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The enactment of Indonesia's Law Number 20 of 2025 on the Code of Criminal Procedure (KUHAP 2025), effective January 2, 2026, introduces a paradigmatic shift in the coordination between investigators and public prosecutors: Article 58 mandates active coordination from the investigation stage, fundamentally departing from the sequential-passive model of the former KUHAP, while Article 70 imposes a strict seven-day deadline for indictment drafting after case files are declared complete. This study examines two interconnected questions: (1) how the legal framework governing investigator–prosecutor coordination is structured under KUHAP 2025 and related legislation; and (2) how that framework is implemented in practice at the Purwakarta District Prosecutor's Office. A normative–empirical mixed-method design was employed, integrating statutory, conceptual, and case-study approaches. Data were gathered through in-depth interviews with prosecutors and investigators at Purwakarta District Prosecutor's Office and Purwakarta Police Resort, case document analysis, and field observation. The theoretical framework combines Lawrence M. Friedman's Legal System Theory and Soerjono Soekanto's Law Enforcement Theory. Findings reveal that KUHAP 2025 delivers substantial normative advancement yet harbours three critical regulatory gaps: the absence of binding technical protocols for implementing mandatory active coordination, the lack of uniform and measurable case-file completeness standards, and no formal mechanism for resolving institutional disagreements on legal interpretation. On the ground, coordination at Purwakarta still operates under the old sequential-passive pattern despite the new law: case-file returns (P-19) remain frequent, driven primarily by absent expert testimony, insufficient factual narration in examination records, and mismatches between charged articles and legal facts. A Friedman–Soekanto diagnostic reveals simultaneous dysfunction across all three legal system components substance, structure, and legal culture with the entrenched 'waiting culture' between the police and the prosecution identified as the most resistant obstacle to reform.

The development of digital technology has brought about significant changes in various aspects of life, including early childhood faith education. Today’s children are growing up in an environment familiar with digital media, making it necessary to adopt a catechetical approach that keeps pace with the times. This article aims to examine the use of digital catechesis as a tool for early childhood faith education. This study employs a qualitative method with a literature review approach, gathering data from books, scientific journals, research articles, Church documents, and other relevant sources. The data is analyzed using descriptive-qualitative methods to understand the benefits, challenges, and role of digital catechesis in fostering children’s faith. Research findings indicate that digital catechesis can serve as an effective, engaging, and interactive medium for helping children learn about the teachings of the faith from an early age. The use of animated videos, religious songs, educational images, and learning apps can enhance children’s interest in learning, attention, and understanding of Catholic faith values. Furthermore, digital catechesis also assists the Church, families, and schools in providing faith education that is more contextual and aligned with the world of today’s children. However, the use of digital media still requires the guidance of parents, teachers, and faith mentors so that children receive proper direction and are protected from the negative impacts of technology. Thus, digital catechesis is a relevant tool in the faith education of young children when used wisely and purposefully. The presence of digital media does not replace the role of faith educators but serves as a tool that enriches the process of proclaiming the faith in the modern era.

This study aims to develop a financial saving application to improve the saving habits of students, particularly in Islamic boarding schools, through an adaptive challenge approach. The system integrates a mobile iOS application with a backend service and Large Language Model (LLM) processing via Ollama. Transaction data entered by users is processed by the backend to generate contextual and personalized saving challenges, applying Reinforcement Learning concepts in an adaptive and data-driven manner. The research adopts a descriptive quantitative method using surveys and system testing with 50 respondents. Results indicate that the application functions as designed, with no significant bugs detected. User evaluation shows high satisfaction, with an average score of 4.3 out of 5, covering ease of use, interface design, and increased awareness of saving. The combination of gamification, reward systems, and adaptive personalization successfully motivates users to save regularly. This system demonstrates the potential of integrating AI-driven personalization to strengthen financial literacy and healthy financial habits among students in a fun and interactive way.methods, and a summary of the results. The abstract should end with a comment about the significance of the results or conclusions brief.

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.

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.

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.

