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The objectives of this research include 1) To determine the application of the Project Based Learning model in making embroidery decoration designs in the fashion decoration subject to improve the learning outcomes of Class XI students at SMKN 1 Sooko. 2.) To determine the effectiveness of applying the Problem Based Learning model in creating embroidery decoration designs in the fashion decoration subject to improve the learning outcomes of Class XI students at SMKN 1 Sooko. This type of research is quantitative research with a pre-experimental design and a one shot case study model. This research was carried out at SMK Negeri 1 Sooko in class XI Design and the number of students was 22 people. The data collection method used is observation to determine the application of the Project Based Learning model to embroidery decoration designs, questionnaires to determine student learning responses and tests to determine the effectiveness of implementing learning models on student learning outcomes. The results of the research state that the application of the Project Based Learning learning model can be seen from the implementation of the Project Based Learning learning model in the design of embroidery decorations in the fashion decoration subject, obtaining an average score of 3.7 for teacher activities and an average score of 3.4 for student activities. Student learning outcomes on the cognitive test obtained an average score of 81.5 and the average score on the psychomotor test obtained an average score of 87.5. The overall average score of students' learning outcomes was 86.4, which was declared complete the Minimum Learning Standard. As many as 85% of students completed the Minimum Learning Standard and 15% did not completed.

This study explores the integration of predictive analytics into supply chain management within national e-commerce enterprises. Predictive analytics, which utilizes historical data combined with machine learning algorithms, regression analysis, and time series forecasting, has shown significant improvements in operational efficiency. The study focuses on four key areas: demand forecasting, inventory management, transportation optimization, and customer satisfaction. By predicting demand more accurately, e-commerce platforms can reduce stockouts and overstock situations, streamline logistics routes, and lower logistics costs. The implementation of predictive analytics led to a 20% reduction in delivery times and a 15% decrease in logistics costs, thereby enhancing customer satisfaction. However, the study also highlights challenges in integrating real-time data from multiple sources and scaling predictive models across diverse product categories and geographic regions. The results emphasize the need for e-commerce platforms to invest in technology that enables seamless data integration and the development of region-specific predictive models. The findings are compared with industry benchmarks, showing that the improvements in logistics and supply chain performance align with global trends. Based on these results, the study recommends best practices for implementing predictive analytics, including effective data collection, machine learning model training, and scalability considerations. By following these practices, e-commerce companies can optimize their supply chains, reduce operational costs, and increase customer satisfaction, positioning them for greater competitive advantage in the marketplace.

The rapid growth of data-intensive applications has posed significant challenges for classical machine learning (ML) algorithms, particularly in terms of computational efficiency and scalability. This study explores the role of quantum computing in optimizing machine learning performance through the implementation of Quantum Machine Learning (QML), specifically using the Quantum Support Vector Machine (QSVM) model. The research adopts a Design Science Research approach, involving problem identification, model development, system implementation, and performance evaluation. Both classical Support Vector Machine (SVM) and QSVM models are developed and tested using benchmark classification datasets. The results indicate that QSVM outperforms the classical SVM model across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Additionally, QSVM demonstrates improved computational efficiency by reducing training time, particularly when handling high-dimensional data. These improvements are attributed to the ability of quantum computing to utilize quantum kernel methods and map data into higher-dimensional feature spaces, enabling better pattern recognition and classification performance.  Despite these promising outcomes, the study also identifies several limitations related to current quantum hardware, such as noise, decoherence, and limited qubit availability, which may affect scalability and practical implementation. Therefore, further research is required to enhance quantum hardware reliability and develop hybrid quantum-classical models. In conclusion, quantum machine learning offers a promising solution to overcome the limitations of classical approaches, providing enhanced performance and efficiency for complex data processing tasks in future intelligent systems.

