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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Pengabdian Masyarakat Multidisiplin
Yogi Yunefri
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Aerospace Engineering Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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PELATIHAN INSTALLASI DAN OPERASIONAL BLENDED LEARNING UNTUK ADMIN FAKULTAS Zamzami Zamzami; Yogi Yunefri; Didik Siswanto
Jurnal Pengabdian Masyarakat Multidisiplin Vol 1 No 3 (2018): Juni
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (331.287 KB) | DOI: 10.36341/jpm.v1i3.469

Abstract

The Learning process at Faculty of Education and Vocational Education (FKIP)) and the Faculty of Administrative Sciences (FIA) lecturers has been providing materials by displaying lecture materials using projector, as well as writing lecture materials on the blackboard. The student quiz is conducted after several meetings, the students are required to work on and complete the quiz given by the lecturer and then immediately collected after the time is up or finished. To give and collect the task, the lecturer give it during the lecture period and there are also lecturers who give the task at the end of the lecture after the lecturer delivered the lecture material, and the collection is done during the meeting in the class or on the next day schedule in the form of hardcopy so that the lecturer must certain to correct it. To face the problems faced by the Faculty of Education (FKIP) and Faculty of Administrative Sciences (FIA) the authors suggest with the use of electronic-based learning media that is Blended Learning.
Naive Bayes Optimization with PSO for Predicting ICU Needs for Covid-19 Patients Lusiana Dwi Lestari; Iqbal Harifal; Taslim Taslim; Yogi Yunefri; Susi Handayani; Eka Sabna; Kursiah Warti Ningsih
SISTEMASI Vol 11, No 3 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v11i3.2094

Abstract

Covid-19 is a global pandemic that requires a coordinated worldwide response across all national health and healthcare systems. Identifying patients who are at high risk of contracting the Covid-19 virus is important to increase awareness before patients are further infected by the Covid-19 virus which can cause severe respiratory illness that requires special treatment in intensive care units (ICU). This study aims to predict ICU needs in patients infected with the Covid-19 virus. The value results from the prediction of ICU needs are used as a reference for hospitals to meet ICU needs for patients infected with Covid-19 so that they can increase ICU supplies. The prediction will be carried out using the Naïve Bayes algorithm method with optimization using the PSO algorithm. Based on the results of the study, the population size 20 with an accuracy value of the NBC algorithm was 87.03%, population size 40 with an accuracy value of 87.28, population size 60 obtained an accuracy of 87.13%, population size 80 with an accuracy value of 87.16 % and population size 100, the results obtained are 87.26% so that each population has an increase in the accuracy value.
Co-Authors Didik Siswanto Iqbal Harifal Lusiana Dwi Lestari Ningsih, Kursiah Warti Sabna, Eka Susi Handayani Taslim Taslim Zamzami Zamzami