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All Journal Journal on Education Society: Jurnal Pengabdian Masyarakat Room of Civil Society Development
Muhammad Awaluddin
Universitas Muhammadiyah Buton
Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences Other

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Workshop and Assistance on the Utilization of E-Learning at the Universitas Muhammadiyah Buton Muhamad Iksan; Wa Ode Alzarliani; Samritin Samritin; Azaluddin Azaluddin; Muhammad Awaluddin
Society : Jurnal Pengabdian Masyarakat Vol 2, No 1 (2023): Januari
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v2i1.235

Abstract

Improving the learning system chosen by the Universitas Muhammadiyah Buton (UM Buton) in order to compete in the era of disruption with advances in information and communication technology (ICT), particularly electronic-based learning systems, one of which is known as the e-learning learning system. Several professors at UM Buton had difficulties in adding learning material in the form of text, and video, visualizing quiz results and semester examinations, and building forums for question-and-answer sessions while utilizing e-learning. Based on these issues, the service team developed training and mentoring for UM Buton lecturers. It is hoped that with this training, UM Buton will be able to produce quality lecturers and highly educated skilled professionals who can keep up with the times and ICT-based technology while carrying out Catur Dharma tasks and responsibilities as a lecturer at Muhammadiyah Higher Education.
E-Learning Application Training for AL-SAFITRI Vocational School Teachers in South Buton Regency Muhammad Hibrian Wiwi; Muhammad Awaluddin
Room of Civil Society Development Vol. 2 No. 1 (2023): Room of Civil Society Development
Publisher : Lembaga Riset dan Inovasi Masyarakat Madani

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (524.959 KB) | DOI: 10.59110/rcsd.v2i1.155

Abstract

Combining face-to-face meetings, in this case in class, with electronic learning can increase the contribution and interactivity between students. Through face-to-face students can get to know fellow students and their accompanying teachers. This familiarity greatly supports virtual collaborative work. Improving the quality of education at SMK 1 Al-Safitri is one of them by conducting online learning (in the network). E-Learning can be interpreted as a science of learning without having to use printed paper/hand out the material presented. On average, people working in the field of e-learning, especially from the fields of education, psychology, and computer science, know and use Moodle. teacher accounts that provide teaching topics or provide other subject matters besides being able to provide teaching topics there are several advantages of this Moodle LMS, namely there is a quiz feature, or videos.
Implementasi Hadoop Mapreduce Untuk Memprediksi Predikat Kelulusan Mahasiswa Muhammad Awaluddin; Rini Angelia Mahlil; Darmawan Darmawan; La Ode Muhammad Saidi
Journal on Education Vol 5 No 4 (2023): Journal on Education: Volume 5 Nomor 4 Mei-Agustus 2023
Publisher : Departement of Mathematics Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joe.v5i4.4116

Abstract

This study aims to implement Hadoop MapReduce as a data processing framework in order to predict student graduation predicates. The growth in the number of students in higher education institutions has led to an increasingly large and diverse volume of academic data. The use of Hadoop MapReduce is expected to overcome the challenges of large-scale data processing and support the development of an efficient pass rate prediction system. This research method involves analyzing student academic datasets, which include information such as exam results, course grades, classes taken, and other academic attributes. This data represents the population of students enrolled in a particular study program at higher education institutions. Hadoop MapReduce is implemented to process data in parallel, using the Map and Reduce functions, as well as setting the appropriate configuration so that the system can handle large-scale data processing efficiently. This study aims to implement Hadoop MapReduce as a data processing framework in order to predict student graduation rates. The use of Hadoop MapReduce is expected to overcome the challenges of large-scale data processing and support the development of an efficient pass rate prediction system. The MapReduce Framework execution process was carried out very quickly, with the longest execution time of 2,375 milliseconds and the fastest 105 milliseconds, for processing student GPA data and the number of student credits, respectively. The prediction test results for 2015 graduates showed a difference of 23 students between the prediction results and reality, with a prediction error rate of 2.09%. Hadoop MapReduce is able to handle data processing very efficiently, even for relatively small data. In addition, the prediction of student graduation rates using the implemented system gives adequate results with a low error rate. Nevertheless, it is necessary to carry out further evaluation and improvement of the prediction model to improve prediction accuracy. Thus, Hadoop MapReduce remains the right choice for big data analysis and processing in various applications, including in the context of predicting student graduation rates.
Co-Authors Azaluddin Azaluddin Darmawan Darmawan La Ode Muhammad Saidi muhamad iksan Muhammad Hibrian Wiwi Rini Angelia Mahlil Samritin Samritin Wa Ode Alzarliani