learning mastery increased to 70.97% (mean score: 80.80) in Cycle I and culminated in an absolute 100% mastery rate (mean score: 94.51) by the end of Cycle II. The synthesis of these findings reveals that transitioning from static visual aids to serial visual stimuli within a problem-oriented framework effectively mitigates cognitive dissonance and eradicates classroom passivity. In conclusion, the integration of the PBL model with serial picture media serves as a comprehensive pedagogical solution that not only maximizes cognitive reading comprehension but also reconstructs students' verbal articulation and social-collaborative skills, offering a highly scalable strategy for early primary education.

Postharvest handling of leafy vegetables, such as kale (Brassica oleracea var. acephala), is crucial for maintaining product quality and market value. This study aims to review the application of sorting and grading processes in improving the quality of horticultural products. The method used is a literature review of scientific journals and relevant references related to postharvest handling of vegetables. The findings indicate that sorting functions to separate damaged and unmarketable products based on physical condition, while grading classifies products into quality categories based on size, shape, and visual appearance. These processes contribute to reducing postharvest losses, improving product uniformity, and increasing market value. Furthermore, proper sorting and grading support quality control and enhance efficiency in distribution and marketing systems. Therefore, sorting and grading are essential components of postharvest management to ensure product quality and market competitiveness.

This study aims to examine the development of teachers’ multicultural competence at SMA Ta'miriyah Surabaya. The study employed a qualitative approach using a case study design. Data were collected through interviews, observations, and documentation involving the principal and teachers as research informants. Data analysis applied the interactive model of Miles, Huberman, and Saldana, which consisted of data condensation, data display, and conclusion drawing. The findings revealed that the development of teachers’ multicultural competence at SMA Ta'miriyah was carried out through three main pathways. First, training programs that implicitly integrated multicultural values into general educational programs, such as the teacher mobilization program. Second, organizational development grounded in Islamic values as the foundation for fostering tolerance and respect for diversity, implemented through curriculum integration, school regulations, teacher role modeling, and collaboration among teachers. Third, career development, which remained primarily focused on academic aspects and had not explicitly incorporated indicators of multicultural competence. Teachers’ multicultural competence at SMA Ta'miriyah was reflected in three main dimensions. In terms of knowledge, teachers acquired understanding through teaching experience, formal education, and training. In terms of attitudes, teachers demonstrated tolerant and inclusive behavior when interacting with students from diverse cultural and ethnic backgrounds. In terms of skills, teachers were able to manage classroom diversity harmoniously through adaptive and responsive pedagogical approaches that accommodated students’ differences.

Indonesia’s multicultural diversity poses serious challenges in maintaining social harmony, marked by increasing cases of intolerance driven by differences in ethnicity, religion, and culture. Education, particularly civics (PPKn), plays a strategic role in instilling tolerance values from an early age. This study aims to analyze the strategies used by elementary school teachers in cultivating tolerance attitudes through PPKn  learning, identify the implementation of tolerance values in classroom activities and student social interactions, and reveal the challenges faced by teachers along with effective and contextual learning strategies. This study employed a qualitative descriptive approach based on library research, collecting data from accredited scientific journals published between 2021 and 2025, sourced from databases including google scholar, DOAJ, and sinta- indexed journals. Data were analyzed using descriptive qualitative analysis through stages of data reduction, data presentation, and conclusion drawing, with source triangulation appliet to ensure validity. The findings reveal that affective teachers employ a holistic approach encompassing cognitive, affective, and psychomotor domains, consistent with Bloom’s revised taxonomy. Teachers serve not only as knowledge transmitters but also as moral role models, as supported by Bandura’s Social Learning Theory. Implementation is carried out through three channels: intracurricular, co-curricular, and extracurricular activities. Key challenges include dominance of cognitive approaches, limited understanding of multicultural education, and negative influencesof social media. This study implies the urgent need for continuous professional training for PPKn teachers, differentiated and inclusive learning design, and active collaboration between schools and families to strengthen the internalization of tolerance values in elementary school students.

This study examines the regulation of copyright royalties as joint property in Indonesia, the United States, and Europe, aiming to provide recommendations for better legal implementation. The research employs a normative juridical method with a comparative legal approach, utilizing library research and qualitative analysis of primary and secondary legal materials. The findings indicate that Indonesia, the United States, and Europe share common ground in recognizing royalties derived from copyright as joint property when such economic benefits are obtained during marriage. However, significant differences exist in their approaches. European countries, particularly Spain and the Republic of Moldova, clearly distinguish between exclusive rights and economic rights, where copyright remains the creator's personal property while royalties are classified as joint property. The United States demonstrates considerable flexibility through state-level regulations, adopting either community property systems or equitable distribution systems. Indonesia, through Decision No. 1622/PDT.G/2023/PA.JB, has begun recognizing royalties as joint property. Nevertheless, Indonesia still requires clearer and more comprehensive regulations to ensure legal certainty regarding the status of royalties as joint property and their distribution following divorce. This study contributes to developing legal frameworks that balance protecting creators' personal rights with the principle of fairness in family law.

