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Distributed software systems face significant challenges related to data quality due to their complex, decentralized architecture. These systems often involve multiple nodes responsible for processing and storing data, making it difficult to maintain consistency and ensure accurate data across the entire network. In particular, issues like data inconsistency, latency, and data fragmentation are prevalent in distributed environments. To address these challenges, this study proposes an integrated data quality governance strategy that combines real time monitoring and automated anomaly detection using machine learning models. The proposed strategy aims to improve data consistency, enhance anomaly detection capabilities, and reduce the need for manual intervention, ultimately improving overall data governance in distributed systems. Real time monitoring ensures immediate identification of data issues as they occur, while machine learning models, such as autoencoders and Isolation Forests, automate the detection of anomalies based on high reconstruction errors and data isolation techniques. The study evaluates the proposed strategy through real-world distributed system scenarios, comparing its effectiveness to traditional approaches like periodic audits and manual validation. Results demonstrate that the integrated approach leads to faster anomaly detection, reduced data inconsistencies, and improved overall system performance. The use of advanced machine learning techniques and real time analytics significantly enhances the system's ability to maintain high data quality standards across multiple distributed nodes. This strategy has wide-ranging implications for industries that rely on distributed systems, such as finance, healthcare, and IoT, where data integrity is essential for operational success. Future research can focus on integrating more advanced machine learning techniques and optimizing the real time monitoring framework to handle larger and more complex systems.

The increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.

User reviews on the Shopee e-commerce platform represent an important source of information for understanding consumer perceptions of products and services. Sentiment analysis is commonly applied to classify user opinions into positive, neutral, and negative sentiment categories based on textual data. This study aims to analyze the performance of the Long Short-Term Memory (LSTM) method in sentiment classification of Shopee user reviews. The dataset used in this study consists of Indonesian-language user reviews that have undergone preprocessing stages, including case folding, text cleaning, tokenization, and stopword removal. The LSTM model was trained using preprocessed text represented as word sequences. Model performance was evaluated using overall accuracy and class-wise classification results. The experimental results indicate that the LSTM method achieved an overall accuracy of 87.62%. In addition, the classification performance for the positive sentiment class reached 95.27%, the neutral class achieved 4.96%, and the negative class reached 74.26%. These results demonstrate that the LSTM method performs well in classifying sentiment in Shopee user reviews, particularly for positive sentiment. This study is expected to provide insights and references for the application of deep learning methods in sentiment analysis of Indonesian e-commerce review data.

This study was based on the low ability of third-grade students at SD Negeri 2 Wonorejo to understand syntactic material, particularly the use of conjunctions, due to a lack of interactive and contextual learning media. This study aimed to develop EKUJARSI E-Flipbook learning media based on Kudus local wisdom that has been tested for feasibility and practicality to improve student understanding. The research method used was Research and Development (R&D) with the ADDIE (Analyze, Design, Development, Implementation, Evaluation) development model. The test subjects included subject matter experts, media experts, classroom teachers, and third-grade students. Data collection instruments used validation sheets and user response questionnaires. The results showed that the EKUJARSI media was highly valid for use, as evidenced by a validation percentage of 89.6% from media experts and 86.15% from subject matter experts. The practicality test also showed positive responses, with teachers giving a score of 80% and students giving a score of 85.9%, which is considered very feasible. Specific findings show that the ease of use aspect received a perfect score of 100% from students, indicating that this media is very user-friendly. It is concluded that the EKUJARSI E-Flipbook is feasible and practical to be implemented as an innovative learning media that effectively integrates local cuisine to facilitate understanding of language concepts.

