Penggunaan Media Pembelajaran di Masa Pandemi Covid-19 agar Hasil Belajar di SD Darut Tauhid Ar-Rafi Tetap Maksimal

The purpose of this study is to provide an overview of the online learning process during the pandemic at SD DAARUT TASBIH Ar RAFI, find out the various teaching media that can be used to be delivered in online teaching and learning activities (online). This study uses the method used in this study, namely the descriptive method with a qualitative approach with data collection techniques of observation, interviews and documentation in conducting the online learning process. Based on the research, various teaching media are used such as youtube, educational platforms such as whatsapp, google classroom , google meet, zoom cloud meeting, and others. Based on the results of the study, it can be concluded that the use of teaching media and educational platforms can be an alternative solution in learning, for the sake of continuity of learning in each education unit

Learning Management System sebagai Cloud Storage dalam Pembelajaran berbasis Digital pada Jenjang Pendidikan Tinggi

The purpose of this research is to analyze the acceptance of the Learning Management System as the Cloud Storage for students in digital-based learning at Universitas Kristen Satya Wacana. The research is conducted using a descriptive quantitative research design. The data is acquired from 250 students of Universitas Kristen Satya Wacana who participate in the online lesson using the Cloud Storage in the Learning Management System. The technique of data collection is by using a closed questionnaire through random sampling. The acceptance of Cloud Storage usage is measured using Technology Acceptance Model (TAM). The result of the research shows a high outcome. During online lessons, the students use Cloud Storage in LMS which is integrated with the data storage feature in form of lesson materials, assignment files, teacher-students discussion forums, presence lists, meeting links, etc. Cloud Storage usage in LMS provides convenience, and benefit gain so that it can increase class performance which is effective and efficient, convenient, and fun, and it will trigger the interest in actual use of digital platforms

Pemanfaatan Platform Digital Dalam Pembelajaran Online Selama Masa Pandemi Covid-19 Di Sekolah Dasar

This study aims to describe the use of digital platforms in the online-based learning process during the covid-19 pandemic in elementary schools. This research uses descriptive qualitative method carried out with a qualitative approach. The object of this study was elementary school teachers in SDN Tanjungsari. Data source technique is using purposive sampling technique. When the research was conducted in the even semester of the 2019/2020 school year. Data collection techniques used are through in-depth interviews conducted directly by telephone. Technical analysis of data using descriptive technical analysis. The results showed that the dominant digital platform used in the asynchronous or indirect online learning process at SDN Tanjungsari was the whatsapp social media platform by utilizing the existing features. Besides whatsapp, the zoom cloud meetings platform is also used in the online learning process in a direct or synchronous. Overall the teaching and learning process online through whatsapp and zoom cloud meetings can be implemented well.Penelitian ini bertujuan untuk mendeskripsikan pemanfaatan platform digital pada proses pembelajaran berbasis online selama masa pandemi covid-19 di sekolah dasar. Penelitian ini menggunakan metode deskriptif kualitatif dilakukan dengan pendekatan kualitatif. Objek penelitian ini adalah guru sekolah dasar di SDN Tanjungsari. Teknik pengambilan sumber data yaitu menggunakan teknik purposive sampling. Waktu penelitian dilakukan pada semester genap tahun ajaran 2019/2020. Teknik pengumpulan data yang digunakan yaitu melalui wawancara mendalam yang dilakukan secara langsung melalui telepon. Teknis analisis data menggunakan teknis analisis deskriptif. Hasil penelitian menunjukkan bahwa platform digital yang dominan digunakan dalam proses pembelejaran online asinkronus atau tidak langsung di SDN Tanjungsari adalah platform media sosial whatsapp dengan memanfaatkan fitur-fitur yang ada. Selain whatsapp, platform zoom cloud meetings juga digunakan dalam proses pembelajaran online secara langsung atau sinkronus. Secara keseluruhan proses belajar mengajar secara online melalui whatsapp dan zoom cloud meetings bisa dilaksanakan dengan baik

Reducing exposure to hateful speech online

It has been observed that regular exposure to hateful content online can reduce levels of empathy in individuals, as well as affect the mental health of targeted groups. Research shows that a significant number of young people fall victim to hateful speech online. Unfortunately, such content is often poorly controlled by online platforms, leaving users to mitigate the problem by them-selves. It’s possible that Machine Learning and browser extensions could be used to identify hateful content and assist users in reducing their exposure to hate speech online. A proof-of-concept extension was developed for the Google Chrome web browser, using both a local word blocker and a cloud-based mod-el, to explore how effective browser extensions could be in identifying and managing exposure to hateful speech online. The extension was evaluated by 124 participants regarding the usability and functionality of the extension, to gauge the feasibility of this approach. Users responded positively on the usability of the extension, as well as giving feedback regarding where the proof-of-concept could be improved. The research demonstrates the potential for a browser extension aimed at average users to reduce individuals’ exposure to hateful speech online, using both word blocking and cloud-based Machine Learning techniques