Antimicrobial resistance is a persistent threat to hospital care, particularly when empirical therapy relies on broad-spectrum antibiotics without continuous evaluation of local use and susceptibility patterns. This literature review aimed to synthesize evidence from four Indonesian hospital-based studies regarding antibiotic utilization, rationality assessment, and bacterial resistance profiles. A structured narrative review was conducted using four selected articles provided by the author. Data were extracted for study design, setting, population, antibiotic evaluation method, dominant antibiotic classes, rationality indicators, resistance profile, and stewardship implications. The four studies were descriptive and hospital-based, using retrospective records, concurrent observation, ATC/DDD with DU 90%, qualitative rationality criteria, and antibiogram data. The synthesis showed a consistent concentration of antibiotic use in broad-spectrum groups, especially third-generation cephalosporins, penicillins, quinolones, and macrolides. In a Bandung public hospital, total antibiotic consumption reached 95,719.01 DDD, with penicillins, cephalosporins, quinolones, macrolides, and sulfonamides included in the DU 90% segment. In intensive care, ceftriaxone was the most frequently used antibiotic and most rationality indicators were appropriate, although clinically significant drug interactions were still identified. In pediatric acute respiratory infection inpatients, cefotaxime and ceftriaxone dominated empirical therapy. Resistance mapping in Denpasar highlighted relevant Gram-positive and Gram-negative pathogens and recommended antibiotics according to susceptibility levels. Overall, the reviewed evidence supports an integrated antimicrobial stewardship model combining ATC/DDD-DU 90% surveillance, qualitative rationality evaluation, antibiogram-based empirical guidance, and periodic feedback to prescribers.

This study aims to analyze strategies for increasing reading interest in developing a literate generation among elementary school students and to identify factors influencing literacy culture within school and family environments. The phenomenon of low reading interest among elementary school students has become a significant concern because the rapid development of digital technology tends to shift students’ attention from reading activities to audiovisual entertainment. This research employed a descriptive qualitative approach using a case study method at SDN 1 Notoharjo. Data were collected through semi-structured interviews, observations, and documentation studies. The participants consisted of one classroom teacher and four third-grade students of SDN 1 Notoharjo. Data analysis was conducted through data reduction, data display, and conclusion drawing, while data validity was tested using source triangulation and method triangulation. The findings revealed that literacy strengthening was implemented through morning reading activities, book discussions, the use of visual and digital media such as e-books, and the utilization of reading corners and school libraries. The study also found that students’ reading interest was influenced by the availability of reading materials, the comfort of literacy environments, family support, and the dominant use of gadgets for entertainment purposes. This study contributes scientifically by strengthening the concept of collaborative literacy involving schools, families, and digital technology in developing reading culture among elementary school students. The findings imply the need for more integrated, adaptive, and sustainable literacy programs to improve students’ reading interest in the digital era.  

This study examines teachers’ strategies in instilling citizenship values among students to develop democratic character and social responsibility. The background of this study is rooted in the increasing need to strengthen students’ civic awareness and character in the face of rapid social and technological changes. The objective of this research is to identify and analyze effective strategies used by teachers in integrating citizenship values into the learning process. This study employs a library research method by analyzing various relevant academic sources, including journals, books, and scholarly articles related to citizenship education and character development. The findings indicate that effective strategies include student-centered learning approaches, the integration of character education into civic education, the use of inquiry-based learning methods, and the utilization of technology to enhance student engagement. Additionally, extracurricular activities and a democratic classroom environment play a significant role in reinforcing students’ understanding and application of citizenship values. These strategies contribute to the development of students’ critical thinking, participation skills, and social awareness. The implications of this study suggest that teachers need to continuously innovate and adapt their teaching strategies to meet the evolving needs of students and societal demands. Furthermore, educational institutions should provide adequate support and resources to optimize the implementation of citizenship education in fostering responsible and democratic citizens.