The increasing adoption of cloud native microservices has brought about significant improvements in scalability, flexibility, and resilience. However, these advancements also introduce substantial security challenges, particularly in distributed environments where traditional perimeter-based security models prove inadequate. This paper proposes a secure architecture for cloud native microservices that integrates Zero trust Network Access (ZTNA) and multi layered encryption techniques to address these security concerns. The architecture operates on the principle of "never trust, always verify," ensuring that access to resources is strictly controlled and continuously monitored. By incorporating multi layered encryption methods such as RSA and AES, the architecture ensures data protection both in transit and at rest, significantly reducing the risk of data breaches and unauthorized access. Through experimental evaluations, the proposed architecture demonstrated its effectiveness in preventing lateral movement, mitigating data leakage, and resisting common attack vectors such as man-in-the-middle (MITM) attacks and privilege escalation. Additionally, the performance of the system remained optimal, with minimal overhead despite the additional security layers. The architecture's scalability and robust security mechanisms make it a viable solution for real-world microservices environments, where both security and performance are crucial. This paper discusses the potential impact of this secure architecture on the broader field of distributed system security and offers recommendations for future work, including the integration of advanced machine learning techniques for real-time threat detection and automated responses, as well as the adaptation of the architecture for emerging technologies like edge computing and 6G networks.

The goal of Christian education is not only to increase students' knowledge but also to shape character that reflects the values ​​of faith in Christ. In the context of a diverse society, an inclusive and tolerant attitude towards differences is crucial for students. This paper aims to illustrate how students can be shaped to develop an inclusive and tolerant attitude based on the values ​​of the Christian faith. Through Christian Religious Education (PAK) learning that emphasizes love, respect for one another, justice, and peace, students are encouraged to recognize that every human being is created in the image and likeness of God. An inclusive and tolerant attitude can develop when students understand that differences are part of God's plan and implement this in their interactions with others. Therefore, Christian education plays a crucial role in instilling the value of Christ's love so that students can live peacefully together, appreciate differences, and become role models in a diverse society.

This study investigates how character education is embedded into the school culture of an Indonesian Islamic junior high school (madrasah), focusing on four core values: religiosity, honesty, tolerance, and discipline. Using a qualitative naturalistic approach, the research was conducted at MTsN 3 Halmahera Utara through in-depth interviews, participant observation, and document analysis involving school leaders, teachers, and students. The findings reveal that character education at the madrasah is not a standalone program but is institutionalized through consistent routines, religious practices, and role modeling. Religiosity is fostered through structured worship, integration of Islamic values into lessons, and spiritual habits. Honesty is promoted via trust-based learning environments, teacher exemplarity, and reinforcement of moral norms. Tolerance is cultivated through inclusive social interactions and respect for religious and cultural differences, while discipline is internalized through habitual rule-following, punctuality, and self-regulation. These findings align with existing theories and regional character education frameworks, confirming that character is best formed through embedded cultural processes and consistent adult exemplarity. The study contributes to the understanding of how Islamic schools can function as moral communities and highlights the importance of alignment between school culture, family support, and wider social contexts in achieving sustainable moral development.

The rapid development of digital technology has brought significant changes to the business landscape, transforming how products are marketed, services are delivered, and business relationships are built. In this context, students, as future members of the workforce and potential entrepreneurs, are required to possess strong digital literacy skills in order to effectively face challenges and seize emerging business opportunities. This study aims to analyze the importance of digital literacy in supporting students’ readiness to respond to future business trends. The research employed a descriptive approach using a literature review and observations of the entrepreneurship learning process in vocational schools. The findings indicate that digital literacy plays a crucial role in enhancing students’ creativity, adaptability, and ability to utilize digital platforms for online marketing, branding, and business communication. Furthermore, digital literacy helps students understand market dynamics, analyze consumer behavior, and adopt innovative business models that align with technological developments. Students with adequate digital literacy are better prepared to face rapid changes in the business environment and demonstrate higher confidence in applying technology to entrepreneurial activities. In conclusion, the integration of digital literacy into entrepreneurship education is essential to produce competitive, innovative, and adaptable graduates who are capable of thriving in the digital era and contributing to sustainable economic development.

