Analisis gaya belajar VAK pada pembelajaran daring terhadap minat belajar siswa

Penelitian ini termasuk pada penelian deskriptif kualitatif. Tujuan dilakukan nya penelitian ini untuk menganalisis gaya belajar VAK yang dapat menimbulkan minat belajar siswa pada pembelajaran daring. Subjek penelitian yaitu siswa sebanyak 36 orang yang berasal melalui SMA di Kota Rantauprapat. Instrumen dalam kajian ini berupa angket/kuesioner, interviu, observasi dan dokumentasi. Hasil kajian yang didapat memperlihatkan bila gaya belajar sangat berpengaruh terhadap minat belajar siswa dengan jumlah presentase peserta didik yang mempunyai gaya belajar visual 64%, sedangkan auditori sebanyak 25% dan kinestik 11%. Dengan demikian, hasil kajian menjelaskan jika murid kelas XII yang mengikuti pembelajaran daring cenderung memiliki gaya belajar visual. Hal ini dilihat dari banyak nya siswa mengerjakan tugas dari guru ketika proses belajar mengajar mempergunakan gaya belajar visual. Kata kunci: Gaya Belajar, Pembelajaran Daring, Minat BelajarThis research is included in qualitative descriptive research. The purpose of this research is to analyze VAK learning styles that can generate student interest in learning online. Research subject were 36 people who come from one of the high schools in the city of rantauprapat. The instrument in this study was a questionnaire, interviews, observation and documentation. The results obtained indicate that the learning style is very influential on students interest in learning with the number of student presentations who have a visual learning style of 64%, while the auditory is 25% and 11% kinesthetic. So from the results of this study it can be concluded that class XII students who take online learning tend to have a visual learning style Keywords: Learning Styles, Online Learning, Learning Interests

Avoiding Machine Learning Becoming Pseudoscience in Biomedical Research

The use of machine learning harbours the promise of more accurate, unbiased future predictions than human beings on their own can ever be capable of. However, because existing data sets are always utilized, these calculations are extrapolations of the past and serve to reproduce prejudices embedded in the data. In turn, machine learning prediction result raises ethical and moral dilemmas. As mirrors of society, algorithms show the status quo, reinforce errors, and are subject to targeted influences – for good and the bad. This phenomenon makes machine learning viewed as pseudoscience. Besides the limitations, injustices, and oracle-like nature of these technologies, there are also questions about the nature of the opportunities and possibilities they offer. This article aims to discuss whether machine learning in biomedical research falls into pseudoscience based on Popper and Kuhn's perspective and four theories of truth using three study cases. The discussion result explains several conditions that must be fulfilled so that machine learning in biomedical does not fall into pseudoscienc