The rapid growth of digital technologies has significantly increased the complexity and frequency of cyber threats, making network security a critical concern in modern information systems. Traditional security approaches, such as rule-based and signature-based systems, are often limited in detecting sophisticated and unknown attacks. Therefore, this study proposes an Anomaly-Based Intrusion Detection System (AbIDS) utilizing machine learning and deep learning techniques to enhance detection capabilities. The research adopts a Design Science Research approach, involving stages of problem identification, data collection, preprocessing, model development, system implementation, and evaluation. Several models, including Decision Tree (DT), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are implemented and compared. The results indicate that deep learning models, particularly LSTM and CNN, outperform traditional machine learning methods in terms of accuracy, precision, recall, and F1-score, while maintaining a lower false positive rate. Additionally, the integration of incremental learning enables the system to adapt to new attack patterns without requiring complete retraining, improving scalability and real-time performance. Despite the promising results, challenges such as computational complexity and false positives remain. Overall, the proposed IDS model demonstrates strong potential as an effective and adaptive solution for enhancing network security in dynamic environments.

This study is motivated by the limited research on holistic education in developing 21st-century skills to address the challenges of the digital era, despite its significant implications for future human resource quality. The objective of this research is to explore the concept of holistic education based on 21st-century skills and its relevance in the digital era through a literature review approach. The method employed is qualitative, focusing on an analysis of academic literature, including documents, journals, books, and various scholarly sources. The analysis was conducted thematically to investigate the principles, models, and practices of holistic education that support the development of critical thinking, creativity, communication, and collaboration skills. The findings indicate that holistic education can serve as a key approach to prepare younger generations to navigate the complexity of the digital era with adaptive, ethical, and innovative thinking skills. This review enriches the literature on holistic education and highlights the importance of an integrated approach that intertwines cognitive, emotional, social, and technological dimensions within the context of modern learning. Practical implications include recommendations for educators and policymakers to design curricula based on 21st-century skills and integrate digital technologies into building holistic learning processes. This study also offers opportunities for further research on the implementation of holistic education in diverse cultural and geographical contexts.

This study aims to determine the implementation of blended learning in improving the scientific literacy of SMA/MA students in Indonesia. This research is a type of meta-analysis research. The sample of this research comes from the analysis of 14 articles that have been published from 2017-2022. The sampled articles have been indexed by SINTA, DOAJ, Google Scholar, Scopus and Copernicus. Research sample search through google scolar and sciencedirect. The sampling technique is purposive sampling technique. The data that can be sampled only has a relationship between the independent variable and the dependent variable, namely blended learning and students' scientific literacy. The data analysis technique in this study is a quantitative data analysis technique with SPSS 21 and JSAP applications with a value of sig.0.005. The application is to calculate the value of Effect Size (ES), Mean and Standard deviation (SD). The results of this study concluded that the application of blended learning was able to increase the scientific literacy of SMA/MA students in Indonesia with an Effect size (ES) of 0.494 and an n-Gain of 0.391. So, teachers in the 4.0 revolution era must be able to apply blended learning models to students so that students are able to face current global competitors.

This research aims to evaluate the effectiveness of the Missouri Mathematics Project (MMP) Learning Model in improving mathematics learning outcomes for junior high school students. This study employs a comparative approach, comparing mathematics learning outcomes between students taught using MMP and those taught using conventional teaching models. Data were collected through hypothesis testing of mathematics learning outcomes for both groups. The data analysis results indicate a significant difference in mathematics learning outcomes between the two groups, with a significance value (sig.) smaller than the commonly used significance level. These findings suggest that the implementation of MMP effectively improves mathematics learning outcomes for junior high school students. The practical implications of these findings are discussed in the context of developing more effective and inclusive mathematics teaching strategies.

