Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
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All Journal INSECTA: Integrative Science Education and Teaching Activity Journal International Journal of Emerging Research and Review
Mila Candra Pristianti
Universitas Negeri Surabaya, Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Education Physics Social Sciences Other

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LITERATURE REVIEW ON THE USE OF PROBLEM BASED LEARNING MODELS IN IMPROVING PHYSICS LEARNING OUTCOMES Mila Candra Pristianti; Binar Kurnia Prahani
INSECTA: Integrative Science Education and Teaching Activity Journal Vol 4, No 1 (2023)
Publisher : Science Education, Institut Agama Islam Negeri Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21154/insecta.v4i1.6003

Abstract

The purpose of this study is to describe the use of PBL models to improve student learning outcomes. This research method uses library research. The data studied were obtained from Google Scholar and Scopus from 2018 to 2022. The scope of the data analyzed is physics learning with the PBL model and its relationship to enhancing learning outcomes. The descriptive qualitative analysis was used to analyze the data. The results of this study indicate that PBL can be effectively implemented in physics learning. This learning can increase student activity in class because it involves students directly participating in solving the problems given and can increase students' understanding of physics. With increasing student understanding, student learning outcomes also increase. This learning can be done by applying different technologies such as digital books, 3D, PhET, and augmented reality. From this study, the PBL models can be used to improve student learning outcomes in face-to-face, mixed, and online learning. Future research can be carried out by directly implementing the PBL model to determine its effect on student problem-solving skills and critical thinking.
Comparison of Top 100 Cited Research on Machine Learning and Deep Learning in The Last Twenty Years Yeni Anistyasari; Binar Kurnia Prahani; Mila Candra Pristianti; Tan Amelia; Paken Pandingangan; Rizki Fitri Rahima Uulaa; Mohammad Walid Rasuliy
International Journal of Emerging Research and Review Vol. 1 No. 1 (2023): March
Publisher : IKIP Widya Darma Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56707/ijoerar.v1i1.3

Abstract

Objective: The aim is to compare the top 100 research related to machine learning (ML) and deep learning (DL) during 2002-2021 using bibliometric analysis. Method: The data were obtained from Scopus. The data taken in this research were selected from 100 articles with the highest citation in the range from 2002 to 2021. Bibliometric analysis is used in this research. Data from Scopus is exported in the form of .csv form which is processed using Ms. Excel and form of .ris which is processed using VOSviewer. Results: The results showed that the trend of ML and DL increased every year. The most widely published document types in ML are articles, while DL is in the form of conference papers. The highest year-wise distribution of publishing ML and DL occurred in 2017. ML advantages are in terms of requires less data and take less time to train, while the DL advantages in terms of higher accuracy than ML, available to tuned in various different ways. Novelty: This research being able to provide an overview for future researchers regarding the trend of ML and DL topics so that the resulting paper can provide various benefits for the coming year. In addition, it can provide broader knowledge about ML and DL itself. Further research can be carried out more intensively using data based on the Web of Science, in addition to the Scopus database.
Co-Authors Binar Kurnia Prahani Mohammad Walid Rasuliy Paken Pandingangan Rizki Fitri Rahima Uulaa Tan Amelia Yeni Anistyasari