This study was motivated by the lack of standardized and objective assessment instruments for the teaching of the Syofyani Minang Payung Dance at the junior high school level, resulting in an assessment process that remains largely subjective and fails to measure psychomotor, affective, and cultural aspects in a balanced manner. This study aims to design and test the validity and reliability of a performance assessment rubric for the Syofyani Minang Payung Dance in cultural arts education at SMP Negeri 1 Bukittinggi. The research method used is a mixed-methods approach with a sequential explanatory design. The research subjects consisted of three dance instructors serving as expert judges, one cultural arts teacher, and 33 junior high school students. The research instrument was a performance-based assessment rubric covering five competency indicators: basic movement techniques; alignment with musical rhythm and dynamics; expression and character interpretation; mastery of payung props and movement safety; and accuracy of floor patterns and group synchronization. Quantitative data analysis was conducted using IBM SPSS Statistics through the Corrected Item-Total Correlation validity test and Cronbach’s Alpha reliability test, while qualitative data was analyzed using descriptive-interpretive methods. The research results show that all indicators have validity scores above 0.30 and are therefore considered valid, and the Cronbach’s Alpha reliability score is above 0.70, indicating good internal consistency of the instrument. Furthermore, the interview results indicate that the rubric is considered relevant, clear, and aligned with the learning characteristics of Syofyani’s Minang Payung Dance at the junior high school level. Consequently, the developed assessment rubric is deemed suitable for use as an objective, standardized, and contextually appropriate assessment instrument for dance education rooted in local culture.

The era of globalization presents challenges of moral degradation among students, including intolerance, individualism, and declining nationalism. These issues make character strengthening through Civic Education (PKn) a national priority. This study aims to analyze the effectiveness of PKn in shaping students’ character, particularly the values of religiosity, nationalism, independence, mutual cooperation, and integrity. The research employed a qualitative Library research design using secondary data from 18 scientific journals and 2 books published between 2021 and 2026. Data were collected through literature review and analyzed using content and thematic analysis to identify patterns of PKn implementation and its impact. The findings indicate that PKn effectively develops Good citizenship through character education based on Pancasila. Effective strategies include teacher role modeling, Problem-Based Learning (PBL), and digital media such as infographics and gamification. These approaches were reported to improve empathy, national loyalty, and integrity among students. The Merdeka Curriculum further supports character development by emphasizing affective learning through authentic projects. Challenges arising from foreign cultural influences can be addressed through collaboration among schools, families, and communities. The study concludes that PKn plays a significant role in strengthening students’ character and supporting the realization of Golden Indonesia 2045 through sustainable character 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 research is motivated by the problems in manual attendance systems at schools, which remain vulnerable to fraud, time-consuming, and inefficient. The expected solution is to develop an automated attendance system based on face recognition that can operate in realtime with high accuracy. The research object is vocational high school students, with the applied method implementing the YOLO v10 algorithm for face detection, followed by the face_recognition library for identification. The instruments used include an Imou CCTV camera as the input device, a mid-range laptop as the hardware platform, and Python with SQLite as the software environment for data processing and attendance storage. The results show that the developed system achieved an average face detection accuracy of 96% under normal lighting and 91% under low lighting, with an average processing speed of 27 FPS. The implementation of an anti-duplication feature also ensured data validity by allowing each student to be recorded only once per day. In conclusion, the use of YOLO v10 in face-based attendance proved to be effective, efficient, and capable of reducing fraud. The implication of this study is that the system can be applied in both Islamic boarding schools and general schools as a modernization of attendance systems, with a recommendation for further development through web-based application and cloud database integration.

This research was designed to explore the extent to which public beliefs influence the implementation of Digital traceability  systems in the halal industrial sector. The approach used was quantitative with a survey method, where questionnaires were distributed to 60 respondents who were consumers of halal products in Indonesia. Data were analyzed using simple linear regression via Microsoft Excel. Research findings indicate that public confidence has a positive and significant influence on the adoption of Digital traceability  systems, with a regression coefficient of 0.476 and a significance level of 0.000 (<0.05). In addition, the coefficient of determination (R Square) value of 0.219 indicates that public confidence contributes 21.9% to the implementation of the Digital traceability  system, while the rest is determined by other factors that were not researched. These findings confirm that public trust is an important element in encouraging acceptance of digital technology, especially in the halal industry which relies heavily on transparency and consumer confidence. Thus, implementing a Digital traceability  system that is supported by information openness and easy access to technology can be an effective strategy to strengthen consumer trust while expanding technology adoption.