This study aims to analyze a comparison of coal stockpile volumes using the DJI Mavic 3 Pro Unmanned Aerial Vehicle (UAV) method versus the truck count method at PT. Mitra Barito. Data collection was conducted through aerial photography using a UAV at altitudes of 60 meters and 70 meters, as well as Ground Control Point (GCP) measurements using GPS. The aerial imagery data was processed using photogrammetry software to generate orthophotos and a Digital Elevation Model (DEM), followed by a geometric accuracy test based on the Geospatial Information Agency Regulation No. 6 of 2018, using the Circular Error 90% (CE90) and Linear Error 90% (LE90) parameters. The research results show that high-quality processing at an altitude of 60 meters yields a CE90 value of 2.1619 meters and an LE90 value of 4.3656 meters, thereby meeting the accuracy standards for RBI maps at a scale of 1:5,000, Class 3 for horizontal accuracy, and a scale of 1:10,000, Class 3 for vertical accuracy. Volume calculations of the stockpile using UAVs yielded a result of 22,750.900 m³, while the truck count method produced a volume of 23,503.300 m³. The volume difference between the two methods was 753.400 m³, with a deviation percentage of 3.2%. Based on the research results, the UAV method is considered capable of providing relatively accurate calculations of coal stockpile volume.

This study was conducted based on the low level of student activeness and understanding in IPAS learning at the elementary school level. The objective of this research was to determine the effectiveness of the role play method in improving students’ activeness and learning outcomes. This study employed a classroom action research approach involving 27 students. The data were collected through observation and evaluation of student learning outcomes. The findings revealed a significant improvement in both student participation and understanding after the implementation of the role play method. Students became more active in asking questions, responding to the teacher, and participating in classroom activities. In terms of learning outcomes, 85.2% of students were able to answer questions correctly and demonstrated good understanding, while the remaining 14.8% also achieved the minimum criteria. These results indicate that learning through direct experience enhances students’ comprehension. The implication of this study suggests that the role play method can be used as an effective alternative teaching strategy to create an interactive, engaging, and student-centered learning environment in elementary education.

Inclusive education aims to provide equal learning opportunities for all students, including those with special needs, within regular educational settings. However, mathematics learning in inclusive classrooms remains challenging because mathematical concepts are often abstract and require logical reasoning that may not be easily accessible to learners with diverse cognitive characteristics. Ethnomathematics has emerged as an alternative approach by integrating cultural practices, local wisdom, and students’ daily experiences into mathematics instruction, creating more meaningful and accessible learning environments. This study aims to analyze the development, implementation patterns, opportunities, and research gaps related to ethnomathematics in inclusive mathematics learning. A literature review method was employed by examining scientific publications from 2020–2025 obtained from Google Scholar, Scopus, ERIC, Springer, and ProQuest databases. Data were analyzed through content analysis involving reduction, classification, interpretation, and synthesis. The findings indicate that ethnomathematics has been implemented through cultural artifacts, digital teaching materials, and project-based contextual learning. The approach supports inclusive learning through multi-representational access, instructional adaptations, scaffolding strategies, and collaborative teaching practices aligned with Universal Design for Learning principles. Furthermore, ethnomathematics enhances students’ motivation, conceptual understanding, mathematical literacy, and cultural identity. Nevertheless, studies focusing on disability-specific adaptations and long-term learning outcomes remain limited and require further investigation.

This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.

Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.

This study discusses a student concentration detection system using Convolutional Neural Network (CNN) with the MobileNetV2 architecture. The dataset was adapted from Classroom Student Behaviors and mapped into four concentration categories: highly focused, focused, less focused, and unfocused. The system was tested with a 720p webcam and produced real-time detection data. The evaluation results show an overall accuracy of 75.85%, with the highest precision achieved in the focused class (0.9859) and the highest recall in the highly focused (0.9739) and unfocused (0.9811) classes. The confusion matrix indicates that the focused class was detected most consistently, while highly focused and unfocused classes were often misclassified as focused, resulting in lower precision. In real-time testing, the system operated at an average of 7 FPS and worked optimally when students faced the camera directly with sufficient lighting, but its performance decreased significantly at face angles greater than 45°. User evaluation shows that 75% of students rated the detection results as accurate/very accurate with an average satisfaction score of 3.6 out of 5, and 75% felt assisted in recognizing their concentration level. From the teachers’ perspective, most stated that the results were consistent with classroom observations, and all expressed willingness to reuse the system.