The Use of Teaching Media in Arabic Language Teaching During Covid-19 Pandemic

The characteristic of Arabic Language which is unique and different compared to other foreign languages, has become certain issue for academic society, especially with this actual condition of COVID-19, it gives impact to both teachers and students.  In university level, more specifically, in order to maintain teaching – learning process to be held, various kind of media are used to support the teaching and learning activities. This research aims for exploring and describing Arabic language teaching – learning online activities in IAIN Palangka Raya using qualitative method and case study. Data collecting methods used in this research are observations, interview and documentation. Research result shows that Arabic language teaching – learning online activities in IAIN Palangka Raya adopting online technology with platforms which based on social media and e-learning application. Those platforms are categorized into three: 1) WhatsApp Group, used for intensive communications between teacher and students related to teaching – learning activities; 2) Google Classroom, used for collecting assignments; and 3) Zoom Cloud Meeting, used for audio-visual media for learning topics which need significant explanation. These medias are used after consideration on ease of accessibility, hardware compatibility, communication features, process and cost needed to use them. The findings of this research describe that Arabic language teaching – learning online activities in IAIN Palangka Raya collaborate those three aforementioned applications so that creativity, innovation and motivation are growing even during Covid-19 pandemic situation

A New Cloud with IoT-Enabled Innovation and Skill Requirement of College English Teachers on Blended Teaching Model

The blended teaching model is a type of educational approach that combines traditional classroom-based instruction with online learning experiences. In this model, students are given access to digital content, resources, and tools, which they can use to supplement their in-person classroom instruction. The blended teaching model is also sometimes referred to as the hybrid learning model. IoT-SDNCT (IoT enabled SDN reinforcement learning with Cloud Technological Innovation and Skill Requirement of College English Teachers on Blended Teaching Model) is a proposed system that aims to revolutionize blended teaching models by leveraging the power of IoT, SDN, and cloud computing technologies. This system incorporates intelligent reinforcement learning algorithms and real-time data analysis to optimize the learning process and improve student engagement and outcomes. In the IoT-SDNCT system, IoT devices such as sensors and wearable technologies are deployed to collect real-time data on student engagement and performance. This data is then transmitted to an SDN controller, which dynamically manages the network infrastructure and optimizes learning pathways. The collected data is also stored and processed in cloud computing platforms, allowing for advanced analytics and personalized feedback for both students and teachers. The key contribution of IoT-SDNCT lies in its ability to adapt the learning process in real-time based on the collected data and intelligent algorithms. This adaptive learning approach enables personalized learning experiences, adjusts the difficulty level of learning tasks, and provides timely feedback to students. Moreover, it empowers teachers with valuable insights and analytics to enhance their teaching strategies and address individual student needs effectively. The proposed system addresses the technological innovation and skill requirements of college English teachers by integrating IoT, SDN, and cloud computing technologies. By utilizing IoT devices, SDN controllers, and cloud platforms, teachers can optimize their teaching methods and create dynamic and interactive learning environments. This not only enhances student engagement but also improves learning outcomes and fosters skill development in both teachers and students. The system's adaptive learning capabilities and real-time data analysis contribute to an enhanced learning experience, increased student engagement, and improved teaching effectiveness

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach

Many algorithms in workflow scheduling and resource provisioning rely on the performance estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the execution of tasks and predicting their runtime accurately, therefore, becomes an essential part of any Workflow Management System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS) platforms that use clouds for deploying scientific workflows, task runtime prediction becomes more challenging because it requires the processing of a significant amount of data in a near real-time scenario while dealing with the performance variability of cloud resources. Hence, relying on methods such as profiling tasks' execution data using basic statistical description (e.g., mean, standard deviation) or batch offline regression techniques to estimate the runtime may not be suitable for such environments. In this paper, we propose an online incremental learning approach to predict the runtime of tasks in scientific workflows in clouds. To improve the performance of the predictions, we harness fine-grained resources monitoring data in the form of time-series records of CPU utilization, memory usage, and I/O activities that are reflecting the unique characteristics of a task's execution. We compare our solution to a state-of-the-art approach that exploits the resources monitoring data based on regression machine learning technique. From our experiments, the proposed strategy improves the performance, in terms of the error, up to 29.89%, compared to the state-of-the-art solutions.Comment: Accepted for presentation at main conference track of 11th IEEE/ACM International Conference on Utility and Cloud Computin

Cloud Computing Based Online Learning for Students Vocational Education (D-3) Electronic Engineering Department

ABSTRACT: The last few years the concept of Cloud Computing is already a lot of interest of industry and education. Cloud-based solution seems to be the key for IT organizations who have a problem of budget constraints. Cloud Computing is a new paradigm in distributed computing presents many ideas, concepts, technologies, and the type of architecture that served as a service-oriented. According to Foster Cloud Computing is a "paradigm of distributed computing on a large scale are motivated by economic factors, which contains a set of virtualization abstract, dynamic scalability, setting the computing power, storage, platforms and services that can be accessed in accordance with the requirements by external customers through the Internet "(Foster et al., 2008). Objectives to be achieved in this research are: 1) To know how to develop online learning model based on cloud computing (cloud computing) for vocational education students (D-3) FT UNM's department of electronics engineering; 2) To know how to design online learning model based on cloud computing (cloud computing) for vocational education students (D-3) FT UNM's department of electronics engineering; 3) To know the result of the development of online learning based on cloud computing (cloud computing) for vocational education students (D-3) department of electronics engineering FT UNM may meet the criteria for a valid, practical, and effective. The method used in this research is the development of research methods (Research & Development), which focuses on online learning based on cloud computing (cloud computing). Students today can not live away from the Internet. Through programs such as facebook, twitter, instagram, and gmail, s tudents are accustomed to using cloud-based technology services (Ercan, 2010). Therefore, the students hope to be able to access digital technology services on campus anywhere and anytime, including cloud services that support social media. Likewise pendiidkan vocational students who are currently in the industry are already using advanced technology. So should students have to understand the process and the system. Besides, in the learning process also greatly contribute to improving student achievement, especially in the learning lab. Thus researchers are interested in developing research to develop an existing model into a new model in online learning based on cloud computing (cloud computing)