This study aims to develop an effective predictive model for identifying students at risk of academic dropout using the Decision Tree and Linear Regression algorithms. The data used are sourced from the public Kaggle dataset Students Dropout and Academic Success, which includes demographic, socioeconomic, and academic performance variables for each semester. The research method includes data preprocessing stages, such as data cleaning, label encoding for categorical variables, numeric feature normalization, and target class adjustment to focus on binary classification, namely Dropout and Graduate. The modeling process is carried out by comparing the performance of the two algorithms using evaluation metrics of accuracy, precision, and recall. The results show that the Decision Tree algorithm has superior performance compared to Linear Regression in mapping non-linear patterns in student data. Feature importance analysis revealed that the number of curricular units in the second semester and tuition payment status are the main predictors of dropout risk. These findings are expected to assist educational institutions in implementing early interventions to improve student academic success.  

This study aims to improve the learning outcomes of fourth-grade students in science through the application of an experimental learning model on the material of changes in the state of matter. The study used a Classroom Action Research (CAR) approach with the Kemmis and McTaggart model which includes the stages of planning, implementation of actions, observation, and reflection. The subjects of the study were 20 fourth-grade students of SDN Daan Mogot 1. The study was conducted in two cycles. Data collection techniques included learning outcome tests, observation of teacher and student activities, and documentation. Data analysis was carried out quantitatively by calculating the average value and percentage of learning completion, and qualitatively through descriptive analysis of the observation results. The results showed that the application of the experimental method was able to improve the quality of the learning process and student learning outcomes. Teacher activity increased from the sufficient category in cycle I to very good in cycle II, while student activity increased from the good category to very good. Increased student activity in observing, discussing, recording results, and drawing conclusions from experiments had a positive impact on understanding the concept of changes in the state of matter. Student learning completion also increased although not all of them reached the classical standard of 80%. Thus, the experimental method is effective in improving the activeness, quality of the learning process, and the science learning outcomes of fourth-grade students. Although material reinforcement and a variety of learning strategies are still needed to optimize learning outcomes.

The low level of interest in learning for elementary school students remains a fundamental problem in the implementation of basic education in Indonesia. One factor suspected of contributing to this condition is the lack of optimal fulfillment of students' physical health and nutritional needs. The Free Nutritional Meal Program (MBG) is strategic government policy aimed at improving students' nutritional status as an effort support learning readiness and the quality of learning in schools. This study aims to analyze the effect of the Free Nutritional Meal Program on increasing interest in learning for elementary school students. The study used a quantitative approach with a quasi-experimental design using a one-group pretest-posttest model. The research subjects included elementary school students and teachers directly involved in the implementation of the MBG program. Data collection techniques were carried out through a learning interest questionnaire, observation of student learning activities, and structured interviews with teachers. The data obtained were analyzed using descriptive statistics and inferential statistics in the form of paired t-tests determine differences in learning interest before and after the program implementation. The results of the study are expected to show an increase in student interest learning after participating in the Free Nutritional Meal Program, which is characterized by increased attention, activeness, and motivation in the learning process. These findings are expected to provide empirical evidence that the Free Nutritional Meal Program not only contributes improving students' physical and health conditions, but also has positive impact on the psychological and academic aspects elementary school students.

The Muhammadiyah Orphanage in Pamekasan faces major challenges in developing life skills and digital education for its children due to limited facilities, teaching staff, and conventional learning methods. To address these issues, an edutainment-based approach and digital pedagogy intervention were implemented to enhance learning quality, motivation, and preparedness for future social and technological challenges. The activities included workshops and training on Digital Pedagogy and Edutainment learning materials, as well as simulations and role-plays using a Game-Based Learning approach, followed by evaluations and participant plan presentations. The program significantly improved the wards’ digital literacy, particularly in personal security (online safety), digital ethics (cyber ethics), gadget usage, and information management, with the average score rising from 2.84 to 4.10 on a 5-point scale, surpassing the target of 75% of participants in the “good” category. Beyond cognitive aspects, the program also boosted motivation, engagement, communication, problem-solving, and independence. Caregiver training was also provided to ensure program sustainability. It is recommended that the orphanage integrate the Game-Based Learning Digital Safety module into its non-formal curriculum, enhance caregiver capacity through advanced training, and improve IT infrastructure.