Bullying is a serious problem that can disrupt the learning process and mental development of students, including in Islamic boarding schools. Early detection of bullying is essential to creating a safe and conducive learning environment. This study aims to apply the You Only Look Once (YOLO) algorithm to automatically detect bullying through video recordings in the environment of the SMK Skill Village Islamic School Business Boarding School. The method used involves collecting a video dataset representing various types of bullying behavior, labeling the data, and training an object detection model using the YOLOv5 algorithm. The developed system is capable of detecting and classifying bullying behavior in real- time with detection accuracy reaching [accuracy value if known]. The implementation of this system is expected to assist school authorities and boarding school administrators in monitoring, preventing, and addressing bullying incidents more quickly and effectively, while also serving as an initial step in leveraging artificial intelligence technology to create a safer and more comfortable educational environment.

The development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in

Social media X (Twitter) has become the main platform for the Indonesian public to express opinions, including on the trend of 'kabur aja dulu' (let's just run away for a bit). This research aims to classify the sentiments of the public using the Naïve Bayes and Support Vector Machine (SVM) methods, and to compare the accuracy of both in sentiment analysis. Data was collected via the Twitter API with the hashtag #kaburajadulu, resulting in 2,067 tweets, which, after the cleansing process and manual labeling, left 385 data points. The analysis process followed the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Model evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The classification results show that 82% of tweets have a positive sentiment and 18% negative. The Naïve Bayes algorithm achieved an accuracy of 86.49%, slightly lower than SVM, which reached 88.05%. In conclusion, Support Vector Machine is more effective in sentiment classification on public opinion data. This research contributes to the digital mapping of public opinion and recommends the development of automatic labeling methods as well as the exploration of advanced algorithms in the future.

QRIS (Quick Response Code Indonesian Standard) has become a widely used national digital payment standard. User satisfaction with this service needs to be monitored continuously to ensure its sustainability. This study aims to predict the level of QRIS user satisfaction based on their experiences and perceptions expressed organically on the Twitter social media platform. The method used is sentiment analysis with the Naive Bayes classification algorithm implemented using RapidMiner software. The research data was obtained from Twitter user comments collected through web scraping techniques. The text data then went through a preprocessing stage that included cleansing, stopword filtering, stemming, and tokenizing to be prepared as features ready to be processed by the model. The data was divided into training (80%) and testing (20%) subsets for model training and validation. The results showed that the Naive Bayes model was able to predict user satisfaction sentiment with an accuracy of 80.99%. These findings indicate that the model is highly accurate in identifying satisfied comments and sufficiently sensitive in detecting dissatisfaction. This study concludes that sentiment analysis of Twitter UGC data using Naive Bayes is an effective and efficient approach for predicting QRIS user satisfaction in real time. The practical implication of this study is to provide an automatic feedback system for service providers to monitor public sentiment and take targeted corrective actions.

The growth of information technology has driven changes in consumer behavior, one of which is through e-commerce platforms such as Shopee. This phenomenon has generated a large number of customer reviews, including those for local cosmetic products such as Wardah. These reviews serve as an important source of information for understanding customer perceptions and satisfaction levels. However, manual analysis of large and linguistically diverse datasets is inefficient and potentially subjective. This study aims to implement the multi-category Naive Bayes algorithm to classify the sentiment of Wardah product reviews on Shopee into three categories: positive, negative, and neutral. The data were collected using a web scraping technique and processed through a series of preprocessing stages including case folding, tokenization, stopword removal, stemming, and text cleaning. Subsequently, term weighting was performed using the TF-IDF method prior to classification. Model performance was evaluated using a confusion matrix as well as accuracy, precision, and recall metrics. The results indicate that the multi-category Naive Bayes algorithm achieved an accuracy of 86.00%, a precision of 86.63%, and a recall of 98.24%. This approach can assist business practitioners in objectively understanding customer opinions and support decision-making in business strategy and product development.

The Merdeka Curriculum, as a new educational policy in Indonesia, emphasizes the cultivation of the Pancasila Student Profile, which is rich in citizenship values. This study aims to analyze the application of citizenship values in Pancasila and Civic Education (PPKn) learning within the framework of the Merdeka Curriculum at the high school level. This research employs a qualitative descriptive method, collecting data through classroom observations, semi-structured interviews with PPKn teachers, and analysis of teaching modules and project-based learning documents (P5). The findings indicate that the application of citizenship values is primarily integrated through Project-Based Learning to Strengthen the Pancasila Student Profile (P5). Values such as mutual cooperation (gotong royong), critical reasoning (bernalar kritis), and global diversity (berkebinekaan global) are the most frequently emphasized. However, teachers face challenges, including the need to adapt pedagogical approaches, difficulties in assessing value-based outcomes authentically, and a lack of specific reference materials aligned with the new curriculum. The implication of this research is the urgent need for structured professional development for teachers and the development of comprehensive teaching guides to ensure the effective and standardized implementation of citizenship values in PPKn learning across all educational units.