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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 sophistication of cyber threats has rendered traditional cybersecurity models insufficient in safeguarding enterprise networks. This study introduces a risk aware cybersecurity governance model that integrates real time threat intelligence with predictive anomaly detection to proactively mitigate potential threats. By leveraging advanced machine learning and AI techniques, the model enhances the ability to identify and address cyber threats before they can escalate into significant incidents. The model’s ability to predict anomalies, analyze real time threat intelligence feeds, and provide early warnings allows for faster response times and reduced risk exposure compared to traditional reactive models. Through simulations and real-world use cases, the proposed model demonstrated a 30% reduction in response time and a 25% decrease in overall risk exposure, showing its potential to improve security decision-making and resilience in dynamic threat environments. Unlike traditional models that rely on static rules and periodic policies, the proposed model uses predictive analytics to stay ahead of evolving threats, ensuring continuous monitoring and rapid adaptation. This proactive approach enhances organizational resilience, particularly in handling sophisticated cyber threats such as ransomware, malware, and phishing attacks. Despite its effectiveness, challenges such as data overload, scalability, and the need for interpretability in AI models remain. Future research will focus on refining predictive models, improving scalability for larger networks, and enhancing the explainability of machine learning models to foster greater trust in automated cybersecurity systems. This study contributes to the ongoing evolution of cybersecurity governance by demonstrating the value of integrating predictive and real time monitoring technologies for enhanced threat detection and mitigation.

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.

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.

The rapid proliferation of Internet of Things (IoT) devices across diverse industries has significantly increased the vulnerability of IoT edge networks to sophisticated cyber threats. Traditional intrusion detection systems (IDS), such as signature-based and anomaly-based approaches, are often insufficient in addressing the dynamic and evolving nature of these threats. This study proposes a hybrid intrusion detection system (IDS) framework that combines supervised machine learning (ML) techniques with deep reinforcement learning (DRL) to enhance detection performance in real-time, resource-constrained IoT environments. The proposed framework utilizes supervised learning for initial traffic classification and DRL for adaptive decision-making, enabling the system to continuously learn and optimize its detection policies based on new attack patterns. The hybrid approach significantly improves detection accuracy and reduces false positives when compared to conventional signature-based and single-model ML systems. In addition to improved detection capabilities, the framework's computational efficiency allows it to operate effectively within the constraints of IoT devices, ensuring that it is suitable for large-scale deployments. Benchmark evaluations using publicly available datasets, such as NSL-KDD, IoT-23, and BoT-IoT, show that the hybrid IDS framework outperforms traditional methods, providing a more robust and adaptive solution to cybersecurity challenges in IoT edge networks. The findings of this study suggest that combining machine learning with deep reinforcement learning offers a promising approach to secure IoT environments and address the limitations of existing IDS techniques. Future work will explore enhancing real-time adaptability, scalability, and the detection of zero-day attacks in evolving IoT ecosystems.

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.

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.  

  Abstract. This study aims to develop and evaluate an artificial intelligence–based expert system to support fault diagnosis in Air Handling Units (AHUs). Early fault identification in AHU systems is often constrained by reliance on technician experience, which may lead to inconsistent diagnostic outcomes. The proposed system is intended to provide a consistent, explainable, and practical decision-support tool to assist maintenance personnel, particularly less-experienced technicians, in identifying AHU faults accurately. Methodology: The research adopts a rule-based expert system approach using forward chaining inference. Knowledge acquisition was conducted through structured interviews with experienced HVAC technicians and supported by technical documentation. The resulting knowledge base consists of observable symptoms, diagnostic rules, and corresponding corrective actions. The system was implemented as an Android-based mobile application to enable direct field usage. System validation was performed using real operational fault scenarios, with expert diagnoses serving as the reference standard. Findings: Evaluation results indicate full agreement between the system-generated diagnoses and expert assessments across all tested scenarios. This demonstrates that the proposed system is capable of producing accurate and consistent diagnostic outcomes within its defined knowledge domain. Implications: The system operates using deterministic rules without incorporating uncertainty modeling or probabilistic reasoning. Additionally, validation was limited to a finite number of real-world scenarios, which may affect generalizability to broader AHU configurations. Practical implications: The expert system can be utilized as a practical diagnostic aid in routine AHU maintenance, improving response time, diagnostic consistency, and technician training effectiveness. Originality: This study contributes a mobile-based, explainable expert system specifically tailored for AHU fault diagnosis, emphasizing practical deployment and rule transparency rather than data-intensive learning models.

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.

