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This Author published in this journals
All Journal Journal of Vocational Education Studies Indonesian Journal of Electrical Engineering and Computer Science
Bahbibi Rahmatullah
Sultan Idris Education University
Education Engineering Social Sciences Other

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Online Learning for Vocational Education: Uncovering Emerging Themes on Perceptions and Experiences Amirah Rasyidah Roslin; Bahbibi Rahmatullah; Nor Zuhaidah Mohamed Zain; Sigit Purnama; Qahtan M. Yas
Journal of Vocational Education Studies Vol. 5 No. 1 (2022)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/joves.v5i1.6097

Abstract

The worldwide spread of the COVID19 pandemic has shifted the teaching of theory and practical sessions in all schools, including in the field of vocational education, to online learning. Theoretical and practical learning in vocational education become more flexible and is not confined to physical space. A systematic literature review of selected research articles was conducted using Systematic Reviews and Meta-Analyses (PRISMA) guidelines based on important keywords of online learning in vocational education. Initially, the research identified 89 articles from Scopus databases by using a specific keywords search. After the screening phase, ten articles were finalized to meet the criteria for review and discussion in this paper. The thematic analysis conducted reveals several exciting topics discussed in this paper; students' experiences, teaching and learning performances, and the teacher and students' perception of online learning for vocational education. The study implies the cruciality of understanding the role of teachers and students in vocational education so online learning can be optimized and conducted efficiently.
Machine learning approaches for predicting postpartum hemorrhage: a comprehensive systematic literature review Dewi Pusparani Sinambela; Bahbibi Rahmatullah; Noor Hidayah Che Lah; Ahmad Wiraputra Selamat
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp2087-2095

Abstract

Postpartum hemorrhage (PPH) represents a significant threat to maternal health, particularly in developing countries, where it remains a leading cause of maternal mortality. Unfortunately, only 60% of pregnant women at high risk for PPH are identified, leaving 40% undetected until they experience PPH. To address this critical issue and ensure timely intervention, leveraging rapidly advancing technology with machine learning (ML) methodologies for maternal health prediction is imperative. This review synthesizes findings from 43 selected research articles, highlighting the predominant ML techniques employed in PPH prediction. Among these, logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), and decision tree (DT) emerge as the most frequently utilized methods. By harnessing the power of ML, we aim to foster technological advancements in the healthcare sector, with a particular focus on maternal health and ultimately contribute to the reduction of maternal mortality rates worldwide.
Co-Authors Ahmad Wiraputra Selamat Amirah Rasyidah Roslin Dewi Pusparani Sinambela Noor Hidayah Che Lah Nor Zuhaidah Mohamed Zain Purnama, Sigit Qahtan M. Yas