The rapid spread of infectious diseases remains a major global health threat, and early detection is vital to minimize their impact. This research investigates the role of predictive modeling using big data in the early detection of infectious disease outbreaks. The primary objective of this study is to assess the effectiveness of big data systems in forecasting potential outbreaks and the implications of these forecasts for public health systems. The study employs machine learning-based predictive models to process large health datasets, including electronic health records, sensor data, and social media information. The results demonstrate that the predictive model achieved an accuracy rate of 87%, significantly surpassing traditional methods in terms of early detection. By integrating various data sources such as medical records, sensor networks, and real-time digital traces, the system is capable of providing more accurate, timely predictions, which can greatly improve the ability of public health authorities to respond effectively to emerging health threats. Furthermore, the application of big data in public health not only improves the speed of response but also enhances the allocation of resources, allowing for more targeted and efficient interventions. Despite these successes, challenges remain, particularly in relation to data quality, privacy, and regulatory issues, which could hinder the broader implementation of such systems. Thus, collaboration between government agencies, healthcare institutions, and technology developers is essential to overcome these obstacles and ensure the sustainable integration of big data into public health infrastructures. This research highlights the significant potential of big data to transform public health responses, offering valuable insights for future epidemic management strategies.  

This study examines the impact of differentiated learning models in inclusive educational settings, focusing on their effect on student participation, motivation, and academic performance. Differentiated instruction (DI) adapts content, teaching methods, and assessments to meet the diverse learning styles, interests, and abilities of students. Using a mixed methods approach, the research combines quantitative and qualitative data to explore how DI influences engagement and academic outcomes in diverse classrooms. The findings show that DI significantly increased student participation, as lessons tailored to individual strengths encouraged more active involvement. Motivation also improved, with students displaying higher intrinsic motivation when engaging with relevant material at their own pace. Additionally, academic performance improved, especially for students who had struggled with traditional teaching methods. These results suggest that DI is an effective strategy for inclusive education, particularly for students with special educational needs and disabilities (SEND). The study emphasizes the importance of teacher training and ongoing professional development for successful DI implementation. The implications are significant for global educational settings, where classrooms are becoming more diverse. By adopting differentiated strategies, educators can create more equitable and inclusive learning experiences. Future research should explore the long-term effects of DI in various cultural and educational contexts.

This research used an experimental design, and the data were processed using SPSS version 25 for Windows, with a sample of 20 Grade V students. Data collection for this research used Pretest, Treatment/Action in the form of implementing the board game "Cultural Exploration of Panrita Lopi". After the action was completed, a Posttest was conducted to measure the students' reading comprehension skills once again. This data was used to compare the results before and after the intervention.The results of the research clearly show that there was a significant improvement in the reading comprehension skills of Grade V students at SDN 131 Bontobana after the implementation of the board game "Cultural Exploration of Panrita Lopi." This indicates that the game can be used as an effective learning tool in improving students' reading comprehension skills.This research can contribute to knowledge in the field of education and learning. Through this research, new information about the use of the cultural exploration game of Panrita Lopi in improving the reading comprehension skills of Grade V students can be collected and presented, thus helping to expand our understanding of effective learning strategies. This research can also provide a better understanding of the relationship between cultural exploration games and reading comprehension skills. The results of this research can be used to develop better theories and learning models that can be used by educators and researchers in the future.

This study examines the impact of blended learning on student engagement and academic achievement in secondary schools during the recovery phase of the pandemic. A quantitative research approach was employed, utilizing a comparative design to assess the differences between blended learning and conventional learning models. Data were collected through student engagement surveys and academic performance records from secondary school students exposed to both learning models. The results indicated that students in the blended learning model exhibited significantly higher levels of engagement across behavioral, emotional, and cognitive dimensions compared to those in the conventional model. Academic achievement was also notably higher in the blended learning group. Statistical analysis, including t-tests and linear regression, revealed a strong positive correlation between student engagement and academic performance. The study concluded that blended learning significantly enhances student involvement and performance, particularly when supported by adequate technological infrastructure and teacher preparedness. These findings suggest that blended learning should be integrated as a permanent strategy in post-pandemic education to support student success and engagement. The study also emphasizes the importance of continuous teacher training and technological support in maximizing the benefits of blended learning.