Pemanfaatan aplikasi berbasis web yang semakin meluas di berbagai sektor menyebabkan meningkatnya risiko terhadap ancaman siber, termasuk serangan phising, DDoS, injeksi SQL, XSS, serta intrusi. Berbagai metode deteksi tradisional yang bersifat statis sering kali tidak mampu mengidentifikasi pola serangan baru yang bersifat dinamis. Untuk mengatasi keterbatasan tersebut, machine learning muncul sebagai pendekatan yang lebih adaptif karena mampu mempelajari pola perilaku, mendeteksi anomali, dan melakukan deteksi ancaman secara otomatis. Penelitian ini melakukan tijauan sistematis literatur (Systematic Literature Review/SLR) untuk meneliti penggunaan machine learning dalam mengingkatkan keamanan aplikasi web dan mengevaluasi performa beberapa algoritma yang digunakan. SLR dilaksanakan dengan menggunakan kerangka PICOC, yang mencakup populasi, intervensi, perbandingan, hasil, konteks. Hasil analisis menunjukkan bahwa machine learning digunakan untuk berbagai aspek keamanan web, seperti deteksi phising, penentuan URL berbahaya, pengurangan dampak serangan DDoS, serta identifikasi intrusi jaringan. Algoritma seperti Random Forest, XGBoost, dan SVM terbukti memiliki kinerja yang stabil dengan tingkat akurasi yang tinggi. Selain itu, teknik pendukung seperti ADASYN, feature selection, dan metode berbasis pemrosesan bahasa alami turut meningkatkan efektivitas model. Secara keseluruhan, hasil penelitian ini menegaskan bahwa machine learning dapat mempercepat dan meningkatkan ketepatan deteksi ancaman serta memberikan adaptasi terhadap pola serangan yang terus berubah, sehingga menjadi komponen penting dalam memperkuat keamanan aplikasi web.

Writing skills are a fundamental component of Indonesian language learning at the elementary school level, yet many students experience difficulties in composing descriptive texts due to limited vocabulary, weak text structure, and low engagement in the learning process. This study aimed to examine the effectiveness of the Project-Based Learning (PjBL) model in improving students’ ability to write descriptive texts about places. The research employed a quantitative approach using a quasi-experimental design with a One Group Pretest–Posttest Design. The subjects of the study were 27 sixth-grade students of SDN Selagedang. Data were collected through descriptive writing tests, observation of learning activities, and documentation. The writing test results were analyzed using descriptive statistics and inferential analysis with a t-test to identify differences in students’ writing ability before and after the implementation of PjBL. The findings revealed a significant improvement in students’ descriptive writing skills following the application of Project-Based Learning, as indicated by higher posttest scores compared to pretest scores. In addition, classroom observations showed increased student engagement, collaboration, and active participation during the learning process. These results suggest that Project-Based Learning provides meaningful learning experiences by integrating observation, collaboration, and authentic writing tasks. The study implies that PjBL can be an effective alternative instructional model for teaching descriptive writing in elementary schools, as it not only enhances students’ writing outcomes but also fosters active and contextual learning environments.

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 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.

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.

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.

This study aims to analyze the effectiveness, challenges, and solutions of the application of the Indonesian teaching module of biographical text material in grade X students of SMK Negeri Simpang Empat Tanah Bumbu in the Independent Curriculum. Teaching modules, which are essential for systematic planning, are often difficult for teachers to compile. This module focuses on the development of listening and speaking skills, as well as the cultivation of the Pancasila Student Profile (independent, critical, creative), supported by students' initial knowledge, various media, and a Creative-Productive learning model. With qualitative methods, the study found that although the teaching modules succeeded in creating planned learning (through activities such as goal negotiations, video exploration, and group discussions), there were major challenges in integrating all elements effectively, optimizing students' initial knowledge, developing independence/critical thinking, and overcoming media constraints and classroom dynamics. Therefore, this study emphasizes the need for teacher innovation in compiling and implementing modules so that learning becomes even, interesting, and in accordance with the spirit of the Independent Curriculum.

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 study aims to develop AR-Venture Nusantara learning media on Indonesian cultural diversity material for fourth-grade elementary school students and to examine its validity, effectiveness, and practicality. The research employed a Research and Development (R&D) approach using the ADDIE model, which consists of analysis, design, development, implementation, and evaluation stages. The study was conducted at UPT SDN 256 Gresik with 19 fourth-grade students as research subjects. Data were collected through interviews, media and material expert validation questionnaires, learning achievement tests, and student response questionnaires. The data were analyzed using descriptive quantitative techniques. The results showed that AR-Venture Nusantara media achieved a very valid category, with media expert validation reaching 96.43% and material expert validation reaching 95.31%. The effectiveness of the media was demonstrated by student learning outcomes, where 16 out of 19 students achieved mastery learning with scores ≥75, indicating classical completeness. In addition, the practicality of the media based on student responses reached 86.51% in the very good category. These findings indicate that AR-Venture Nusantara media is valid, effective, and practical for use in IPAS learning, particularly in introducing Indonesian cultural diversity.

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.