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

This study aims to develop a student learning progress monitoring system designed to assist teachers and parents in tracking students’ academic performance at SMP Negeri 2 Loura. The main issue identified in the school is that academic information is still distributed using manual procedures, which results in delays and limited transparency regarding students’ learning progress. To address this problem, the system was developed using the Model View Controller (MVC) architecture and the Waterfall approach to system development, which consists of several sequential phases such as analyzing requirements, designing the system, implementing the solution, conducting tests, and performing ongoing maintenance. The findings of this research show that the system that has been created is capable of presenting academic information in a complete and structured manner, including assignment scores, daily tests, and semester examinations. The system provides faster and easier access for teachers to input grades and for parents to monitor their children’s academic development in real time. Functional testing shows that all features operate correctly according to user needs, with no errors found during system operation.

The aim of this study is to analyze verbal and non-verbal communication in an English as a Foreign Language (EFL) classroom by using the Sinclair and Coulthard (1975) model of classroom discourse. Although many studies have examined verbal interaction in EFL classrooms, few have discussed how verbal and non-verbal communication work together to support effective learning. To fill this gap, this research focuses on identifying the types and frequency of verbal and non-verbal communication used by the teacher and students during classroom interaction. This study used a descriptive qualitative method. The data were taken from an 80-minute video recording of an eleventh-grade English class at MAS Darul Quran. The recording was transcribed and analyzed based on Sinclair and Coulthard’s framework, which includes three main levels: Exchange (Informing, Directive, Question–Answer), Move (Initiation, Response, Feedback), and Act (Questioning, Explaining, Agreeing, Refusing, Revising, Appraising). The findings show that the classroom interaction was mainly teacher-centered. The teacher dominated the talk through Initiation moves, mostly in the form of questions, explanations, and instructions, while students gave short and simple responses. Feedback was used less often and mostly as short praise or confirmation. The teacher also used various non-verbal behaviors such as gestures, eye contact, movement, and changes in voice tone to direct attention and motivate students. The results suggest that combining verbal and non-verbal strategies can create a more interactive and engaging classroom atmosphere that supports student participation and understanding.

Differences in students’ levels of learning interest are often associated with their understanding of religious values, one of which is the understanding of hadiths concerning the virtues of knowledge. Hadiths emphasizing the virtues of knowledge are believed to stimulate interest in the learning process. This study aims to examine the influence of students’ understanding of hadiths on the virtues of knowledge on their learning interest at STDI Imam Syafi’i Jember. This research employed a quantitative approach with a correlational design. A total of 130 students were selected as respondents using simple random sampling. The research instruments were tested for validity and reliability, yielding Cronbach’s alpha values of 0.838 for the variable of understanding the hadiths on the virtues of knowledge and 0.884 for the learning interest variable. The normality test using Shapiro–Wilk produced a value of 0.097, indicating that the data were normally distributed, while the heteroscedasticity test showed a significance value of 0.968, confirming that the regression model was appropriate for use. The regression analysis revealed a significance value of p = 0.001 and a coefficient of determination (R²) of 0.362. This indicates that understanding the hadiths on the virtues of knowledge has a positive influence of 36.2% on students’ learning interest, while the remaining 63.8% is influenced by other factors. These findings affirm that understanding the values contained in hadiths on the virtues of knowledge plays an important role in enhancing students’ learning interest.