This research using the PTK (Classroom Action Research) method which aims to be able to find efforts to increase student activities through project-based learning models, to improve cognitive abilities and student learning interests. Learners are educational subjects considered active in shaping their chemistry learning achievement. The subjects this study is students of class XI IPA 2 SMA Dian Harapan Holland Village Manado. The focus of this research is the cognitive ability and learning interest of students. The results showed that the cognitive ability and interest in learning class XI IPA 2 SMA Dian Harapan Holland Village Manado there is an increase marked by an increase in the average score from cycle I to cycle III. There was also an increase in the activity of students. Therefore, the implementation of the Project Based Learning Model (PjBL) can provide a good and positive response for students.

The use of learning models can also be a factor that determines students' learning success. If the learning model used is appropriate, it can arouse students' interest in studying the material being taught in such a way that it influences the quality of learning outcomes. So the teacher must be able to create a learning model that creates a situation where the class of students can be active in the learning process. This research was carried out using the Classroom Action Research (PTK) method. Wardani & Wihardit (2018) stated that PTK is research conducted by a teacher in the class he teaches which aims to improve student learning outcomes. The subjects of this research were 36 students in class XI MIPA 3, consisting of 11 male students and 15 female students. P2K implementation is carried out in 2 cycles. The results of classroom action research using the Discovery Learning model with a scientific approach for two cycles, the results obtained are: First, the application of the Discovery Learning learning model with a scientific approach can improve students' science learning outcomes in class XI SMAN 9 Maros. Second, the application of the Discovery learning model Learning with an effective scientific approach can increase liveliness and activity in the teaching and learning process in accordance with observations of students' attitudes during the implementation of classroom action research in cycle I and cycle II.

This research aims to determine the influence of the Discovery Learning Learning Model on the Learning Outcomes of Class X.7 Students in Civics Subjects at UPT SMA Negeri 3 Maros. The research subjects were 36 students of class X.7 UPT SMA Negeri 3 Maros for the 2023/2024 academic year. This type of research is classroom action research, the steps of action planning, action implementation, observation-evaluation, and reflective analysis are recycled. Observation, testing, and documentation are used as data collection strategies. The study findings were examined qualitatively, the results of observations showed that there was an increase from cycle I to cycle II. This is proven in the analysis of the results of student discussion/question and answer, namely in cycle I the percentage of student understanding was 100% with an average score of 82. Meanwhile in cycle II it increased with the presentation of student understanding reaching 100% with an average score of 87. The results of these calculations can be It was concluded that there was a significant influence on the discovery learning model on the learning outcomes of Class X.7 students in Civics subjects at UPT SMA Negeri 3 Maros.

Learning models develop as technology develops. Nowadays teachers can no longer teach using conventional teaching methods only but they also have to use technology for teaching. The use of technology in teaching does not mean that you have to abandon old methods. However, combining the two could be a good alternative. One of the learning models is the hybrid learning or blended learning model. What is its application in teaching English courses at the Catholic University of Widya Karya Malang? This research explores the process of using hybrid learning in learning English at Widya Karya Catholic University Malang, especially in English subject . The method used in this research is a qualitative descriptive method, which provides an overview of the implementation of this learning method. It turns out that using this method provides many positive benefits both from the teacher's side and from the student's side.

This journal research aims to see the implementation of teacher perceptions in curriculum development. This research uses quantitative research by collecting structured data through measurement instruments such as questionnaires. From the results of observations through questionnaires or systematic observations regarding this curriculum change, first, this curriculum change has an effect on learning styles, for example, if previously teachers only relied on books and blackboards when teaching, now it is all digital and of course this also makes it easier for teachers to teach and makes it easier for students to receive material from the teacher and can also influence learning models or methods. Meanwhile, changes to the curriculum can improve the quality of education. With this curriculum change, there are also challenges faced by schools, one of which is that teachers need to be prepared to utilize technology effectively in the learning process and also how to implement it. So, in conclusion, this curriculum change is indeed important but must be adjusted to the curriculum and current developments. In accordance with research that the author observed, schools currently prefer the Independent Curriculum