This study aims to analyze the process of internalizing legal values in the formation of a culture of law-abidingness by emphasizing the perspective of civic responsibility and the Islamic Sociological Jurisprudence Theory approach. The main problem studied is how legal values are not only understood normatively, but also internalized in the individual and collective consciousness of society, thus giving rise to sustainable law-abiding behavior. The research method used is a normative juridical approach with strengthening conceptual and sociological analysis of legal dynamics in society. The results show that the internalization of legal values is a multidimensional process involving cognitive, affective, and spiritual aspects, which are influenced by education, the social environment, role models, and the consistency of law enforcement. In the context of civic responsibility, a culture of law-abidingness is not only formal compliance with regulations, but also reflects moral awareness and active participation of citizens in maintaining social order. Meanwhile, Islamic Sociological Jurisprudence Theory offers an integrative paradigm that combines the normative dimensions of sharia with social reality, through a comparative approach of schools of thought, maqāṣid al-syarī‘ah, and an orientation toward the welfare of the people. This research emphasizes that the formation of an effective culture of law-abidingness requires a holistic and contextual approach, in which law is understood as a living and adaptive social instrument. Thus, the internalization of legal values based on civic responsibility and an Islamic sociological jurisprudence approach can encourage the realization of substantive justice, high legal awareness, and a civilized and sustainable social order.    

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.

Postpartum depression is a postpartum mental health disorder that significantly impacts maternal well-being, infant development, and family functioning. The high prevalence of postpartum depression in Indonesia is due to limited access to health services, low mental health literacy, and social stigma in the community. This indicates a significant gap between the need for maternal mental health services and the availability of existing interventions, making education a crucial component in efforts to prevent postpartum depression early. This study aims to analyze the prevention of postpartum depression in postpartum mothers through telenursing-based education and screening using the Edinburgh Postnatal Depression Scale (EPDS) in the community. A descriptive case study design was used, involving one respondent, a 25-year-old primigravida mother residing in the Bantul area. The intervention was implemented online via WhatsApp and video calls, including structured health education on postpartum psychological changes, adaptive coping strategies, and the importance of social support. The intervention also included daily remote monitoring of the respondent's condition via the WhatsApp mobile application. The EPDS was administered as a pre-test and post-test to evaluate changes in the respondent's psychological condition. The findings showed a significant decrease in the EPDS score from 16 (moderate depression) to 6 (minimal depression), indicating significant psychological improvement. These results imply that integrating EPDS screening, structured health education, and daily monitoring is an effective and accessible community-based approach to preventing postpartum depression, particularly for mothers with limited mobility and access to health services.

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 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.

The field of education, especially Islamic Religious Education (PAI), has seen substantial changes as a result of the advancement of digital technology and artificial intelligence (AI). The existence of digital media and AI offers numerous opportunities to improve the effectiveness, efficiency, and quality of education through interactive, flexible, and easily accessible learning resources. The purpose of this article is to analyze how artificial intelligence (AI) and digital media are used in Islamic Religious Education. This includes the idea of AI and digital media, AI-based learning materials, the use of AI in educational activities, ethical considerations of AI use, the effects and difficulties of integrating AI, and the Islamic viewpoint on technological advancement. This study employed a library research method by collecting data from relevant journals, books, and other scholarly sources. The findings indicate that the use of AI and digital media in PAI learning can enhance access to information, personalize learning experiences, improve evaluation effectiveness, and increase student engagement in the learning process. However, the implementation of AI also presents several challenges, such as limited digital literacy among teachers, inadequate facilities and infrastructure, ethical concerns, and the potential decline of direct educational interaction between teachers and students. Therefore, the utilization of AI in Islamic Religious Education should be carried out wisely while maintaining Islamic values, ethics, and the primary goal of Islamic education in shaping students’ character.

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.

Introduction: Ramadan fasting is a religious practice observed by Muslims by abstaining from food and drink from dawn until sunset. These changes may increase the need for oral and dental healthcare. However, some Muslims still perceive that undergoing dental treatment while fasting may invalidate the fast, leading many patients to delay or avoid dental visits during Ramadan. Objective: This study aims to review and analyse various scientific literature regarding the safety and permissibility of dental procedures for patients who are observing fasting during Ramadan. Methods: This study used a narrative review method by searching scientific articles through PubMed, ScienceDirect, and Google Scholar databases using keywords relevant to the research topic. Articles were selected based on predetermined inclusion and exclusion criteria. Results: The review findings indicate that most dental procedures, such as local anaesthesia administration, scaling, restorative treatment, and tooth extraction, generally do not invalidate fasting as long as no material or fluid is intentionally swallowed. The use of suction devices, isolation of the treatment area, and proper scheduling of dental procedures are important factors in minimizing the risk of swallowing fluids during treatment. Conclusion: Most dental procedures can be safely performed on fasting patients while still considering both medical aspects and Islamic principles. Patient education regarding the safety of dental procedures during fasting should be improved to prevent delays in treatment and complications in oral health.