This community service activity was implemented at SDN 3 Tangkiling to cultivate an entrepreneurial mindset among elementary school students by integrating financial literacy and exploring creative professions. The program emphasized enhancing students' comprehension of fundamental financial principles through socialization activities and rudimentary buying and selling simulations using play money. The “Mini Shop” activity was implemented to facilitate students' learning about the value of money, the distinction between needs and wants, and the importance of saving. This activity was designed using an active and contextual learning approach, which aligns with the characteristics of elementary school students and the principles of the Merdeka Curriculum, emphasizing character building, independence, and creativity. Additionally, the exploration of creative professions was undertaken through the implementation of role-playing and interactive discussions, thereby introducing a variety of occupations and cultivating an interest in entrepreneurship from an early age. The outcomes of the activity indicated that students exhibited increased enthusiasm and comprehension of fundamental financial concepts and demonstrated an interest in elementary entrepreneurial activities. It is anticipated that this program will evolve into an inspirational learning model for instilling financial literacy and entrepreneurial spirit at the elementary school level.

This community service activity was implemented at SDN 3 Tangkiling to cultivate an entrepreneurial mindset among elementary school students by integrating financial literacy and exploring creative professions. The program emphasized enhancing students' comprehension of fundamental financial principles through socialization activities and rudimentary buying and selling simulations using play money. The “Mini Shop” activity was implemented to facilitate students' learning about the value of money, the distinction between needs and wants, and the importance of saving. This activity was designed using an active and contextual learning approach, which aligns with the characteristics of elementary school students and the principles of the Merdeka Curriculum, emphasizing character building, independence, and creativity. Additionally, the exploration of creative professions was undertaken through the implementation of role-playing and interactive discussions, thereby introducing a variety of occupations and cultivating an interest in entrepreneurship from an early age. The outcomes of the activity indicated that students exhibited increased enthusiasm and comprehension of fundamental financial concepts and demonstrated an interest in elementary entrepreneurial activities. It is anticipated that this program will evolve into an inspirational learning model for instilling financial literacy and entrepreneurial spirit at the elementary school level.

The development of national character is a strategic agenda in addressing moral, social, and cultural challenges in the era of globalization. Strengthening fundamental values derived from Islamic teachings is considered an important solution, particularly the values of obedience to Allah SWT, the spirit of competing in goodness (fastabiqul khairat), and work ethic. These three values play a crucial role in shaping individuals who are faithful, possess noble character, are responsible, and have high competitiveness. However, the reinforcement of these values in the practice of Islamic Religious Education (PAI) at the elementary, junior high, and senior high school levels has not yet been optimally and systematically integrated. Therefore, this article aims to analyze the concepts, urgency, and strategies for integrating the values of obedience, competition in goodness, and work ethic into PAI learning. The research method employed is a literature study using a qualitative-descriptive approach through the analysis of scientific literature, curriculum documents, and relevant concepts of Islamic education. The findings indicate that the value of obedience serves as a spiritual and moral foundation for students, the value of fastabiqul khairat encourages active, collaborative attitudes oriented toward positive contributions, while work ethic strengthens discipline, responsibility, and productivity. The integration of these three values can be implemented through the development of teaching materials, the application of contextual learning methods, educators’ role modeling, and the cultivation of a religious school culture. The implications of this study affirm that integrating these values into PAI significantly contributes to the formation of a young generation with integrity and readiness to build a dignified nation.

This research aims to develop Zepp Quiz as a creative learning media to enhance students' understanding of Islamic Cultural History at Madrasah Aliyah. The research method used is Research and Development (R&D) with the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The developed product consists of 10 multiple-choice questions about the Conquest of Mecca with varying difficulty levels, equipped with supporting illustrations, educational feedback, point systems, timers, and leaderboards. Data collection was conducted through quantitative methods using validation questionnaires from media experts and material experts with a Likert scale of 1-5. Data analysis was performed descriptively by calculating average scores and feasibility percentages. The results showed that media expert validation obtained an average score of 4.31 or 86% in the very feasible category, while material expert validation obtained an average score of 4.25 or 85% in the very feasible category. Based on expert validation results, Zepp Quiz learning media is declared very feasible to be used as a creative learning media in Islamic Cultural History learning at Madrasah Aliyah.