This research aims to describe the implementation of the learning model project based learning in learning to write proposal texts for class XI students at SMA Negeri 8 Yogyakarta. This research is included in qualitative descriptive research. The subjects in this research were Indonesian language teachers and class XI students of SMA Negeri 8 Yogyakarta, even semester of the 2022/2023 academic year. The objects in this research are learning tools prepared by the teacher and proposal texts produced by class XI students at SMA Negeri 8 Yogyakarta. Data collection in this research was carried out by documentation and observation. The document in this research is the Learning Implementation Plan or Teaching Module. Furthermore, the observations in this research are participatory observations. Based on the analysis of planning, implementation and evaluation of learning, it was concluded that the project based learning model has been able to become a learning model that guides students' independence in learning Indonesian on the topic of activity proposal texts. With this independence, learning will be student-centered, so that the implementation of learning that focuses on new paradigm learning can be achieved optimally.

This article aims to analyze the Methods, Models, and Approaches to Evaluation in Islamic education. The research employs a literature review method, gathering data from relevant books, journals, and scholarly articles. Content analysis is used to classify the data, and evaluation is conducted to summarize and explain the findings. In the field of Islamic education evaluation, the evaluation models used encompass several approaches outlined by experts. These models include the Goal Oriented Evaluation Model, Goal Free Evaluation Model, Formative Summative Evaluation Model, and Countenance Evaluation Model. Additionally, evaluation methods in Islamic education involve both test and non-test techniques, with tests categorized into diagnostic tests, formative tests, and summative tests. Non-test techniques include rating scales, questionnaires, matching lists, interviews, observations, and life histories. The article also explains the approach to Islamic education evaluation from a learning perspective, covering formative evaluation, summative evaluation, and placement evaluation. This approach refers to the viewpoint on the evaluation process and provides a theoretical foundation for the implementation of appropriate evaluation methods.

The Cyber-Physical System is a key system in the implementation of the 4th generation industrial revolution. This system combines automation systems, electronics, internet networks and machine learning. The implementation of physical cyber systems in agriculture  is one of the long-awaited applications because it is the backbone of the implementation of  sustainable agribusiness systems. With this system precision agriculture and pervasive computing in agriculture can help stakeholders in agribusiness to enjoy various benefits optimally. One example of its application is a smart farming system that is conditioned to provide measurable and maximum yields without having to sacrifice soil nutrients because it is well monitored according to weather conditions.  In previous studies, several LoRa communication-based systems for monitoring local weather in a place and measurements of soil nutrient conditions have been carried out and displayed through the internet network. Monitoring and control systems on other agricultural models based on hydroponics and aquaponics have also been carried out for some types of crops. The results provide greater potential for wider application and in actual conditions in the agricultural industry. In this study, the integration of physical cyber systems with lora communication-based  agricultural monitoring and control nodes will be carried out more broadly by considering the local conditions under which the system is tested. In this case, the research will be carried out at the Research and Recreation Park, Telkom University and agrifarming industry partners as a model of actual application. The research findings show that the application of advanced sensor technology has improved the accuracy of weather measurements by up to 95%. Quantitative data collected from the new weather station showed a significant improvement in weather monitoring. Qualitatively, positive responses from stakeholders such as disaster management authorities and farmers were also noted. In conclusion, the development of this modern weather station supports the community's need for reliable weather information and is a step forward in addressing the challenges of climate change.

High Order Thinking Skill (HOTS) is a way of thinking for students at a higher cognitive level. This research aims to prove whether the implementation of HOTS-based learning has an impact in improving students' critical thinking skills in mathematics learning in elementary schools. The research method used is a literature review using the Google Scholar and Semantik Scholar databases for the 2020-2023 period. The HOTS-based learning model focuses on developing analyzing, evaluating and creating which refers to students' abilities in critical thinking. Based on the 8 selected articles, it shows that the implementation of HOTS learning in mathematics subjects can be implemented through learning models such as Game Based Learning or Digital Game Based Learning, teacher skills in implementing learning and developing assessments need to be considered. The application of HOTS learning is considered effective and able to improve students' critical thinking skills.